Digital media defines modern childhood, but its cognitive effects are unclear and hotly debated. We believe that studies with genetic data could clarify causal claims and correct for the typically unaccounted role of genetic predispositions.
Here, we estimated the impact of different types of screen time (watching, socializing, or gaming) on children’s intelligence while controlling [using a polygenic score predicting 7% IQvariance] for the confounding effects of genetic differences in cognition and socioeconomic status. We analyzed 9,855 children from the USA who were part of the ABCD dataset with measures of intelligence at baseline (ages 9–10) and after 2 years.
At baseline, time watching (r = −0.12) and socializing (r = −0.10) were negatively correlated with intelligence, while gaming did not correlate. After 2 years, gaming positively impacted intelligence (standardized β = +0.17), but socializing had no effect. This is consistent with cognitive benefits documented in experimental studies on video gaming. Unexpectedly, watching videos also benefited intelligence (standardized β = +0.12), contrary to prior research on the effect of watching TV. Although, in a post hoc analysis, this was not statistically-significant if parental education (instead of SES) was controlled for.
Broadly, our results are in line with research on the malleability of cognitive abilities from environmental factors, such as cognitive training and the Flynn effect.
[Obviously wrong. Cognitive training doesn’t work in randomized experiments, the Lee et al 2018 PGS explains less than a fifth of genetics and doesn’t ‘control’ for much at all, and their correlates are probably just residual confounding.]
Brain size and IQ are positively correlated. However, multiple meta-analyses have led to considerable differences in summary effect estimations, thus failing to provide a plausible effect estimate.
Here we aim at resolving this issue by providing the largest meta-analysis and systematic review so far of the brain volume and IQ association (86 studies; 454 effect sizes from k = 194 independent samples; n > 26 000) in 3 cognitive ability domains (full-scale, verbal, performance IQ).
By means of competing meta-analytical approaches as well as combinatorial and specification curve analyses, we show that most reasonable estimates for the brain size and IQ link yield r-values in the mid-0.20s, with the most extreme specifications yielding rs of 0.10 and 0.37.
Summary effects appeared to be somewhat inflated due to selective reporting, and cross-temporally decreasing effect sizes indicated a confounding decline effect, with 3⁄4th of the summary effect estimations according to any reasonable specification not exceeding r = 0.26, thus contrasting effect sizes were observed in some prior related, but individual, meta-analytical specifications. Brain size and IQ associations yielded r = 0.24, with the strongest effects observed for more g-loaded tests and in healthy samples that generalize across participant sex and age bands.
[Keywords: multiverse analysis, in vivo brain volume, systematic review, intelligence, specification curve analysis, meta-analysis]
Personality and cognition are heritable mental traits, and their genetic determinants may be distributed across interconnected brain functions. However, previous studies have employed univariate approaches which reduce complex traits to summary measures.
We applied the “pleiotropy-informed” multivariate omnibus statistical test (MOSTest) to genome-wide association studies (GWAS) of 35 item and task-level measures of neuroticism and cognition from the UK Biobank (n = 336,993). We identified 431 significant genetic loci and found evidence of abundant pleiotropy across personality and cognitive domains. Functional characterisation implicated genes with significant tissue-specific expression in all tested brain tissues and enriched in brain-specific gene-sets.
We conditioned independent GWAS of the Big 5 personality traits and cognition on our multivariate findings, which boosted genetic discovery in other personality traits and improved polygenic prediction. These findings advance our understanding of the polygenic architecture of complex mental traits, indicating a prominence of pleiotropic genetic effects across higher-order domains of mental function.
A core part of political research is to identify how political preferences are shaped. The nature of these questions is such that robust causal identification is often difficult to achieve, and we are not seldom stuck with observational methods that we know have limited causal validity.
The purpose of this paper is to measure the magnitude of bias stemming from both measurable and unmeasurable confounders across 3 broad domains of individual determinants of political preferences: socio-economic factors [education, income, wealth], moral values [social trust, altruism & antisocial attitudes, utilitarian judgement], and psychological constructs [risk preferences, Extraversion, locus of control, IQ]. We leverage an unique combination of rich Swedish population registry data for a large sample of identical twins, with a comprehensive battery of 34 political preference measures, and build a meta-analytical model comparing our most conservative observational (naive) estimates with discordant twin estimates. This allows us to infer the amount of bias from unobserved genetic and shared environmental factors that remains in the naive models for our predictors, while avoiding precision issues common in family-based designs.
The results are sobering: in most cases, substantial bias remains in naive models. A rough heuristic is that about half of the effect size even in conservative observational estimates is composed of confounding.
…The results are sobering: for a large set of important determinants, a substantial bias seems to remain even in conservative naive models. In a majority of cases, half or more of the naive effect size appears to be composed of confounding, and in 0 cases are the naive effect sizesunderestimated. The implications of this are important. First, it provides a reasonable bound on effect estimates stemming from observational methods without similar adjustments for unobserved confounders. While the degree of bias will vary depending on both predictors and outcomes, a rough but useful heuristic derived from the results of this paper is that effect sizes are often about half as big as they appear. Second, future research will have to consider more carefully the confounding effects of genetic factors and elements of the rearing environment that are not easily captured and controlled for.
Method: The method employed follows 3 steps for each predictor separately. First, 3 regression models (empty, naive, and within, as outlined below) are run for each political preference outcome in the sample of complete twin pairs. Second, a meta-analytical average for all outcomes, per model, is calculated. Third, this average effect size is compared across models to see how it changes with specification…The precision problem is at least partially solved by the aggregation of many [34] outcomes: while we should expect standard errors to be higher in the discordant models, the coefficients should not change in any systematic direction if the naive effect sizes are unbiased. Systematic changes in the average effect size across the different preference items is therefore a consequence of model choice (and, we argue, a reduction in bias) rather than variance artefacts.
Models: Naive: The second model (the “naive” model, n), and hence the first model comparison, adds a comprehensive set of controls available in the register data. The ambition is to produce as robust a model as possible with conventional statistical controls. The controls include possible contextual (municipal fixed effects), familial (parental birth years, income, and education) and individual (occupational codes, income, and education) confounders. In total, this should produce a model that is fairly conservative…Within: Finally, the third model (the “within” model, w) adds twin-pair fixed effects, producing a discordant twin design. This controls for all unobserved variables shared within an identical twin pair, that is, genetic factors, upbringing and home environment, as well as possible neighborhood and network effects
Figure 2: Main results, all outcomes, ‘naive’ versus ‘within’. Average beta coefficients across all outcomes, per model and predictor. 90% confidence intervals shown.
Most research on individual differences in performance on tests of cognitive ability focuses on general cognitive ability (g), the highest level in the three-level Cattell-Horn-Carroll (CHC) hierarchical model of intelligence. About 50% of the variance of g is due to inherited DNA differences (heritability) which increases across development. Much less is known about the genetics of the middle level of the CHC model, which includes 16 broad factors such as fluid reasoning, processing speed, and quantitative knowledge.
We provide a meta-analytic review of 863,041 monozygotic-dizygotic twin comparisons from 80 publications for these middle-level factors, which we refer to as specific cognitive abilities (SCA). Twin comparisons were available for 11 of the 16 CHC domains. The average heritability across all SCA is 55%, similar to the heritability of g. However, there is substantial differential heritability and the SCA do not show the dramatic developmental increase in heritability seen for g.
We also investigated SCA independent of g (g-corrected SCA, which we refer to as SCA.g). A surprising finding is that SCA.g remain substantially heritable (53% on average), even though 25% of the variance of SCA that covaries with g has been removed.
Our review frames expectations for genomic research that will use polygenic scores to predict SCA and SCA.g. Genome-wide association studies of SCA.g are needed to create polygenic scores that can predict SCA profiles of cognitive abilities and disabilities independent of g. These could be used to foster children’’s cognitive strengths and minimize their weaknesses.
Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for the effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated.
We used functional magnetic resonance imaging data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and 7 different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system and replicates in 2 independent samples (n = 138 and n = 184).
Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multitask brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold—the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multitask brain network.
…Here, we use fMRI data from a large sample of healthy adults (n = 812) assessed during different cognitive states, that is, during resting state and during 7 different task states, to test the hypothesis that higher levels of general intelligence relate to less brain network reconfiguration.
Specifically, we expected this association to manifest in reaction to different cognitive demands and on various spatial scales. We used a straight-forward operationalization of brain network reconfiguration and implemented our analyses on a whole-brain level as well as on the level of 7 and 17 canonical functional brain networks.
The results confirm our hypotheses and suggest that functional brain networks of more intelligent people may require less adaption when switching between different cognitive states, thus pointing toward the existence of an advantageous intrinsic brain network architecture.
Furthermore, we show that although the different cognitive states were induced by different demanding tasks, their relative contribution to the observed effect was nearly identical; a finding that supports the assumption of a task-general neural correlate—a neural-positive manifold.
Finally, the involvement of multiple brain networks suggests intelligence as an emergent property of a widely distributed multitask brain network.
Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants1. While genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known2.
Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily due to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is likely mediated by genetically associated cognitive and behavioural traits, which may mean male carriers are less likely to find reproductive partners.
This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. While this genetic association could only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.
Theories of adult cognitive development classically distinguish between fluid abilities, which require effortful processing at the time of assessment, and crystallized abilities, which require the retrieval and application of knowledge. On average, fluid abilities decline throughout adulthood, whereas crystallized abilities show gains into old age. These diverging age trends, along with marked individual differences in rates of change, have led to the proposition that individuals might compensate for fluid declines with crystallized gains.
Here, using data from 2 large longitudinal studies, we show that rates of change are strongly correlated across fluid and crystallized abilities. Hence, individuals showing greater losses in fluid abilities tend to show smaller gains, or even losses, in crystallized abilities.
This observed commonality between fluid and crystallized changes places constraints on theories of compensation and directs attention toward domain-general drivers of adult cognitive decline and maintenance.
Altering the speed of a test may alter the cognitive processes measured.
Speed/length of Raven’s Progressive Matrices influence construct validity.
Estimated validity of an abbreviated ability tests requires addressing reliability
Researchers should consider speed and length when selecting or modifying a test.
Historically, there has been substantial disagreement about the importance of speed vs. level in determining individual differences in intelligence—a disagreement that persists across various different modern assessment measures of intellectual abilities.
The current investigation considers whether changes to the administration constraints (time limitations or speededness, and total test length) of the Raven’s Advanced Progressive Matrices test—which has been identified as a measure highly saturated with general intelligence—results in differences to the underlying ability determinants of test performance.
A review of empirical studies was conducted, where versions of Raven’s Advanced Progressive Matrices Tests were administered under various time constraints and item lengths. Meta-analytic techniques were used to determine whether introducing speed constraints or shortening the length of the test changes the construct validity of the tests (as indicated by differences in convergent and discriminant correlations with other ability traits).
The meta-analysis combined results from 142 studies composed of a total of 26,848 participants. Substantial differences were found for correlations of Raven’s Advanced Progressive Matrices and Spatial Visualization (as large as p̂ = 0.26), Memory (as large as p̂ = 0.08), and Perceptual Speed (as large as p̂ = 0.34) abilities under speeded conditions and shorter test lengths.
Examinees may draw on different strategies for test performance, that in turn, draw on different combinations of abilities, when the test is abbreviated or substantial time constraints are introduced. Implications for using this test under different conditions are discussed.
[Keywords: speed, level, Raven’s progressive matrices, short-form tests, test modification]
Cognitive variation is common among-individuals and can be consistent across time and context. From an evolutionary perspective, among-individual variation is important as a pre-requisite for natural selection and adaptive evolution. Selection is widely hypothesized to favor high cognitive performance but directional selection should erode variation over time, how then is cognitive variation maintained? As selection does not act on traits in isolation, covariance among specific cognitive traits and/or other aspects of phenotype (eg. personality) could result in fitness trade-offs that are important in shaping evolutionary dynamics. Here we test this using Trinidadian guppies (Poecilia reticulata), using a multivariate approach by characterizing the correlation structure among task-specific cognitive performance measures and a personality trait. We estimate the among-individual correlation matrix (ID) in performance across three cognitive tasks; colour association learning task; motor learning task; reversal learning task, and the personality trait, boldness, measured as emergence time from a shelter. We found no support for trade-offs among performance in these tasks. Nor do we find evidence of hypothesised speed-accuracy trade-offs within the association learning task. Rather we find strong positive correlation structure in ID, with 57% of variation explained by the leading eigen vector. While noting that non-cognitive factors and assay composition may affect the structure of ID, we suggest our findings are consistent with the g-model of cognitive performance variation, in which a dominant axis of variation loads positively on all performance measures. Thus, we add to a growing body of support for general variation among individuals in animal cognitive ability.
Background: Cannabis is among the most consumed psychoactive substances world-wide. Considering changing policy trends regarding the substance, it is crucial to understand more clearly its potential acute and residual adverse effects from a public health viewpoint. Cognitive function is one of the targeted areas with conflicting findings. This meta-review measured the magnitude of acute and residual effects of cannabis on cognition in adolescents and adults provided by meta-analyses and evaluated quality of evidence.
Methods: A systematic search was performed in PubMed, PsycINFO, Web of Science and Google Scholar. Meta-analyses were included if they quantitatively examined the performances of users from the general population on cognitive tasks.
Results: The search retrieved 10 eligible meta-analyses (71 effects sizes, n = 43 761) with evidence ranging from low to moderate quality, which were categorized into domains of cognitive functions: executive functions (k = 7), learning and memory (k = 5), attention (k = 4), processing speed (k = 5), perceptual motor function (k = 2) and language (k = 2).
Verbal learning and memory displayed the most robust evidence and were most impaired by acute cannabis intoxication that persisted after intoxication passed. Small-to-moderate acute and residual adverse effects were reported for executive functioning. Cannabis use led to small deficits in inhibitory processes and flexibility, whereas small-to-moderate deficits were reported for working memory and decision-making. Evidence regarding processing speed and attention has shown that cannabis administration induced small-to-moderate adverse effects and residual neurocognitive deficits were observed in heavy cannabis-using youths. Results showed no statistically-significant difference between cannabis users and non-users on language, and small-to-moderate effects for simple motor skills.
Conclusion: Meta-analytical data on the acute effects of cannabis use on neurocognitive function have shown that cannabis intoxication leads to small to moderate deficits in several cognitive domains. These acute impairments accord with documented residual effects, suggesting that the detrimental effects of cannabis persist beyond acute intake.
…Despite the findings provided in this meta-review, several elements need to be discussed when interpreting results. First and foremost, the meta-analyses discussed comprised cross-sectional data with several analyses having relatively small sample sizes, which limits the inference of a causal relationship between cannabis use and cognition as well as the generalizability of results…Although most of the evidence on the cognitive sequelae of cannabis use has been provided by cross-sectional data associated with methodological limitations, a growing number of longitudinal studies, which are useful to address causal inferences, have emerged. This has led to several reviews examining, among others, evidence provided by prospective designed studies[20, 27, 31, 81]. For instance, Bourque et al 27 noted similar findings to those observed in cross-sectional data. Indeed, most studies showed declines in both executive functioning and verbal learning/memory[82–95], while results were less consistent for processing speed[82, 85, 88, 90, 94–96]. Furthermore, longitudinal data have similarly shed light on the hypotheses that have been put forth to explain the association between cannabis use and cognitive functions (see Bourque et al 27 for an overview). A first hypothesis, that has received mixed evidence, specifies that cannabis use leads to persistent cognitive impairments. These neurotoxic effects last although cannabis users reduce their intake or quit altogether. While some longitudinal studies suggest that cognitive deficits resolve following abstinence[92, 94], other studies have confirmed that cannabis use frequency led to subsequent long-term cognitive decline (ie. executive function) regardless of prolonged cannabis intake, while adjusting for covariates[84, 87, 97]. Following, the premorbid cognitive vulnerability hypothesis proposes that individuals at increased risk of using the substance more regularly already presented cognitive deficits before cannabis use onset. Several studies have shown that specific cognitive impairments (ie. memory and executive functions) seemed to incline individuals to earlier onset of use in addition to more frequent use in comparison to non-using individuals[83–86, 98]. However, such findings were not evident in all studies[82, 87, 97, 99, 100] and some studies more probably support the common antecedent hypothesis[86, 98], which postulates that common factors (eg. externalizing behaviour) may predispose individuals to both cannabis use and cognitive deficits in users. Hence, results from longitudinal co-twin studies have suggested that cannabis use may not necessarily cause neurocognitive decline, but rather that factors related to family background, such as genetic and shared environmental factors, may more clearly explain worse cognitive performances amid cannabis users[86, 98].
Cognitive reflection correlates with all cognitive abilities and numeracy skills
Cognitive reflection is accounted for by a general factor of intelligence plus a second-stratum factor of numerical ability
Intelligence and numerical ability have direct and indirect effects on cognitive reflection through numeracy skills
This paper presents a series of psychometric meta-analysis on the relationship between cognitive reflection (CR) and several cognitive abilities (ie. cognitive intelligence, numerical ability, verbal ability, mechanical-spatial ability, and working memory), and skills (ie. numeracy skills). Also, the paper presents a bifactor analysis carried out to determine whether CR is a related but independent factor or a second-stratum factor in a hierarchical model of cognitive intelligence. Finally, the study also tested a path meta-analytic model of the CR-cognitive ability relationships.
The results showed that CR correlated substantially with all the cognitive abilities and skills (k ranged 3–44 and n ranged 624–20,307). The bifactor analysis showed that CR variance was mainly accounted for by a general factor of cognitive intelligence plus a second-stratum factor of numerical ability. The results of the bifactor analysis were similar for numerical-CRT and verbal-CRT. It was not found evidence supporting the existence of a cognitive reflection factor. Finally, the path meta-analytic model showed that the combination of cognitive intelligence and numerical ability accounted for 69% of CR variance. The path model showed that cognitive intelligence and numerical ability have direct and indirect (through numeracy skills) effects on CR.
Theoretical and practical implications are discussed, and future research is suggested.
Psychometric studies have identified a broad domain of human spatial abilities
Spatial abilities are also found across non-human species
Evolutionary pressures that enhance spatial abilities are discussed
Evolutionary perspective supports psychometric studies of human spatial abilities
Psychometric studies have consistently identified spatial abilities as a broad domain of human cognition. Spatial abilities are in fact found in species in which engagement with the physical world, as in prey capture or mate searches, influences survival or reproductive prospects and much is now known about the brain and cognitive systems that support these activities.
Sex differences in spatial abilities are found in species in which one sex or the other engages the physical world in more complex ways, such as having a larger home range. Sex differences provide a unique opportunity to study the influence of evolutionary pressures on cognition, because the study of males and females from the same species controls for many aspects of evolutionary history. When there are differences in past selection pressures on males and females they are typically related to reproductive demands.
The approach is illustrated here for spatial abilities and provides a blueprint for linking psychometric and evolutionary approaches to the study of human spatial and other abilities.
[Keywords: intelligence, spatial abilities, evolution, sex differences, cognitive abilities]
This paper systematically revisits prior meta-analytic conclusions about the criterion-related validity of personnel selection procedures, and particularly the effect of range restriction corrections on those validity estimates. Corrections for range restriction in meta-analyses of predictor-criterion relationships in personnel selection contexts typically involve the use of an artifact distribution.
After outlining and critiquing 5 approaches that have commonly been used to create and apply range restriction artifact distributions, we conclude that each has large issues that often result in substantial over-correction and that therefore the validity of many selection procedures for predicting job performance has been substantially overestimated.
Revisiting prior meta-analytic conclusions produces revised validity estimates. Key findings are that most of the same selection procedures that ranked high in prior summaries remain high in rank, but with mean validity estimates reduced by 0.10–0.20 points. Structured interviews emerged as the top-ranked selection procedure. We also pair validity estimates with information about mean Black-White subgroup differences per selection procedure, providing information about validity-diversity tradeoffs.
We conclude that our selection procedures remain useful, but selection predictor-criterion relationships are considerably lower than previously thought.
[Keywords: selection procedures, validity, meta-analysis, range restriction, artifact distribution]
…Before reviewing approaches to generating artifact distributions, there is a critical observation we need to make and elaborate, namely, that meta-analyses of selection procedure validity to date have assumed that the artifact distribution applies to all studies used in the meta-analysis. In the context of analyzing intercorrelations among predictors (as opposed to selection method validation, which focuses on predictor-criterion relationships), Sackett et al 2007 and Berry et al 2007 noted that the application of the same correction factor (or artifact distribution correction factor) to all studies can be seriously misguided. Berry et al 2007 focused on the relationship between cognitive ability and employment interviews. Some studies administered the 2 measures to all applicants; in this setting there was no range restriction whatsoever. Others screened initially on ability, and only interviewed a subset; in this case there was direct restriction on ability and indirect restriction on the interview. Others administered both predictors to current employees; in this case there was indirect restriction if the selection method used to select current employees was correlated with the interview, with ability, or with both. Berry et al 2007 detailed additional scenarios beyond these 3, but for our purposes the point is simply that applying an uniform correction across all studies makes no sense. Berry et al 2007 separated the available research studies into subsets based on information about range restriction mechanisms in each subset, and applied appropriate corrections within each subset. Conceptually, one could apply appropriate corrections to subsets, and combine the subsets for an estimate of the parameter of interest (eg. mean operational validity).
This review discusses evidence across a number of popular brief interventions designed to enhance cognitive abilities and suggests that these interventions often fail to elicit reliable improvements. Consequences of exaggerated claims are discussed, together with a call for constructive criticism when evaluating this body of research.
A number of popular research areas suggest that cognitive performance can be manipulated via relatively brief interventions. These findings have generated a lot of traction, given their inherent appeal to individuals and society. However, recent evidence indicates that cognitive abilities might not be as malleable as preliminary findings implied and that other more stable factors play an important role.
In this article, I provide a critical outlook on these trends of research, combining findings that have mainly remained segregated despite shared characteristics.
Specifically, I suggest that the purported cognitive improvements elicited by many interventions are not reliable, and that their ecological validity remains limited.
I conclude with a call for constructive skepticism when evaluating claims of generalized cognitive improvements following brief interventions.
Background: Intellectual disability (ID) aggregates in families, but factors affecting individual risk and heritability estimates remain unknown.
Methods: A population-based family cohort study of 4,165,785 individuals born 1973–2013 in Sweden, including 37,787 ID individuals and their relatives. The relative risks (RR) of ID with 95% confidence intervals (95% CI) were obtained from stratified Cox proportional-hazards models. Relatives of ID individuals were compared to relatives of unaffected individuals. Structural equation modeling was used to estimate heritability.
Results: Relatives of ID individuals were at increased risk of ID compared to individuals with unaffected relatives. The RR of ID among relatives increased proportionally to the degree of genetic relatedness with ID probands; 256.70 (95% CI 161.30–408.53) for monozygotic twins, 16.47 (13.32–20.38) for parents, 14.88(12.19–18.16) for children, 7.04 (4.67–10.61) for dizygotic twins, 8.38 (7.97–8.83) for full siblings, 4.56 (4.02–5.16) for maternal, 2.90 (2.49–3.37) for paternal half-siblings, 3.03 (2.61–3.50) for nephews/nieces, 2.84 (2.45–3.29) for uncles/aunts, and 2.04 (1.91–2.20) for cousins. Lower RRs were observed for siblings of probands with chromosomal abnormalities (RR 5.53, 4.74–6.46) and more severe ID (mild RR 9.15, 8.55–9.78, moderate RR 8.13, 7.28–9.08, severe RR 6.80, 5.74–8.07, and profound RR 5.88, 4.52–7.65). Male sex of relative and maternal line of relationship with proband was related to higher risk (RR 1.33, 1.25–1.41 for brothers vs. sisters and RR 1.49, 1.34–1.68 for maternal vs. paternal half-siblings). ID was substantially heritable with 0.95 (95% CI 0.93–0.98) of the variance in liability attributed to genetic influences.
Conclusions: The risk estimates will benefit researchers, clinicians, families in understanding the risk of ID in the family and the whole population. The higher risk of ID related to male sex and maternal linage will be of value for planning and interpreting etiological studies in ID.
Understanding the relationships between cognitive abilities and fitness is integral to an evolutionary study of brain and behavior. However, these relationships are often difficult to measure and detect.
Here we draw upon an opportunistic sample of brown-headed cowbird (Molothrus ater) subjects that had 2 separate research experiences: First, they engaged in a large series of cognitive tests in David Sherry’s Lab in the Advanced Facility for Avian Research (AFAR) at Western University, then subsequently moved to the Field Avian Research Megalab (FARM) at Wilfrid Laurier University where they lived in large breeding flocks in aviaries with other wild-caught cowbirds. Thus, we had extensive measures of cognitive abilities, breeding behavior, and reproductive success for these birds.
We report here, for the fist time, the surprisingly strong connections we found among these different measures. Female cowbirds’ spatial cognitive abilities correlated positively with how intensely they were courted by males, and with their overall egg production. Males’ spatial cognition correlated positively with their ability to engage in singing contests (“countersinging”) with other males. In addition, a separate non-spatial cognitive ability correlated positively with the attractiveness of the songs they sung.
In sum, these results suggest the cognitive skills assessed in the lab were strongly connected to breeding behavior and reproductive success. Moreover, since certain cognitive abilities related to different aspects of breeding success, it suggests that cognitive modules may have specialized adaptive value, but also that these specialized skills may interact and influence fitness in surprising ways.
Background: Although the ICD and DSM differentiate between different psychiatric disorders, these often share symptoms, risk factors, and treatments. This was a population-based, case-control, sibling study examining familial clustering of all psychiatric disorders and low IQ, using data from the Israel Draft-Board Registry on all Jewish adolescents assessed between 1998 and 2014.
Methods: We identified all cases with autism spectrum disorder (ASD, n = 2128), severe intellectual disability (ID, n = 9572), attention-deficit hyperactive disorder (ADHD) (n = 3272), psychotic (n = 7902), mood (n = 9704), anxiety (n = 10 606), personality (n = 24 816), or substance/alcohol abuse (n = 791) disorders, and low IQ (⩾2 SDs below the population mean, n = 31 186). Non-CNS control disorders were adolescents with Type-1 diabetes (n = 2427), hernia (n = 29 558) or hematological malignancies (n = 931). Each case was matched with 10 age-matched controls selected at random from the Draft-Board Registry, with replacement, and for each case and matched controls, we ascertained all full siblings. The main outcome measure was the relative recurrence risk (RRR) of the sibling of a case having the same (within-disorder RRR) or a different (across-disorder RRR) disorder.
Results: Within-disorder RRRs were increased for all diagnostic categories, ranging from 11.53 [95% confidence interval (CI): 9.23–14.40] for ASD to 2.93 (95% CI: 2.80–3.07) for personality disorders. The median across-disorder RRR between any pair of psychiatric disorders was 2.16 (95% CI: 1.45–2.43); the median RRR between low IQ and any psychiatric disorder was 1.37 (95% CI: 0.93–1.98). There was no consistent increase in across-disorder RRRs between the non-CNS disorders and psychiatric disorders and/or low IQ.
Conclusion: These large population-based study findings suggest shared etiologies among most psychiatric disorders, and low IQ.
Data from natural settings and laboratories imply Panworking memory (WM) is 2 ± 1
WM increases until puberty but puberty occurs at half the age for Pan as for humans
Claims for extraordinary working memory in Pan are not supported by data
WM increase during hominin evolution parallels complexity increase in stone artifacts
Cumulative WM changes in Homo sapiens evolution led to qualitative cognitive changes
In this article we review publications relevant to addressing widely reported claims in both the academic and popular press that chimpanzees working memory (WM) is comparable to, if not exceeding, that of humans. WM is a complex multidimensional construct with strong parallels in humans to prefrontal cortex and cognitive development. These parallels occur in chimpanzees, but to a lesser degree.
We review empirical evidence and conclude that the size of WM in chimpanzees is 2 ± 1 versus Miller’s famous 7 ± 2 in humans. Comparable differences occur in experiments on chimpanzees relating to strategic and attentional WM subsystems. Regardless of the domain, chimpanzee WM performance is comparable to that of humans around the age of 4 or 5.
Next, we review evidence showing parallels among the evolution of WM capacity in hominins ancestral to Homo sapiens, the phylogenetic evolution of hominins leading to Homo sapiens, and evolution in the complexity of stone tool technology over this time period.
[Keywords: working memory, human evolution, cognitive evolution, comparative psychology, chimpanzee, hominin evolution, theory of mind, planning]
The strong correlation between education and voting is among the most robust findings in social science. We show that genes associated with the propensity to acquire education are also associated with higher voter turnout. A within-family analysis suggests education-linked genes exert direct effects on voter turnout but also reveals evidence of genetic nurture in second-order elections. Our findings have important implications for the study of political inequality. Scholars have argued that parental education is the main driver of the reproduction of political inequality across generations. By separating the effect of genes from parental nurturing, our findings suggest that the roots of individual-level political inequality run deeper than family background.
Twin and adoption studies have shown that individual differences in political participation can be explained, in part, by genetic variation. However, these research designs cannot identify which genes are related to voting or the pathways through which they exert influence, and their conclusions rely on possibly restrictive assumptions.
In this study, we use 3 different US samples and a Swedish sample to test whether genes that have been identified as associated with educational attainment, one of the strongest correlates of political participation, predict self-reported and validated voter turnout.
We find that a polygenic score capturing individuals’ genetic propensity to acquire education is statistically-significantly related to turnout. The strongest associations we observe are in second-order midterm elections in the United States and European Parliament elections in Sweden, which tend to be viewed as less important by voters, parties, and the media and thus present a more information-poor electoral environment for citizens to navigate. A within-family analysis [n = 10,000 sibling pairs] suggests that individuals’ education-linked genes directly affect their voting behavior..after controlling for the EDU PGS, the effect of education shrinks by 8%–17%, signaling that genes associated with education partially confound the relationship between education and turnout…but, for second-order elections, it also reveals evidence of genetic nurture. Finally, a mediation analysis suggests that educational attainment and cognitive ability combine to account for between 41% and 63% of the relationship between the genetic propensity to acquire education and voter turnout.
…Figure 1 illustrates that the polygenic score’s explanatory power is on par with that of personal income, parental income, and parental education and accounts for about half as much variation as years of education…Another possible mediator is personality, given that the EA PGS is correlated with personality traits(34, 35), a growing literature has demonstrated personality traits to be important for turnout(36), and personality and turnout have been shown to be influenced by shared genetic factors(9, 10).
Belief in astrology is on the rise, although the reasons behind this are unclear. We tested whether individual personality traits could predict such epistemically unfounded beliefs.
Data was collected for 264 participants through an anonymous online survey shared on social media. The survey consisted of 4 instruments: Belief in Astrology (BAI), the Big Five personality traits (IPIP-30), narcissism (SD3 [Short Dark Triad]), and intelligence (ICAR16-R3D). Data analysis was done with multiple linear regression.
Narcissism was surprisingly the strongest predictor, and intelligence showed a negative relationship with belief in astrology.
Overall, our novel results suggest that something as innocent as astrology could both attract and possibly reinforce individual differences.
[Keywords: belief in astrology, pseudoscience, Big Five, narcissism, intelligence]
Duckworth & Seligman 2005’s seminal work found that self-discipline (self-control) was more salient for academic achievement than intelligence. Very little replication work exists, including in different cultures; the current study addressed these gaps.
Data were collected from 6th and 7th grade cohorts of early adolescents [Brno Longitudinal Study of Youth (BLSY), an accelerated longitudinal study of 6th and 7th grade Czech adolescents] (n = 589; age: Mean = 12.34 years, and SD = 0.89; 58% female) over 2 years. The study tested whether self-control was a stronger predictor than intelligence in explaining academic performance 2 years later as well as in explaining developmental changes over the course of 2 years.
Path analyses provided evidence that both self-control and intelligence longitudinally predicted teacher-reported academic competence as well as school-reported grades; however, intelligence was a substantially stronger predictor than self-control. In addition, only intelligence predicted developmental changes in each measure of academic performance over time, self-control did not.
This study investigated the systematic rise in cognitive ability scores over generations, known as the Flynn Effect, across middle childhood and early adolescence (7–15 years; 291 monozygotic pairs, 298 dizygotic pairs; 89% White).
Leveraging the unique structure of the Louisville Twin Study (longitudinal data collected continuously from 1957 to 1999 using the Wechsler Intelligence Scale for Children [WISC], WISC-R, and WISC-III editions), multilevel analyses revealed between-subjects Flynn Effects—as both decrease in mean scores upon test re-standardization and increase in mean scores across cohorts—as well as within-child Flynn Effects on cognitive growth across age. Overall gains equaled ~3 IQ points per decade.
Novel genetically informed analyses suggested that individual sensitivity to the Flynn Effect was moderated by an interplay of genetic and environmental factors.
…Within-level and between-level FEs: The FE has usually been documented as a between-subjects phenomenon, either as mean increases in cognitive ability scores over generations or mean decreases between test versions. There is reason to suspect that environmental influences associated with between-subjects FEs also influence cognitive ability at the individual level, boosting intellectual growth across development above and beyond typical age-related gains. Dickens & Flynn 2001, for example, proposed that environmental enrichment makes it easier for children to select into environments that match their cognitive ability. Cognitively beneficial environments, in turn, boost cognitive growth, which facilitates further self-selection into more positive environments, in turn boosting cognitive ability, and so on, creating a reciprocal cycle that makes individual children exhibit greater intellectual growth across development. The individual gains brought about by individual multipliers, as Dickens & Flynn 2001 called them, can be thought of as within-person FEs. If enough children in a given population show such within-person FEs, the group mean will also rise, eventually resulting in a between-subjects FE across cohorts. A reciprocal process of social multipliers, which is the between-subjects analog of the individual multipliers process, can compound mean cognitive ability gains over time.
Although the possible connection between within-level and between-level FEs has been discussed for 2 decades, few studies have examined this empirically. Effective investigation of within-person FEs requires longitudinal data to model individual cognitive growth across age and isolate within-person FEs from age-related gains, as well as multilevel data across cohorts to distinguish within-level FEs from between-level FEs. Datasets meeting those criteria are rare and, perhaps because of this rarity, nearly all previous FE studies have performed solely crosssectional, between-subjects analyses. Only 2 previous reports have investigated within-person FEs using a multilevel approach. In 2 distinct multilevel analyses of math scores collected longitudinally across childhood between 1986 and 2012, O’Keefe & Rodgers 2017 observed statistically-significant within-person FEs, along with between-subjects gains. Later, O’Keefe & Rodgers 2020 performed a followup analysis of the same data. Results highlighted the utility of examining within-person FEs using longitudinal, multilevel approaches, to arrive at a more nuanced understanding of the FE.
…Current study: In this study, we examined the FE in data from the Louisville Twin Study (LTS), an intensive longitudinal study of cognitive development (Rhea 2015; Wilson 1983). Analyses focused on middle childhood and early adolescence (ages 7–15 years). Several features of the LTS data make them particularly well-suited for FE analyses and for addressing the gaps in the literature described above. First, initial data were collected continuously from 1957 to 1999, making it possible to test for rising IQ scores across generational cohorts of U.S. boys and girls over a long time span. Second, 3 versions of the WISC were administered over the course of the study (WISC, WISC-R, and WISC, 3rd ed. [WISC-III]). This allowed us to examine whether test restandardization resulted in systematic drops in mean IQ scores. Third, children were followed longitudinally, with some taking multiple versions of the WISC over the course of their participation. This enabled us to test for FEs not only between subjects (ie. rises in mean scores), but also within children (ie. rate of within-person cognitive growth), all while taking advantage of the statistical benefits offered by longitudinal models (eg. distinguishing age effects from cohort effects, increased power). Finally, because the LTS is a twin study, we were able to partition the variance of the within-person FE into genetic and environmental components (also referred to as biometric components) and examine the relative influence of genetic and environmental factors on individual sensitivity to the FE. In doing so, we performed the first-ever genetically informed analyses of the FE.
Thus, the unique structure of the LTS data enabled us to examine the FE as both cohort effects and test version effects simultaneously in a single sample. This data structure also made it possible to analyze FEs both within children and between children, and to examine the relative influence of genetic and environmental factors on within-person FEs. By modeling all of these elements, we were able to isolate specific aspects of the FE while controlling for alternate effects (ie. cohort vs. test version effects, within-level vs. between-level effects, genetic vs. environmental components). At the between-subjects level, we hypothesized that we would observe evidence of the FE in 2 ways: (1) for a given age and test version, children who participated more recently in the LTS testing period would have higher cognitive ability scores on average than previous cohorts; (2) for a given age and cohort, children who took newer WISC versions would score lower on average than children who took older versions. Within individual children, we expected that within-person FEs would boost the rate at which children grew intellectually between ages 7 and 15 beyond expected age-related growth. Because this was the first genetically formed study of the FE, we treated our biometric analyses as exploratory.
…Developmental changes in cognitive ability can be difficult to observe when cohort, test version, and age are all varying simultaneously. To our knowledge, this was the first study to document cohort and test version FEs together in a single sample. Studies that use either the cohort or test version approach face major limitations inherent in each method (limited representativeness of military conscripts in the former, vulnerability to changes in content between test versions in the latter). The fact that we documented both types of effects substantially increases our confidence that the FE is robust in the LTS sample. Furthermore, modeling both cohort and test version effects enabled us to document the full manifestation of the FE in the LTS, which otherwise might not have been apparent. Although mean IQ scores were ~100 at each age (Table 1), this apparent stability was the result of a complex process in which gains across cohorts and within individuals across age were balanced out by decreases in scores due to test restandardization. Analyzing one type of FE without controlling for the other would have revealed only half of the story…These results suggest that the FE boosted both individual cognitive growth between ages 7 and 15 relative to age-based norms and mean cognitive ability scores across generations. Our results provide novel evidence of within-person FEs not only on fluid intelligence, as documented previously (O’Keefe & Rodgers 2017), but also crystallized and general cognitive ability (as measured by VIQ and FSIQ, respectively). Moreover, our within-person FE findings speak to the importance of modeling the FE at multiple levels of analysis, where possible. By capturing both within-level and between-level FEs, multi-level models offer a more nuanced understanding of how the FE operates across development. At least in our sample, the FE was not only a population-level phenomenon that drives broad gains in mean cognitive ability across generations. The FE also appeared to influence the cognitive development of individual children, boosting their intellectual growth beyond what would have occurred without positive environmental inputs. Had we only measured between-level FEs, as is traditionally done in FE research, we would have missed this important aspect of cognitive development.
…Our finding that sensitivity to the FE on FSIQ, VIQ, and PIQ all showed substantial heritability (A) serves as a fascinating example of gene-environment interplay—the extent to which a child’s growth in cognitive ability received a boost from the environment was influenced by genetic factors. The environment also plays an important role, as variance in shared (C) and non-shared (E) environmental factors were both associated with individual-level sensitivity to the FE. Given the magnitudes of the observed standard errors, interpretations about possible biometric differences across cognitive domains should be made with some caution. That being said, results suggested that sensitivity to the FE on FSIQ may be more heritable than sensitivity to the FE on more specific cognitive domains (ie. crystallized intelligence and fluid intelligence as estimated by VIQ and PIQ, respectively). If robust, this variability speaks to the utility of analyzing the FE in multiple cognitive domains, as biometric results from FE analyses of general intelligence (eg. IQ) may not apply directly to more specific measures of cognitive ability.
Background: Almost 2 decades of research produced mixed findings on the relationship between celebrity worship and cognitive skills. Several studies demonstrated that cognitive performance slightly decreases with higher levels of celebrity worship, while other studies found no association between these constructs. This study has 2 aims: (1) to extend previous research on the association between celebrity worship and cognitive skills by applying the two-factor theory of intelligence by Cattell on a relatively large sample of Hungarian adults, and (2) to investigate the explanatory power of celebrity worship and other relevant variables in cognitive performance.
Methods: A cross-sectional study design was used. Applying an online survey, a total of 1,763 Hungarian adults (66.42% male, Mage = 37.22 years, SD = 11.38) completed 2 intelligence subtests designed to measure ability in vocabulary (Vocabulary Test) and digit symbol (Short Digit Symbol Test). Participants also completed the Celebrity Attitude Scale and the Rosenberg Self-esteem Scale. Subjective material wealth, current family income and general sociodemographics were also reported by participants.
Results: Linear regression models indicated that celebrity worship was associated with lower performance on the cognitive tests even after controlling for demographic variables, material wealth and self-esteem, although the explanatory power was limited.
Conclusions: These findings suggest that there is a direct association between celebrity worship and poorer performance on the cognitive tests that cannot be accounted for by demographic and socioeconomic factors.
Some research has investigated the Big Five personality dimensions among gifted individuals, but these individual studies have provided inconclusive results.
The current meta-analysis examined the nature of the relationship between the Big Five dimensions and giftedness among individuals. Hedge’s unbiased g was used as the effect size metric, and a 3-level multilevel meta-analytic approach was applied, due to the dependency among the effect-sizes obtained from the same study.
The analyses used 82 effect sizes, from 13 published studies, and indicated that there was a statistically-significant difference between gifted and non-gifted participants in terms of Openness to Experience in favor of gifted individuals (g = 0.473, p = 0.005, 95% CI [0.199, 0.747]). However, there were no statistically-significant differences in terms of Extraversion, Agreeableness, Conscientiousness, and Neuroticism.
The implications and limitations of the findings are discussed.
[Keywords: gifted, personality, the Big Five model, meta-analysis, multilevel]
There is an ongoing debate regarding the degree to which a forecaster’s ability to draw correct inferences from market signals is real or illusory. This paper attempts to shed light on the debate by examining how personal characteristics do or do not affect forecaster success. Specifically, we investigate the role of fluid intelligence, manipulativeness, and theory of mind on forecast accuracy in experimental asset markets.
We find that intelligence improves forecaster performance when market mispricing is low, manipulativeness improves forecaster performance when mispricing is high, and the degree to which theory of mind skills matter depends on both the level of mispricing and how information is displayed. All three of these results are consistent with hypotheses derived from the previous literature. Additionally, we observe that male forecasters outperform female forecasters after controlling for intelligence, manipulativeness, and theory of mind skills as well as risk aversion. Interestingly, we do not find any evidence that forecaster performance improves with experience across markets or within markets.
Cognitive variation is common among-individuals within populations, and this variation can be consistent across time and context. From an evolutionary perspective, among-individual variation is important and required for natural selection. Selection has been hypothesised to favour high cognitive performance, however directional selection would be expected to erode variation over time. Additionally, while variation is a prerequisite for natural selection, it is also true that selection does not act on traits in isolation. Thus, the extent to which performance covaries among specific cognitive domains, and other aspects of phenotype (eg. personality traits) is expected to be an important factor in shaping evolutionary dynamics. Fitness trade-offs could shape patterns of variation in performance across different cognitive domains, however positive correlations between cognitive domains and personality traits are also known to occur. Here we aimed to test this idea using a multivariate approach to characterise and test hypothesised relationships of cognitive performance across multiple domains and personality, in the Trinidadian guppy (Poecilia reticulata). We estimate the among-individual correlation matrix (ID) in performance across three cognitive domains; association learning in a colour discrimination task; motor cognition in a novel motor task and cognitive flexibility in a reversal learning task, and the personality trait boldness, measured as time to emerge. We found no support for trade-offs occurring, but the presence of strong positive domain-general correlations in ID, where 57% of the variation is explained by the leading eigen vector. While highlighting caveats of how non-cognitive factors and assay composition may affect the structure of the ID-matrix, we suggest that our findings are consistent with a domain-general axis of cognitive variation in this population, adding to the growing body of support for domain-general variation among-individuals in animal cognitive ability.
The use of spoken and written language is a capacity that is unique to humans. Individual differences in reading-related and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30–80%, depending on the trait. The relevant genetic architecture is complex, heterogeneous, and multifactorial, and yet to be investigated with well-powered studies.
Here, we present a multi-cohort genome-wide association study (GWAS) of 5 traits assessed individually using psychometric measures: word reading, non-word reading, spelling, phoneme awareness, and non-word repetition, with total sample sizes ranging from 13,633 to 33,959 participants aged 5–26 years (12,411 to 27,180 for those with European ancestry, defined by principal component analyses).
We identified a genome-wide statistically-significant association with word reading (rs11208009, p = 1.098 x 10–8) independent of known loci associated with intelligence or educational attainment. All five reading/language-related traits had robust SNP-heritability estimates (0.13–0.26), and genetic correlations between them were modest to high. Using genomic structural equation modelling, we found evidence for a shared genetic factor explaining the majority of variation in word and non-word reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to non-word repetition, intelligence and educational attainment.
A multivariate GWAS was performed to jointly analyse word and non-word reading, spelling, and phoneme awareness, maximizing power for follow-up investigation. Genetic correlation analysis of multivariate GWAS results with neuroimaging traits identified association with cortical surface area of the banks of the left superior temporal sulcus, a brain region with known links to processing of spoken and written language.
Analysis of evolutionary annotations on the lineage that led to modern humans showed enriched heritability in regions depleted of Neanderthal variants.
Together, these results provide new avenues for deciphering the biological underpinnings of these uniquely human traits.
The past 30 years of research in intelligence has produced a wealth of knowledge about the causes and consequences of differences in intelligence between individuals, and today mainstream opinion is that individual differences in intelligence are caused by both genetic and environmental influences. Much more contentious is the discussion over the cause of mean intelligence differences between racial or ethnic groups. In contrast to the general consensus that interindividual differences are both genetic and environmental in origin, some claim that mean intelligence differences between racial groups are completely environmental in origin, whereas others postulate a mix of genetic and environmental causes.
In this article I discuss 5 lines of research that provide evidence that mean differences in intelligence between racial and ethnic groups are partially genetic. These lines of evidence are:
I also discuss future potential lines of evidence regarding the causes of average group differences across racial groups.
However, the data are not fully conclusive, and the exact degree to which genes influence intergroup mean differences in intelligence is not known. This discussion applies only to native English speakers born in the United States and not necessarily to any other human populations.
Focal cortical lesions lead to local, not global, deficits.
Measurement models to explain the positive manifold are causal models with unique predictions going beyond model fit statistics.
Correlated factor, network, process sampling, mutualism, investment models, make causal predictions inconsistent with lesion evidence.
Hierarchical and bifactor models are consistent with the pattern of lesion effects, as well as possibly one form of bonds sampling models.
Future models and explanations of the positive manifold have to accommodate focal lesions leading to local not global deficits.
Here we examine 3 classes of models regarding the structure of human cognition: common cause models, sampling/network models, and interconnected models. That disparate models can accommodate one of the most globally replicated psychological phenomena—namely, the positive manifold—is an extension of underdetermination of theory by data. Statistical fit indices are an insufficient and sometimes intractable method of demarcating between the theories; strict tests and further evidence should be brought to bear on understanding the potential causes of the positive manifold. The cognitive impact of focal cortical lesions allows testing the necessary causal connections predicted by competing models. This evidence shows focal cortical lesions lead to local, not global (across all abilities), deficits. Only models that can accommodate a deficit in a given ability without effects on other covarying abilities can accommodate focal lesion evidence. After studying how different models pass this test, we suggest bifactor models (class: common cause models) and bond models (class: sampling models) are best supported. In short, competing psychometric models can be informed when their implied causal connections and predictions are tested.
[Keywords: human intelligence, structural models, causality, statistical model fit, cortical lesions]
[This would seem to explain the failure of dual n-back & WM training in general.
Training the specific ability of WM could only cause g increases in models with ‘upwards causation’ like hierarchical models or dynamic mutual causation like mutualism/investment models; these are ruled out by the lesion literature which finds that physically-tiny lesions damage specific abilities but not g, and if decreasing a specific ability cannot decrease g, then it’s hard to see how increasing that ability could ever increase g. See also Lee et al 2019.]
Self-reported mate preferences suggest intelligence is valued across cultures, consistent with the idea that human intelligence evolved as a sexually selected trait. The validity of self-reports has been questioned though, so it remains unclear whether objectively assessed intelligence is indeed attractive.
In Study 1, 88 target men had their intelligence measured and based on short video clips were rated on intelligence, funniness, physical attractiveness and mate appeal by 179 women. In Study 2 (n = 763), participants took part in 2 to 5 speed dating sessions in which their intelligence was measured and they rated each other’s intelligence, funniness, and mate appeal.
Measured intelligence did not predict increased mate appeal in either study, whereas perceived intelligence and funniness did. More intelligent people were perceived as more intelligent, but not as funnier.
Results: suggest that intelligence is unimportant for initial attraction, which raises doubts concerning the sexual selection theory of intelligence.
[Keywords: intelligence, mate choice, sexual selection]
The human brain is organised into networks of interconnected regions that have highly correlated volumes. In this study, we aim to triangulate insights into brain organisation and its relationship with cognitive ability and ageing, by analysing genetic data.
We estimated general genetic dimensions of human brain morphometry within the whole brain, and 9 predefined canonical brain networks of interest. We did so based on principal components analysis (PCA) of genetic correlations among grey-matter volumes for 83 cortical and subcortical regions (nparticipants = 36,778).
We found that the corresponding general dimension of brain morphometry accounts for 40% of the genetic variance in the individual brain regions across the whole brain, and 47–65% within each network of interest. This genetic correlation structure of regional brain morphometry closely resembled the phenotypic correlation structure of the same regions. Applying a novel multivariate methodology for calculating SNP effects for each of the general dimensions identified, we find that general genetic dimensions of morphometry within networks are negatively associated with brain age (rg = −0.34) and profiles characteristic of age-related neurodegeneration, as indexed by cross-sectional age-volume correlations (r = −0.27). The same genetic dimensions were positively associated with a genetic general factor of cognitive ability (rg = 0.17–0.21 for different networks).
We have provided a statistical framework to index general dimensions of shared genetic morphometry that vary between brain networks, and report evidence for a shared biological basis underlying brain morphometry, cognitive ability, and brain ageing, that are underpinned by general genetic factors.
…This indicates that the genetic association between brain morphometry and cognitive ability was not driven by specific network configurations. Instead, dimensions of shared genetic morphometry in general indexed genetic variance relevant to larger brain volumes and a brain organisation that is advantageous for better cognitive performance. This was regardless of how many brain regions and from which regions the measure of shared genetic morphometry was extracted. This lack of differentiation between networks, in how strongly they correlate with cognitive ability, is in line with the suggestion that the total number of neurons in the mammalian cortex, which should at least partly correspond to its volume, is a major predictor of higher cognitive ability.37 These findings suggest that highly shared brain morphometry between regions, and its genetic analogue, indicate a generally bigger, and cognitively better-functioning brain.
Predictive processing is emerging as a common computational hypothesis to account for diverse psychological functions subserved by a brain, providing a systems-level framework for characterizing structure-function relationships of its distinct substructures. Here, we contribute to this framework by examining gradients of functional connectivity as a low dimensional spatial representation of functional variation in the brain and demonstrating their computational implications for predictive processing. Specifically, we investigated functional connectivity gradients in the cerebral cortex, the cerebellum, and the hippocampus using resting-state functional MRI data collected from large samples of healthy young adults. We then evaluated the degree to which these structures share common principles of functional organization by assessing the correspondence of their gradients. We show that the organizing principles of these structures primarily follow two functional gradients consistent with the existing hierarchical accounts of predictive processing: A model-error gradient that describes the flow of prediction and prediction error signals, and a model-precision gradient that differentiates regions involved in the representation and attentional modulation of such signals in the cerebral cortex. Using these gradients, we also demonstrated triangulation of functional connectivity involving distinct subregions of the three structures, which allows characterization of distinct ways in which these structures functionally interact with each other, possibly subserving unique and complementary aspects of predictive processing. These findings support the viability of computational hypotheses about the functional relationships between the cerebral cortex, the cerebellum, and the hippocampus that may be instrumental for understanding the brain’s dynamics within its large-scale predictive architecture.
The dark triad of personality (D3)—consisting of psychopathy, Machiavellianism, and narcissism—is a set of socially aversive personality traits. All 3 traits encompass disagreeable behavior and a particular disregard for the well-being of others, but also a tendency to strategic and deceptive manipulation of social environments in order to attain one′s goals. To exercise these complex manipulations effectively it seems beneficial to have high cognitive abilities.
Therefore, a meta-analysis was conducted to examine possible relationships between intelligence and the dark triad. A total of 143 studies were identified to estimate the strength of relationships between the D3 and general, verbal, and nonverbal intelligence.
The results indicate that none of the constructs of the dark triad are meaningfully related to intelligence. However, there was a small negative correlation between intelligence and Factor 2 psychopathy. The substantial heterogeneity regarding the observed effect-sizes could not be explained with meta-regression for the most part. There was no evidence for a publication bias.
In total, the results challenge the notion that the dark triad is an adaptive set of personality traits that enables individuals to effectively manipulate their social surroundings.
Participants (n = 275) completed a questionnaire about 35 intelligence myths.
18 myths were rated as true (definitely or partly), 2 as definitely false and 6 a probably false.
There were no statistically-significant demographic or personality correlates of the total correct score.
The paper considers why myths, misconceptions and ignorance seem so difficult to dispel.
This study is concerned with the extent to which people believe in, and endorse, various myths about intelligence and intelligence testing.
It examined the prevalence of myths about intelligence as set out in a recent book (Warne 2020). Participants (n = 275) completed a questionnaire in which they rated the extent to which they thought various statements/facts about intelligence were essentially true or false.
In all, 18 of these myths were rated as true (definitely or partly), 2 as definitely false and 6 probably false by the majority of the participants. There were no statistically-significant demographic or personality correlates of the total correct score (determined by rating the myth as false).
The discussion considers why, in this important area of psychology, myths, misconceptions and ignorance seem so difficult to dispel. Limitations of this, and similar, studies are noted, and implications are discussed.
Genetic and environmental sources of variance in IQ were estimated from 486 adoptive and biological families
Families include 419 mothers, 201 fathers, 415 adopted and 347 biological fully-adult offspring (Mage = 31.8 years; SD = 2.7)
Proportion of variance in IQ attributable to environmentally mediated effects of parental IQs was estimated at 0.01 [95% CI 0.00, 0.02]
Heritability was estimated to be 0.42 [95% CI 0.21, 0.64]
Parent-offspring correlations for educational attainment polygenic scores show no evidence of adoption placement effect
While adoption studies have provided key insights into the influence of the familial environment on IQ scores of adolescents and children, few have followed adopted offspring long past the time spent living in the family home.
To improve confidence about the extent to which shared environment exerts enduring effects on IQ, we estimated genetic and environmental effects on adulthood IQ in an unique sample of 486 biological and adoptive families. These families, tested previously on measures of IQ when offspring averaged age 15, were assessed a second time nearly 2 decades later (Moffspring age = 32 years).
We estimated the proportions of the variance in IQ attributable to environmentally mediated effects of parental IQs, sibling-specific shared environment, and gene-environment covariance to be 0.01 [95% CI 0.00, 0.02], 0.04 [95% CI 0.00, 0.15], and 0.03 [95% CI 0.00, 0.07] respectively; these components jointly accounted for 8% of the IQ variance in adulthood. The heritability was estimated to be 0.42 [95% CI 0.21, 0.64].
Together, these findings provide further evidence for the predominance of genetic influences on adult intelligence over any other systematic source of variation.
…The question of persistence is perhaps the most important in considering the effects of the rearing environment on IQ, especially since other types of studies have documented a “fadeout” of environmental improvements over time (eg. Protzko 2015). Kendler and colleagues have used a cosibling control design to examine the effect of the rearing environment on IQ in a sample of 436 adoptive-biological sibships (Kendler et al 2015). These male, 18–20 year old adopted Swedish conscripts showed a mean gain in 4.41 IQ points relative to their biological siblings, who were raised by the original biological family. This finding, which they replicated in a larger sample of half-sibs (with a mean gain of 3.18 IQ points associated with adoption), is a strong indicator that IQ can be, to some extent, affected up to late adolescence by the family environment. These results are consistent with those from the classic cross-fostering study of 14-year-old French children by Capron & Duyme 1989.
Studies such as these do suggest that although this effect is small relative to the genetic effects on IQ, it is not zero; however, the size of this effect diminishes substantially after adolescence. Sandra Scarr, a pioneer of modern IQ adoption studies, was perhaps the first to note this fadeout phenomenon (eg. Scarr & Weinberg 1978). With respect to the tapering correlations in IQ between children and their adoptive parents as the child matured, observed in the Minnesota Adolescent Adoption Study, Scarr & Weinberg remarked:
“We interpret the results to mean that younger children, regardless of their genetic relatedness, resemble each other intellectually because they share a similar rearing environment. Older adolescents, on the other hand, resemble one another only if they share genes. Our interpretation is that older children escape the influences of the family and are freer to select their own environments. Parental influences are diluted by the more varied mix of adolescent experiences.” (Scarr & Weinberg, 1983, p. 264).
…We tested for placement effects using polygenic scores for educational attainment (PGSEA) derived from the largest genome-wide association study to date (Lee et al 2018)…In an aggregated sample consisting of all white participants (offspring and both parents), an R2 of 0.154 [95% CI 0.113, 0.195] in our prediction of Verbal IQ with a PGS surpasses all previous benchmarks known to us (Allegrini et al 2019; Lee et al 2018; Rietveld et al 2014; Savage et al 2018; Selzam et al 2016; Sniekers et al 2017), and an R2 of 0.114 [95% CI 0.077, 0.151] for Total IQ is near the upper end of previous predictions (SI Table S13). However, we acknowledge that our sample is not large by the standards of PGS validation…These scores also enable an unique test for the so-called “placement effect”, wherein adoptees (typically twins reared apart) are thought by some skeptics to resemble their adoptive parents prior to placement, thus biasing biometrical estimates. By demonstrating a total lack of evidence (p = 0.514) for a correlation between parents and adoptive offspring in polygenic scores, we provide support for the validity of at least some adoption studies in establishing causal inference…the similarity of these correlations to their theoretically predicted values provides evidence that the placement of adoptees in their homes was not strongly purposive or selective, implying that the adoption process may somewhat approximate a true experiment.
…IQ has been subject to a large number of twin and adoption studies, many of which have found a small but statistically-significant effect of parental transmission in adoptive samples up until late adolescence…Our biometric decomposition of variance is consistent with this figure: the parental environment contributing 4% of the variance in fullscale IQs at age ~15 (Table 3), with a standard deviation of 14.2 for full-scale IQ in adopted offspring, indicates that a 1-SD increase in the quality of the parental environment would increase IQ by approximately 2.83 points (ie. 14.2 × √0.04; Burks 1938).
The evidence for parenting effects on Wechsler IQ subtests is more equivocal, and biometric decomposition reveals a moderate but statistically-significant effect of gene-environment covariance on Vocabulary in childhood. While a similarly-sized G-E covariance is observed for childhood Total IQ, this effect has completely disappeared in adulthood; the same cannot be said unambiguously for Vocabulary, which retains weak evidence in adulthood for a persistent parenting effect.
Many but not all cognitive abilities decline during ageing. Some even improve due to lifelong experience. The critical capacities of attention and executive functions have been widely posited to decline. However, these capacities are composed of multiple components, so multifaceted ageing outcomes might be expected. Indeed, prior findings suggest that whereas certain attention/executive functions clearly decline, others do not, with hints that some might even improve.
We tested ageing effects on the alerting, orienting and executive (inhibitory) networks posited by Posner and Petersen’s influential theory of attention, in a cross-sectional study of a large sample (n = 702) of participants aged 58–98. Linear and nonlinear analyses revealed that whereas the efficiency of the alerting network decreased with age, orienting and executive inhibitory efficiency increased, at least until the mid-to-late 70s. Sensitivity analyses indicated that the patterns were robust.
The results suggest variability in age-related changes across attention/executive functions, with some declining while others improve.
Students’ socioeconomic status (SES) is central to much research and policy deliberation on educational inequalities. However, the SES model is under severe stress for several reasons.
SES is an ill-defined concept, unlike parental education or family income. SES measures are frequently based on proxy reports from students; these are generally unreliable, sometimes endogenous to student achievement, only low to moderately intercorrelated, and exhibit low comparability across countries and over time.
There are many explanations for SES inequalities in education, none of which achieves consensus among research and policy communities.
SES has only moderate effects on student achievement, and its effects are especially weak when considering prior achievement, an important and relevant predictor.
SES effects are substantially reduced when considering parent ability, which is causally prior to family SES.
The alternative cognitive ability/genetic transmission model has far greater explanatory power; it provides logical and compelling explanations for a wide range of empirical findings from student achievement studies.
The inadequacies of the SES model are hindering knowledge accumulation about student performance and the development of successful policies.
Context and implications:
Rationale for this study:
This review was written in response to the disconnect between the literature surrounding student achievement studies, and the cognitive psychology and behavioural genetic academic literatures. It is well-established that student achievement is closely related to cognitive ability and both have sizable genetic components, findings largely ignored in achievement studies. This review’s aim is for more considered responses to socioeconomic inequalities in student achievement by both researchers and policymakers.
Why the new findings matter:
The review provides overwhelming evidence that much of the current thinking about SES and student achievement is mistaken.
Implications for researchers and policymakers:
The current emphasis on SES is misleading and wastes considerable human and financial resources that could much better be utilized. The focus should be on student performance ensuring that low achievers have rewarding educational and occupational careers, and raising the overall skill levels of students, not on the nebulous, difficult to measure, concept of SES, which is only moderately associated with achievement.
Objectives: Despite the broad appeal of abstract notions of political tolerance, people vary in the degree to which they support the political rights of groups they dislike. Prior research highlighted the relevance of individual differences in the cognitive domain, claiming the application of general tolerance ideals to specific situations is a cognitively demanding task. Curiously, this work has overwhelmingly focused on differences in cognitive style, largely neglecting differences in cognitive ability, despite compelling conceptual linkages. We remedy this shortcoming.
Methods: We explore diverse predictors of tolerance using survey data in 2 large samples from Denmark (n = 805) and the United States (n = 1,603).
Results: Cognitive ability was the single strongest predictor of political tolerance, with larger effects than education, openness to experience, ideology, and threat. The cognitively demanding nature of tolerance judgments was further supported by results showing cognitive ability predicted tolerance best when extending such tolerance was hardest. Additional small-sample panel results demonstrated substantial 4-year stability of political tolerance, informing future work on the origins of political tolerance.
Conclusions: Our observation of a potent role for cognitive ability in tolerance supports cognitively oriented accounts of tolerance judgments and highlights the need for further exploration of cognitive ability within the political domain.
…Our Danish sample was selected using a registry of military draftees, which has been in operation since 2006. We drew our sample from the subset of the registry which had taken a cognitive ability test…For our American sample, 2,766 respondents completed our survey using Mechanical Turk in September & October 2014.
This paper explores inequalities in IQ and economic preferences between children from families of high and low socioeconomic status (SES). We document that children from high-SES families are more intelligent, patient, and altruistic as well as less risk seeking. To understand the underlying mechanisms, we propose a framework of how SES, parental investments, as well as maternal IQ and preferences influence a child’s IQ and preferences. Our results indicate that disparities in the level of parental investments hold substantial importance. In light of the importance of IQ and preferences for behaviors and outcomes, our findings offer an explanation for social immobility.
[blog] A recent study by Dutton et al 2019 found that the religiousness-IQ nexus is not on g when comparing different groups with various degrees of religiosity and the non-religious. It suggested, accordingly, that the nexus related to the relationship between specialized analytic abilities on the IQ test and autism traits, with the latter predicting atheism. The study was limited by the fact that it was on group-level data, it used only one measure of religiosity that measure may have been confounded by the social element to church membership and it involved relatively few items via which a Jensen effect could be calculated.
Here, we test whether the religiousness-IQ nexus is on g with individual-level data using archival data from the Vietnam Experience Study, in which 4,462 US veterans were subjected to detailed psychological tests. We used multiple measures of religiosity—which we factor-analysed to a religion-factor—and a large number of items.
We found, contrary to the findings of Dutton et al 2019, that the IQ differences with regard to whether or not subjects believed in God are indeed a Jensen effect. We also uncovered a number of anomalies, which we explore.
We develop a novel graphical paradigm of a strict-dominance-solvable game to study the developmental trajectory of steps of reasoning between 8 years old and adulthood.
Most participants play the equilibrium action either always or only when they have a dominant strategy. Although age is a determinant of equilibrium choice, some very young participants display an innate ability to play at equilibrium. Finally, the proportion of equilibrium play increases statistically-significantly until fifth grade and stabilizes afterward, suggesting that the contribution of age to equilibrium play vanishes early in life.
…To minimize these concerns, we propose a novel graphical interface in which subjects possess 3 objects with 3 attributes each: a shape, a color, and a letter. Their goal is to select an object with a certain characteristic, which depends on the object selected by another player in the game. This is true for all but one player, who must simply match a feature of a specific single object. This player’s decision constitutes the starting point of the iteration process, and the problem of the other players can be iteratively solved by successive elimination, with a maximum of 3 steps of reasoning.
To analyze the developmental trajectory of behavior in our paradigm, we recruited 3 populations. The first experiment involves a population of children and adolescents (8–18 years old) recruited at a single private school in Los Angeles, as well as a control young-adult population from the University of Southern California. This experiment tests the effect of age on strategic sophistication. In the second experiment, we recruited younger children (5–8 years old) from that same school and we implemented a simpler version of the same game. This experiment is designed to assess whether the skills detected in children older than 8 years old are already developing before that age. Last, we recruited a third population of middle schoolers (11–14 years old) from a single public school also in Los Angeles. This experiment aims to inform us on the potential impact of school characteristics and student demographics on sophistication.
Our analysis yields 3 main results. First, the vast majority of participants either play always at equilibrium or they play at equilibrium only when they have a dominant strategy. There are few random players, and virtually no one exhibits an “intermediate” level of reasoning (ie. plays at equilibrium when it requires 2 steps of reasoning but not when it requires 3 steps). This is in sharp contrast with the existing adult literature that emphasizes large heterogeneity in levels of reasoning and abundance of intermediate types (Costa-Gomes, Crawford, and Broseta 2001; Johnson et al 2002; Costa-Gomes & Crawford 2006; Brañas-Garza, Espinosa, and Rey-Biel 2011; Brocas et al 2014; Kneeland 2015; Gill & Prowse 2016; Brocas, Carrillo, and Sachdeva 2018). Second, although age is an important determinant of equilibrium thinking, there is an ability component that is either innate or acquired at a very young age. Furthermore, the evolution over the entire window of observation is not as steep as one might expect. Indeed, the proportion of individuals who consistently play at equilibrium is statistically-significantly above 0 at 8 years old (24%) and statistically-significantly below 1 at 17 years old (59%). Third and related, the change in equilibrium play is not constant. Choice improves statistically-significantly between third and fifth grade and stabilizes afterward. In other words, the contribution of age to equilibrium behavior vanishes relatively early in life (between 12 and 13 years old). Our data reveal important predictors of performance. We find that female participants and subjects with a self-reported preference for science subjects perform statistically-significantly better. Finally, differences across schools and across tracks within schools are also associated with differences in sophistication. In particular, we find that students enrolled in different programs or in different GPA-based tracks within programs exhibit different levels of sophistication. [IQ/intelligence was not measured.] Overall, even though the main pattern of behavior (namely, the absence of an intermediate level of reasoning) is replicated in all populations, the distribution of sophistication is modulated by individual and group characteristics.
…We designed a simple paradigm in which subjects were matched in groups of 3 and assigned a role as player 1, player 2, or player 3, from now on referred to as role 1, role 2, and role 3. Each player in the group had 3 objects, and each object had 3 attributes: a shape (square, triangle, or circle), a color (red, blue, or yellow), and a letter (A, B, or C). Players had to simultaneously select one object. Role 1 would obtain points if the object he chose matched a given attribute of the object chosen by role 2. Similarly, role 2 would obtain points if the object he chose matched a given attribute of the object chosen by role 3. Finally, role 3 would obtain points if the object he chose matched a given attribute of an extra object. The attributes to be matched were different for different roles and specified by the experimenter. Accordingly, in each game any number of participants could obtain points. All options and objectives of players were common knowledge and displayed on the computer screen. Figure 1 provides a screenshot of the game as seen by role 2. The game can be easily solved with an inductive argument starting from role 3. In the example of Figure 1, role 3 has to match the shape of the outside object, so he obtains the points if he chooses the red square C. Conditional on that choice, role 2 obtains points if he chooses the red triangle B, and, again conditional on that choice, role 1 obtains points if he chooses the yellow circle B (the original software uses easily distinguishable colors).9
Figure 1: Screenshot of the game (as seen by role 2).
Figure 2: Proportion of equilibrium choices by grade and role.
…Figure 3 reports the proportion of subjects by grade [strategy type] who are classified under each type, from most sophisticated (bottom) to least sophisticated (top). In strong support of hypothesis 1, our theoretical model provides a very solid behavioral template. Indeed, the choice of 76% of LILA students and 97% of USC students can be accounted for by one of the 4 types described in section II.B. The proportion of subjects who do not fit in one of these types (O) decreases with age, although it is statistically smaller only for tenth graders. In other words, the level-k behavioral theory that has proved successful in explaining nonequilibrium behavior of adults performs well also with children and adolescents.
Figure 3: Proportion of subjects by type and grade.
…Hypothesis 2 is not supported by the data. Subjects either recognize only a dominant strategy or always play at equilibrium. Also, some very young players display an innate ability to play always at equilibrium while some young adults are unable to perform 2 steps of dominance.
…Hypothesis 3 is weakly supported by the data. Equilibrium performance increases with age very statistically-significantly during elementary school but it stabilizes in 6th grade.
…While equilibrium performance increases with age, there is also a substantial innate component: some of our youngest participants play perfectly from the first trial whereas some of our oldest participants do not go beyond one step of reasoning. Even though there is some evidence of learning, repeated exposure is ineffective at bringing participants to play Nash. Finally, performance increases statistically-significantly between 8 and 12 years of age and stabilizes afterward, suggesting that most of what is needed to solve dominance-solvable games is acquired by the end of elementary school. Interestingly, most students acquire complex mathematical skills during adolescence. Our observations suggest that this extra knowledge does not translate into better strategic decision making.
…Adolescents are particularly exposed to situations in which strategic sophistication is crucial to avoid wrong decisions. Examples include engaging in risky activities, such as accepting drugs from peers or engaging in unprotected sex. Also, with the development of the internet, naive users are often preyed upon, asked to provide personal information, or tricked into making harmful decisions. Information deliberately intended to deceive young minds also circulates through social media. Making correct decisions in such environments requires understanding the intentions of others and anticipating the consequences of following their advice or opinions. More generally, children and adolescents are gradually discovering the dangers hiding behind social interactions and need to come equipped to detect them, assess them, and navigate around them. We conjecture that failures in these abilities are closely related to underdeveloped logical abilities, and we predict that the level of sophistication of an individual detected through a simple task matches their behavior in social settings.
Using blood-based epigenome-wide analyses of general cognitive function (g; n = 9,162) we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in g. A DNAm predictor explains ~4% of the variance in g, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology-related and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. As sample sizes increase, our ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.
Personality traits relate to both STEM preferences and STEM specialization.
Openness and Agreeableness are the best predictors of STEM preferences.
Extraversion is the strongest predictor of actual choice for STEM.
Cognitive skills become more important when moving from preferences to actual choice.
There are markedly different patterns for boys compared to girls.
Around the developed world, the need for graduates from Science, Technology, Engineering and Mathematics (STEM) fields is growing. Research on educational and occupational choice has traditionally focused on the cognitive skills of prospective students, and on how these determine the expected costs and benefits of study programs. Little work exists that analyzes the role of personality traits on study choice.
This study investigates how personality traits relate to preferences of students for STEM studies and occupations, and to specialization choice in high school. We use a rich data set that combines administrative and survey data of Dutch secondary education students.
We find that personality traits are related to both the preference that students have for STEM as the actual decision to specialize in STEM studies, but to different degrees. We identify statistically-significant relations with preference indicators for all Big Five traits, especially for Openness to Experience (positive), Extraversion and Agreeableness (both negative). The size of these relations is often larger than those between cognitive skills and STEM preferences. Personality traits are comparatively less important with respect to the actual specialization choice, for which we identify a robust (and sizable) negative relation with Extraversion, and for girls find a positive relation with Openness to Experience.
The results suggest that once students have to make actual study choice decisions, they rely more on cognitive skills rather than personality traits, in contrast to their expressed preferences.
There has been some controversy as to whether baseline pupil size is related to individual differences in cognitive ability. Previously, we had shown that a larger baseline pupil size was associated with higher cognitive ability and that the correlation to fluid intelligence was larger than that to working memory capacity (Tsukahara et al 2016). However, other researchers have not been able to replicate our findings—though they only measured working memory capacity and not fluid intelligence. Many of the studies showing no relationship had major methodological issues, namely small baseline pupil size values—down to the physiological minimum—that resulted in reduced variability on baseline pupil size.
We conducted 2 large-scale studies to investigate how different lighting conditions affect baseline pupil size values and the correlation with cognitive abilities. We found that fluid intelligence, working memory capacity, and attention control did correlate with baseline pupil size except in the brightest lighting conditions. We showed that a reduced variability in baseline pupil size values is due to the monitor settings being too bright. Overall, our findings demonstrated that the baseline pupil size-working memory capacity relationship was not as strong or robust as that with fluid intelligence or attention control.
Our findings have strong methodological implications for researchers investigating individual differences in task-free or task-evoked pupil size. We conclude that fluid intelligence does correlate with baseline pupil size and that this is related to the functional organization of the resting-state brain through the locus coeruleus-norepinephrine system.
[Keywords: fluid intelligence, working memory capacity, pupil size, luminance]
…Our pupils respond to more than just the light. They indicate arousal, interest or mental exhaustion. Pupil dilation is even used by the FBI to detect deception. Now work conducted in our laboratory at the Georgia Institute of Technology suggests that baseline pupil size is closely related to individual differences in intelligence. The larger the pupils, the higher the intelligence, as measured by tests of reasoning, attention and memory. In fact, across 3 studies, we found that the difference in baseline pupil size between people who scored the highest on the cognitive tests and those who scored the lowest was large enough to be detected by the unaided eye.
…We found that a larger baseline pupil size was correlated with greater fluid intelligence, attention control and, to a lesser degree, working memory capacity—indicating a fascinating relationship between the brain and eye. Interestingly, pupil size was negatively correlated with age: older participants tended to have smaller, more constricted, pupils. Once standardized for age, however, the relationship between pupil size and cognitive ability remained.
But why does pupil size correlate with intelligence? To answer this question, we need to understand what is going on in the brain. Pupil size is related to activity in the locus coeruleus, a nucleus situated in the upper brain stem with far-reaching neural connections to the rest of the brain. The locus coeruleus releases norepinephrine, which functions as both a neurotransmitter and hormone in the brain and body, and it regulates processes such as perception, attention, learning and memory. It also helps maintain a healthy organization of brain activity so that distant brain regions can work together to accomplish challenging tasks and goals. Dysfunction of the locus coeruleus, and the resulting breakdown of organized brain activity, has been related to several conditions, including Alzheimer’s disease and attention deficit hyperactivity disorder. In fact, this organization of activity is so important that the brain devotes most of its energy to maintain it, even when we are not doing anything at all—such as when we stare at a blank computer screen for minutes on end.
One hypothesis is that people who have larger pupils at rest have greater regulation of activity by the locus coeruleus, which benefits cognitive performance and resting-state brain function. Additional research is needed to explore this possibility and determine why larger pupils are associated with higher fluid intelligence and attention control. But it’s clear that there is more happening than meets the eye.
We introduce the first game-based intelligence assessment in Minecraft.
Three intelligence tasks were implemented in the interactive game environment.
Two of the three tasks exhibit satisfactory psychometric properties.
Process data from game-logs encodes information about ability levels.
Minecraft is a promising platform for game-based assessment research.
Video games are a promising tool for the psychometric assessment of cognitive abilities. They can present novel task types and answer formats, they can record process data, and they can be highly motivating for test takers. This paper introduces the first game-based intelligence assessment implemented in Minecraft, an exceptionally popular video game with more than 200m copies sold.
A matrix-based pattern completion task (PC), a mental rotation task (MR) and a spatial construction task (SC) were implemented in the three-dimensional, immersive environment of the game. PC was intended as a measure of inductive reasoning, whereas MR and SC were measures of spatial ability. We tested 129 children aged 10–12 years old on the Minecraft-based tests as well as equivalent pen-and-paper tests. All three scales fit the Rasch model and were moderately reliable. Factorial validity was good with regard to the distinction between PC and SC, but no distinct factor was found for MR. Convergent validity was good as abilities measured with Minecraft and conventional tests were highly correlated at the latent level (r = 0.72). Subtest-level correlations were in the moderate range. Furthermore, we found that behavioral log-data collected from the game environment was highly predictive of performance in the Minecraft test and, to a lesser extent, also predicted scores in conventional tests. We identify a number of behavioral features associated with spatial reasoning ability, demonstrating the utility of analyzing granular behavioral data in addition to traditional response formats.
Overall, our findings indicate that Minecraft is a suitable platform for game-based intelligence assessment and encourage future work aiming to explore game-based problem solving tasks that would not be feasible on paper or in conventional computer-based tests.
[Keywords: Game-based assessment Intelligence assessment, Minecraft, Project Malmo, process data, game log-data]
Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some of which are novel—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.
Greater Vasa parrots are prospectively highly intelligent but critically understudied
An individual is found to spontaneously solve the string-pulling problem, and to be able to re-solve it after 7 years
This replicates previous findings, and expands on them
A general insight factor is identified among 14 parrot species
Covariance associates strongly with fission-fusion intensity, weakly with brain size and species differences
Spontaneous solving of an insight-based means-end reasoning task (the string-pulling problem) is observed in an adult male captive bred Greater Vasa parrot (Coracopsis vasa (Shaw 1812)), with an efficiency of 66%, replicating previous work in a singleton context. This case report adds to the existing literature on this species by also demonstrating longitudinal retention, specifically the same bird was found to be able to re-solve the simple form of the problem after a period of 7 years (the bird was first tested in 2013, and re-tested in 2020), with an efficiency of 43% (the difference between efficiencies was not statistically-significant, χ2 = 0.991, p = 0.319).
In a second analysis, species-level data across 5 patterned string-pulling tasks involving 14 parrot species were reanalysed, revealing that the Greater Vasa parrot exhibited the greatest general competence among those evaluated. A ‘general insight factor’ (GIF) was also found across taxa, the loadings onto which exhibit positive and large-magnitude associations with the correlation between fission-fusion flocking intensity and indicator level performance (r = 0.831), and also positive small and modest-magnitude associations with the correlation between relative brain size and indicator-level performance, and the magnitude of average pair-wise species differences in performance across indicators (r = 0.219 and 0.365 respectively).
Finally, the theoretical implications of these findings are discussed.
Cognitive enhancement interventions aimed at boosting human fluid intelligence (gf) have targeted executive functions (EFs), such as updating, inhibition, and switching, in the context of transfer-inducing cognitive training. However, even though the link between EFs and gf has been demonstrated at the psychometric level, their neurofunctional overlap has not been quantitatively investigated. Identifying whether and how EFs and gf might share neural activation patterns could provide important insights into the overall hierarchical organization of human higher-order cognition, as well as suggest specific targets for interventions aimed at maximizing cognitive transfer.
We present the results of a quantitative meta-analysis of the available fMRI and PET literature on EFs and gf in humans, showing the similarity between gf and (1) the overall global EF network, as well as (2) specific maps for updating, switching, and inhibition. Results highlight a higher degree of similarity between gf and updating (80% overlap) compared with gf and inhibition (34%), and gf and switching (17%). Moreover, 3 brain regions activated for both gf and each of the 3 EFs also were identified, located in the left middle frontal gyrus, left inferior parietal lobule, and anterior cingulate cortex. Finally, resting-state functional connectivity analysis on 2 independent fMRI datasets showed the preferential behavioural correlation and anatomical overlap between updating and gf.
These findings confirm a close link between gf and EFs, with implications for brain stimulation and cognitive training interventions.
Relation between intelligence and functional connectivity exhibited by P-FIT regions
2 independent samples comprising a total of 1140 healthy individuals
Matrix reasoning tests and fMRI resting-state imaging
Brodmann areas 7, 40 and 46 exhibit relevant connections across both samples
[See also “Multi-Task Brain Network Reconfiguration is Inversely Associated with Human Intelligence”, Thiele et al 2021] The Parieto-Frontal Integration Theory (P-FIT) predicts that human intelligence is closely linked to structural and functional properties of several brain regions mainly located in the parietal and frontal cortices. It also proposes that solving abstract reasoning tasks involves multiple processing stages and thus requires the harmonic interplay of these brain regions. However, empirical studies directly investigating the relationship between intellectual performance and the strength of individual functional connections related to the P-FIT network are scarce.
Here we demonstrate, in 2 independent samples comprising a total of 1489 healthy individuals, that fMRI resting-state connectivity, especially between P-FIT regions, is associated with interindividual differences in matrix reasoning performance. Interestingly, respective associations were only present in the overall samples and the female subsamples but not in the male subsamples, indicating a sex-specific effect. We found 5 statistically-significant connections which replicated across both samples. These were constituted by BAs 8, 10, 22, 39, 46, and 47 in the left as well as BAs 44 and 45 in the right hemisphere.
Given that many of these brain regions are predominantly involved in language processing, we hypothesized that our results reflect the importance of inner speech for solving matrix reasoning tasks. Complementary to previous research investigating the association between intelligence and functional brain connectivity by means of comprehensive network metrics, our study is the first to identify specific connections from the P-FIT network whose functional connectivity strength at rest can be considered an indicator of intellectual capability.
The field of cognitive epidemiology studies the prospective associations between cognitive abilities and health outcomes. We review research in this field over the past decade and describe how our understanding of the association between intelligence and all-cause mortality has consolidated with the appearance of new, population-scale data.
To try to understand the association better, we discuss how intelligence relates to specific causes of death, diseases/diagnoses and biomarkers of health through the adult life course. We examine the extent to which mortality and health associations with intelligence might be attributable to people’s differences in education, other indicators of socioeconomic status, health literacy and adult environments and behaviours. Finally, we discuss whether genetic data provide new tools to understand parts of the intelligence-health associations.
Social epidemiologists, differential psychologists and behavioural and statistical geneticists, among others, contribute to cognitive epidemiology; advances will occur by building on a common cross-disciplinary knowledge base.
In 10 years, the ability to predict intelligence from DNA has gone from 0% to 10%.
Genome-wide polygenic scores (GPS) are transforming research on intelligence.
GPS will transport intelligence to many new areas of science.
The availability of GPS at birth, prenatally, and before conception will impact society.
We need to maximize benefits and minimize risks of DNA prediction of intelligence.
The DNA revolution made it possible to use DNA to predict intelligence. We argue that this advance will transform intelligence research and society.
Our paper has 3 objectives:
First, we review how the DNA revolution has transformed the ability to predict individual differences in intelligence. Thousands of DNA variants have been identified that—aggregated into genome-wide polygenic scores (GPS)—account for more than 10% of the variance in phenotypic intelligence. The intelligence GPS is now one of the most powerful predictors in the behavioral sciences.
Second, we consider the impact of GPS on intelligence research. The intelligence GPS can be added as a genetic predictor of intelligence to any study without the need to assess phenotypic intelligence. This feature will help export intelligence to many new areas of science. Also, the intelligence GPS will help to address complex questions in intelligence research, in particular how the gene-environment interplay affects the development of individual differences in intelligence.
Third, we consider the societal impact of the intelligence GPS, focusing on DNA testing at birth, DNA testing before birth (eg. embryo selection), and DNA testing before conception (eg. DNA dating).
The intelligence GPS represents a major scientific advance, and, like all scientific advances, it can be used for bad as well as good. We stress the need to maximize the considerable benefits and minimize the risks of our new ability to use DNA to predict intelligence.
Despite a long-standing expert consensus about the importance of cognitive ability for life outcomes, contrary views continue to proliferate in scholarly and popular literature. This divergence of beliefs presents an obstacle for evidence-based policymaking and decision-making in a variety of settings.
One commonly held idea is that greater cognitive ability does not matter or is actually harmful beyond a certain point (sometimes stated as > 100 or 120 IQ points). We empirically tested these notions using data from four longitudinal, representative cohort studies comprising 48,558 participants in the United States and United Kingdom from 1957 to the present.
We found that ability measured in youth has a positive association with most occupational, educational, health, and social outcomes later in life. Most effects were characterized by a moderate to strong linear trend or a practically null effect (mean R2 range = 0.002–.256). Nearly all nonlinear effects were practically insignificant in magnitude (mean incremental R2 = 0.001) or were not replicated across cohorts or survey waves.
We found no support for any downside to higher ability and no evidence for a threshold beyond which greater scores cease to be beneficial. Thus, greater cognitive ability is generally advantageous—and virtually never detrimental.
Changes in mean intelligence test scores were minimal in Denmark in 2006–2019 [2006–2010: 111.5, 111.1, 110.8, 110.7, 110.6, | 2011–2019: 109.1, 109.2, 109.0, 109.1, 109.3, 109.2, 109.1, 108.7, 108.8].
A change in the format of the intelligence test resulted in a sudden drop in scores.
Neither changes in parental age, dysgenics, or immigration can explain the findings.
Changes in sample composition may conceal a true decline in intelligence test scores.
The present register-based study investigated the secular trend of intelligence test scores during the period from 2006 through 2019 in a Danish population-representative sample, as well as whether the observed trend could be explained by changes in parental age, dysgenics, and immigration or changes in the format of the intelligence test and sample characteristics.
The study population consisted of all Danish men appearing before a draft board during the study period (n = 400,288). Intelligence test scores were obtained by the use of Børge Priens Prøve, typically at age 19. For each of the included draft board cohorts, the intelligence test score mean and standard deviation were estimated.
The results showed that changes in mean intelligence test scores were minimal during the study period. A slight decline was observed from 2006 to 2010. Furthermore, there was a drop of 1.5 IQ points from 2010 to 2011, which coincided with the change in the format of the intelligence test from paper-and-pencil to computer-based, but there was essentially no change after 2011. Neither changes in parental age, dysgenics, or immigration seem to have influenced the observations. However, changes in sample composition may conceal a true decline in intelligence test scores given that a larger proportion of individuals with low intelligence seems to be exempted from testing.
In conclusion, the study findings suggest no systematic change in intelligence test scores during the last decade, but due to changes in sample composition, it cannot be excluded that there has been a negative secular trend.
…A slight decline in mean IQ score was observed from 2006 to 2010, which can be seen as a continuation of the decline previously reported between 1998 and 2004 (Teasdale & Owen 2008)…We have had the opportunity to rescale the mean intelligence test scores from the Danish draft board examinations reported by Teasdale & Owen 2008 against our baseline year 1960 to compare them with our observations. The rescaled mean IQ scores are as follows: 1988: 111.0 (SD: 13.0); 1998: 112.4 (SD: 12.7); 2003–4: 111.1 (SD: 12.8). As can be seen, there was an increase from 1988 to 1998 followed by a small decline from 1998 to 2003–4. The mean IQ score in 1998 remains the highest recorded using Danish draft board data, whereas the mean IQ score in 2003–4 is comparable with our mean intelligence test score in 2006. As such, there has been a decline of 1.8 IQ points during the period from 1998 through 2010 followed by a drop of 1.5 IQ points which is probably due to the change in the format of the intelligence test and virtually no change from 2011 through 2019. However, the variance has declined statistically-significantly throughout the study period, corresponding to a decline of 0.15 SD per year (p < 0.001). A previous study has suggested that the negative secular trend observed in developmental test performances may be rooted in declining performances of the top percentiles (Flynn & Shayer 2018), leading to declining variances. If this is also true in our study where the proportion of individuals with low test intelligence scores who were exempted from testing has increased over time, this might explain our observation of no change in mean intelligence test scores, but a declining variance.
[cf. Zmigrod 2022] Although human existence is enveloped by ideologies, remarkably little is understood about the relationships between ideological attitudes and psychological traits. Even less is known about how cognitive dispositions—individual differences in how information is perceived and processed—sculpt individuals’ ideological worldviews, proclivities for extremist beliefs and resistance (or receptivity) to evidence.
Using an unprecedented number of cognitive tasks (n = 37) and personality surveys (n = 22), along with data-driven analyses including drift-diffusion and Bayesian modelling, we uncovered the specific psychological signatures of political, nationalistic, religious and dogmatic beliefs.
Cognitive and personality assessments consistently outperformed demographic predictors in accounting for individual differences in ideological preferences by 4–15×. Furthermore, data-driven analyses revealed that individuals’ ideological attitudes mirrored their cognitive decision-making strategies. Conservatism and nationalism were related to greater caution in perceptual decision-making tasks and to reduced strategic information processing, while dogmatism was associated with slower evidence accumulation and impulsive tendencies. Religiosity was implicated in heightened agreeableness and risk perception. Extreme pro-group attitudes, including violence endorsement against outgroups, were linked to poorer working memory, slower perceptual strategies, and tendencies towards impulsivity and sensation-seeking—reflecting overlaps with the psychological profiles of conservatism and dogmatism.
Cognitive and personality signatures were also generated for ideologies such as authoritarianism, system justification, social dominance orientation, patriotism and receptivity to evidence or alternative viewpoints; elucidating their underpinnings and highlighting avenues for future research. Together these findings suggest that ideological worldviews may be reflective of low-level perceptual and cognitive functions.
Intelligence predicts important life and health outcomes, but the biological mechanisms underlying differences in intelligence are not yet understood. The use of genetically determined metabotypes (GDMs) to understand the role of genetic and environmental factors, and their interactions, in human complex traits has been recently proposed. However, this strategy has not been applied to human intelligence.
Here we implemented a 2-sample Mendelian randomization (MR) analysis using GDMs to assess the causal relationships between genetically determined metabolites and human intelligence. The standard inverse-variance weighted (IVW) method was used for the primary MR analysis and 3 additional MR methods (MR-Egger, weighted median, and MR-PRESSO) were used for sensitivity analyses.
Using 25 genetic variants as instrumental variables (IVs), our study found that 5-oxoproline was associated with better performance in human intelligence tests (pIVW = 9.25 × 10−5). The causal relationship was robust when sensitivity analyses were applied (pMR-Egger = 0.0001, pWeighted median = 6.29 × 10−6, PMR-PRESSO = 0.0007), and repeated analysis yielded consistent result (pIVW = 0.0087). Similarly, also dihomo-linoleate (20:2n6) and p-acetamidophenylglucuronide showed robust association with intelligence.
Our study provides novel insight by integrating genomics and metabolomics to estimate causal effects of genetically determined metabolites on human intelligence, which help to understanding of the biological mechanisms related to human intelligence.
Objectives: Debate about the cause of IQ score gaps between Black and White populations has persisted within genetics, anthropology, and psychology. Recently, authors claimed polygenic scores provide evidence that a substantial portion of differences in cognitive performance between Black and White populations are caused by genetic differences due to natural selection, the “hereditarian hypothesis.” This study aims to show conceptual and methodological flaws of past studies supporting the hereditarian hypothesis.
Materials and methods: Polygenic scores for educational attainment were constructed for African and European samples of the 1000 Genomes Project. Evidence for selection was evaluated using an excess variance test. Education associated variants were further evaluated for signals of selection by testing for excess genetic differentiation (Fst). Expected mean difference in IQ for populations was calculated under a neutral evolutionary scenario and contrasted to hereditarian claims.
Results: Tests for selection using polygenic scores failed to find evidence of natural selection when the less biased within-family GWAS effect sizes were used. Tests for selection using Fst values did not find evidence of natural selection. Expected mean difference in IQ was substantially smaller than postulated by hereditarians, even under unrealistic assumptions that overestimate genetic contribution.
Conclusion: Given these results, hereditarian claims are not supported in the least. Cognitive performance does not appear to have been under diversifying selection in Europeans and Africans. In the absence of diversifying selection, the best case estimate for genetic contributions to group differences in cognitive performance is substantially smaller than hereditarians claim and is consistent with genetic differences contributing little to the Black-White gap.
Water fluoridation is a common but debated public policy. In this paper, we use Swedish registry data to study the causal effects of fluoride in drinking water. We exploit exogenous variation in natural fluoride stemming from variation in geological characteristics at water sources to identify its effects. First, we reconfirm the long-established positive effect of fluoride on dental health. Second, we estimate a zero effect on cognitive ability in contrast to several recent debated epidemiological studies. Third, fluoride is furthermore found to increase labor income. This effect is foremost driven by individuals from a lower socioeconomic background.
…Let us continue to our main results. We begin with cognitive ability for men born between 1985 and 1987. Our conclusion from Table 4 is that fluoride does not affect cognitive ability. Column 1 displays the unconditional treatment effect. In columns 2 and 3, we add fixed effects for cohort and municipality of birth. We then include parental covariates, which results in a reduced sample since we have data on fathers’ cognitive ability only from 1969 and onward. To make the samples comparable with and without these covariates, we run column 4 for the same sample as in column 5. We also run two subsample analyses: in column 6, we run the analysis for those who have lived in the same SAMS in a municipality for the entire period from age 0 to 18, and in column 7 we restrict the sample to those who have moved only within a municipality.
Table 4: cognitive ability
Looking at the estimates, they are very small and often not statistically-significantly different from zero. Sometimes the estimates are negative and sometimes positive, but they are always close to zero. If we take the largest negative point estimates (−0.0047, col. 1) and the largest standard error for that specification (0.0045), the 95% confidence interval would be −0.014 to 0.004. We may thus rule out negative effects larger than 0.14 standard deviations in cognitive ability if fluoride is increased by 1 milligram/liter (the level often considered when artificially fluoridating the water).
…We now continue with the long-term outcome of annual labor income in 2014 for individuals born between 1985 and 1992. Given our results for cognitive ability, we do not expect negative effects of fluoride. However, positive effects are possible given the results found for dental health.
The results are presented in Table 5. The point estimates are often statistically-significant, and the coefficients are always positive. Taking column 6 as an example, where all covariates and fixed effects are included, we find that the point estimate equals 0.0044, meaning that income increases by 4.4% if fluoride is increased by 1 milligram/liter. These reduced form estimates may be compared with Glied & Neidell 2010, who, by using American data, found that women who drink fluoridated water have on average 4% higher earnings. Our estimated effect on income may also be compared with estimated education premiums. The return of one additional year of education yields an increase in income by 6%–10%, according to the instrumental variable estimates in the review in Card (1999). An increase in fluoride by 1 milligram/liter would thus yield a similar increase as roughly half a year of additional education. Nonlinear specifications are presented in figure A1 and tables A8–A10, which overall supports the findings presented here. In section B5 in the appendix, we present the result for employment status (another margin for labor market status), and we find that fluoride has a positive effect.
Differences in human general intelligence or reasoning ability can be quantified with the psychometric factor g, because individual performance across cognitive tasks is positively correlated. g also emerges in mammals and birds, is correlated with brain size and may similarly reflect general reasoning ability and behavioural flexibility in these species. To exclude the alternative that these positive cross-correlations may merely reflect the general biological quality of an organism or an inevitable by-product of having brains it is paramount to provide solid evidence for the absence of g in at least some species. Here, we show that wild-caught cleaner fish Labroides dimidiatus, a fish species otherwise known for its highly sophisticated social behaviour, completely lacks g when tested on ecologically non-relevant tasks. Moreover, performance in these experiments was not or negatively correlated with an ecologically relevant task, and in none of the tasks did fish caught from a high population density site outperform fish from a low-density site. g is thus unlikely a default result of how brains are designed, and not an automatic consequence of variation in social complexity. Rather, the results may reflect that g requires a minimal brain size, and thus explain the conundrum why the average mammal or bird has a roughly 10× larger brain relative to body size than ectotherms. Ectotherm brains and cognition may therefore be organized in fundamentally different ways compared to endotherms.
Important questions remain about the profile of cognitive impairment in psychotic disorders across adulthood and illness stages. The age-associated profile of familial impairments also remains unclear, as well as the effect of factors, such as symptoms, functioning, and medication. Using cross-sectional data from the EU-GEI and GROUP studies, comprising 8455 participants aged 18 to 65, we examined cognitive functioning across adulthood in patients with psychotic disorders (n = 2883), and their unaffected siblings (n = 2271), compared to controls (n = 3301). An abbreviated WAIS-III measured verbal knowledge, working memory, visuospatial processing, processing speed, and IQ. Patients showed medium to large deficits across all functions (ES range = −0.45 to −0.73, p < 0.001), while siblings showed small deficits on IQ, verbal knowledge, and working memory (ES = −0.14 to −0.33, p < 0.001). Magnitude of impairment was not associated with participant age, such that the size of impairment in older and younger patients did not statistically-significantly differ. However, first-episode patients performed worse than prodromal patients (ES range = −0.88 to −0.60, p < 0.001). Adjusting for cannabis use, symptom severity, and global functioning attenuated impairments in siblings, while deficits in patients remained statistically-significant, albeit reduced by half (ES range = −0.13 to −0.38, p < 0.01). Antipsychotic medication also accounted for around half of the impairment in patients (ES range = −0.21 to −0.43, p < 0.01). Deficits in verbal knowledge, and working memory may specifically index familial, ie. shared genetic and/or shared environmental, liability for psychotic disorders. Nevertheless, potentially modifiable illness-related factors account for a substantial portion of the cognitive impairment in psychotic disorders.
Prior research has indicated that one can summarize the variation in psychopathology measures in a single dimension, labeled P by analogy with the g factor of intelligence. Research shows that this P factor has a weak to moderate negative relationship to intelligence.
We used data from the Vietnam Experience Study to reexamine the relations between psychopathology assessed with the MMPI (Minnesota Multiphasic Personality Inventory) and intelligence (total n = 4,462: 3,654 whites, 525 blacks, 200 Hispanics, and 83 others).
We show that the scoring of the P factor affects the strength of the relationship with intelligence. Specifically, item response theory-based scores correlate more strongly with intelligence than sum-scoring or scale-based scores: r’s = −0.35, −0.31, and −0.25, respectively.
We furthermore show that the factor loadings from these analyses show moderately strong Jensen patterns such that items and scales with stronger loadings on the P factor also correlate more negatively with intelligence (r = −0.51 for 566 items= −0.60 for 14 scales).
Finally, we show that training an elastic net model on the item data allows one to predict intelligence with extremely high precision, r = 0.84. We examined whether these predicted values worked as intended with regards to cross-racial predictive validity, and relations to other variables. We mostly find that they work as intended, but seem slightly less valid for blacks and Hispanics (r’s = 0.85, 0.83, and 0.81, for whites, Hispanics, and blacks, respectively).
[Keywords: Vietnam Experience Study, MMPI, general psychopathology factor, intelligence, cognitive ability, machine learning, elastic net, LASSO, random forest, crud factor]
…To further examine predictive accuracy, we trained a lasso model to see if a relatively sparse model could be obtained. The validity of the lasso model, however, was essentially identical to the elastic net one, and the optimal lasso fit was not very sparse (363 out of 556 items used)…It is seen that about 90 items are needed to reach a correlation accuracy of 0.80, whereas only 3 items are needed to reach 0.50. This may be surprising, but some items have absolute correlations to g of around 0.40, so it is unsurprising that combining 3 of them yields a model accuracy at 0.50.
…Finally, we fit a random forest model. This performed slightly worse than the elastic net (r = 0.78). The failure of the random forest model to do better than the elastic net indicates that nonlinear and interaction effects are not important in a given dataset for the purpose of prediction. In other words, the additive assumption is supported for this dataset and outcome variable
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and resting state fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.
Using two nationally representative datasets, we find large differences between Black and White children in teacher-reported measures of noncognitive skills. We show that teacher reports understate true Black-White skill gaps because of reference bias: teachers appear to rate children relative to others in the same school, and Black students have lower-skilled classmates on average than do White students. We pursue three approaches to addressing these reference biases. Each approach nearly doubles the estimated Black-White gaps in noncognitive skills, to roughly 0.9 standard deviations in third grade.
Power of cognitive ability and social class contrasted.
Large representative sample from longitudinal study, waves 1–3, of 6,216 children
Outcomes were attainments, difficulties and relationships.
Cognitive ability explained large amounts of variance.
Social background only minor effects
The paper examines the effects of socioeconomic background (SES)—measured by social class, family income and parental education—cognitive ability, and gender on a variety of key outcomes from a large longitudinal study based on a representative sample of 13-year-olds.
The data analysed comprised 6,216 children who participated in waves 1 to 3 of the Growing Up in Ireland (GUI) longitudinal survey. The outcome measures drawn from wave 3, when respondents were aged about 17, were: examination results and several cognitive measures, life difficulties, and quality of relationships. 3 regression models were compared with and without, SES measures (occupational class, household income and parental education) and cognitive ability.
On academic and cognitive attainments, cognitive ability at age 13 had substantially more explanatory power than the SES measures together. On measures of adolescent difficulties and on family relationships, cognitive ability was important, but gender and to a lesser extent, household income and parental education had some effects.
Claims that class background and family income are of central importance for adolescent outcomes are not supported.
[Keywords: intelligence, school tests, family background, household income, social class]
Human children show unique cognitive skills for dealing with the social world but their cognitive performance is paralleled by great apes in many tasks dealing with the physical world. Recent studies suggested that members of a songbird family—corvids—also evolved complex cognitive skills but a detailed understanding of the full scope of their cognition was, until now, not existent. Furthermore, relatively little is known about their cognitive development.
Here, we conducted the first systematic, quantitative large-scale assessment of physical and social cognitive performance of common ravens with a special focus on development. To do so, we fine-tuned one of the most comprehensive experimental test-batteries, the Primate Cognition Test Battery (PCTB), to raven features enabling also a direct, quantitative comparison with the cognitive performance of 2 great ape species. Full-blown cognitive skills were already present at the age of 4 months with subadult ravens’ cognitive performance appearing very similar to that of adult apes in tasks of physical (quantities, and causality) and social cognition (social learning, communication, and theory of mind).
These unprecedented findings strengthen recent assessments of ravens’ general intelligence, and aid to the growing evidence that the lack of a specific cortical architecture does not hinder advanced cognitive skills. Difficulties in certain cognitive scales further emphasize the quest to develop comparative test batteries that tap into true species rather than human specific cognitive skills, and suggest that socialization of test individuals may play a crucial role.
We conclude to pay more attention to the impact of personality on cognitive output, and a currently neglected topic in Animal Cognition—the linkage between ontogeny and cognitive performance.
Why do some individuals learn more quickly than others, or perform better in complex cognitive tasks? In this article, we describe how differential and experimental research methods can be used to study intelligence in humans and non-human animals. More than one hundred years ago, Spearman (1904) discovered a general factor underpinning performance across cognitive domains in humans. Shortly thereafter, Thorndike (1935) discovered positive correlations between cognitive performance measures in the albino rat. Today, research continues to shed light on the underpinnings of the positive manifold observed among ability measures.
In this review, we focus on the relationship between cognitive performance and attention control: the domain-general ability to maintain focus on task-relevant information while preventing attentional capture by task-irrelevant thoughts and events. Recent work from our laboratory has revealed that individual differences in attention control can largely explain the positive associations between broad cognitive abilities such as working memory capacity and fluid intelligence. In research on mice, attention control has been closely linked to a general ability factor reflecting route learning and problem solving.
Taken together, both lines of research suggest that individual differences in attention control underpin performance in a variety of complex cognitive tasks, helping to explain why measures of cognitive ability correlate positively. Efforts to find confirmatory and disconfirmatory evidence across species stands to improve not only our understanding of attention control, but cognition in general.
Cognitive training and brain stimulation show promise for ameliorating age-related neurocognitive decline. However, evidence for this is controversial. In a Registered Report, we investigated the effects of these interventions, where 133 older adults were allocated to four groups (left prefrontal cortex anodal transcranial direct current stimulation (tDCS) with decision-making training, and three control groups) and trained over 5 days. They completed a task/questionnaire battery pre-training and post-training, and at 1- and 3-month follow-ups. COMT and BDNF Val/Met polymorphisms were also assessed. Contrary to work in younger adults, there was evidence against tDCS-induced training enhancement on the decision-making task. Moreover, there was evidence against transfer of training gains to untrained tasks or everyday function measures at any post-intervention time points. As indicated by exploratory work, individual differences may have influenced outcomes. But, overall, the current decision-making training and tDCS protocol appears unlikely to lead to benefits for older adults.
The study of intelligence in humans has been ongoing for over 100 years, including the underlying structure, predictive validity, related cognitive measures, and source of differences. One of the key findings in intelligence research is the uniform positive correlations among cognitive tasks. This has been replicated with every cognitive test battery in humans. Nevertheless, many other aspects of intelligence research have revealed contradictory lines of evidence. Recently, cognitive test batteries have been developed for animals to examine similarities to humans in cognitive structure. The results are inconsistent, but there is evidence for some similarities. This article reviews the way intelligence and related cognitive abilities are assessed in humans and animals and suggests a different way of devising test batteries for maximizing between-species comparisons.
Macphail’s “null hypothesis”, that there are no differences in intelligence, qualitative, or quantitative, between non-human vertebrates has been controversial. This controversy can be useful if it encourages interest in acquiring a detailed understanding of how non-human animals express flexible problem-solving capacity (“intelligence”), but limiting the discussion to vertebrates is too arbitrary.
As an example, we focus here on Portia, a spider with an especially intricate predatory strategy and a preference for other spiders as prey. We review research on pre-planned detours, expectancy violation, and a capacity to solve confinement problems where, in each of these 3 contexts, there is experimental evidence of innate cognitive capacities and reliance on internal representation. These cognitive capacities are related to, but not identical to, intelligence.
When discussing intelligence, as when discussing cognition, it is more useful to envisage a continuum instead of something that is simply present or not; in other words, a continuum pertaining to flexible problem-solving capacity for “intelligence” and a continuum pertaining to reliance on internal representation for “cognition.” When envisaging a continuum pertaining to intelligence, Daniel Dennett’s notion of 4 Creatures (Darwinian, Skinnerian, Popperian, and Gregorian) is of interest, with the distinction between Skinnerian and Popperian Creatures being especially relevant when considering Portia. When we consider these distinctions, a case can be made for Portia being a Popperian Creature. Like Skinnerian Creatures, Popperian Creatures express flexible problem solving capacity, but the manner in which this capacity is expressed by Popperian Creatures is more distinctively cognitive.
The literature on the relationship between socioeconomic background (SES) and university education is inconsistent. Some studies conclude SES is important to university entry and course completion, others find trivial SES effects, net of students’ prior performance, and a third group concludes that SES effects are important and policy relevant even when considering prior performance. Parallel arguments apply to demographic, school sector, and institutional differences in the university career, that is, are they unimportant when considering student performance? Using comprehensive and accurate measures of SES and student performance, and a statistical method that utilizes all non-missing data, this study quantifies the effects of socioeconomic, demographic, and institutional factors and prior student performance. SES has only weak effects on university entry and attrition, and no effects on course completion. Student performance has strong effects on entry and has moderate effects on attrition and completion. Demographic other differences mostly disappear when controlling for student performance.
[Keywords: PISA test scores, tertiary entrance performance (ATAR), university participation, university course attrition, university course completion]
The relation between working memory capacity (WMC) and baseline pupil diameter was examined. Participants (n = 341) performed several WMC tasks and baseline pupil diameter was measured in a dark room with a black background screen. The results indicated a weak and non-significant correlation between WMC and baseline pupil diameter consistent with some prior research. A meta-analysis of available studies (k = 26; n = 4356) similarly indicated a weak and non-significant correlation between WMC and baseline pupil diameter. Moderator analyses indicated that the primary moderator responsible for heterogeneity across studies was where the study was conducted. Studies from one laboratory tend to demonstrate a statistically-significant positive correlation, whereas other laboratories have yet to demonstrate the correlation. Broadly, the results suggest that the correlation between WMC and baseline pupil diameter is weak and not particularly robust.
We investigate whether elite Chicago public high schools differentially benefit high-achieving students from more and less affluent neighborhoods. Chicago’s place-based affirmative action policy allocates seats based on achievement and neighborhood socioeconomic status (SES). Using regression discontinuity design (RDD), we find that these schools do not raise test scores overall, but students are generally more positive about their high school experiences. For students from low-SES neighborhoods, we estimate negative effects on grades and the probability of attending a selective college. We present suggestive evidence that these findings for students from low-SES neighborhoods are driven by the negative effect of relative achievement ranking.
Figure 3B: Relationship between the Centered Application Score and Select Outcomes, Tiers 1 and 4.
Genetic quality may be expressed through many traits simultaneously, and this would suggest a phenotype-wide fitness factor. In humans, intelligence has been positively associated with several potential indicators of genetic quality, including ejaculate quality. We conducted a conceptual replication of one such study by investigating the relationship between intelligence (assessed by the Raven Advanced Progressive Matrices Test-Short Form) and ejaculate quality (indexed by sperm count, sperm concentration, and sperm motility) in a sample of 41 men (ages ranging 18 to 33 years; M = 23.33; SD = 3.60). By self-report, participants had not had a vasectomy, and had never sought infertility treatment. We controlled for several covariates known to affect ejaculate quality (eg. abstinence duration before providing an ejaculate) and found no statistically-significant relationship between intelligence and ejaculate quality; our findings, therefore, do not match those of Arden, Gottfredson, Miller et al or those of previous studies. We discuss limitations of this study and the general research area and highlight the need for future research in this area, especially the need for larger data sets to address questions around phenotypic quality and ejaculate quality.
…An important limitation of the current research is the small sample of 41 men, as small sample sizes increase the risk of both Type I and Type II errors. Our analyses, therefore, may have lacked sufficient power to detect the small effect sizes, r = 0.14 to 0.19, reported by Arden, Gottfredson, Miller, and Pierce (2009). Small sample sizes are a recurrent limitation of psychological research investigating ejaculate quality (eg. Baker & Bellis, 1989; Pook et al 2005), perhaps due to difficulties recruiting participants outside a clinical setting. Arden, Gottfredson, Miller, and Pierce analyzed data from a sample of 425 men, which afforded the analyses over 80% power to detect small effects. However, it is important to note that the correlation coefficients we obtained were similar in magnitude to those reported by Arden, Gottfredson, Miller, and Pierce, ranging from −0.18 to 0.30, and the repeated-measures nature of our study gave it greater power despite the small sample size.
The human brain remains active in the absence of explicit tasks and forms networks of correlated activity. Resting-state functional magnetic resonance imaging (rsfMRI) measures brain activity at rest, which has been linked with both cognitive and clinical outcomes. The genetic variants influencing human brain function are largely unknown.
Here we utilized rsfMRI from 44,190 individuals of multiple ancestries (37,339 in the UK Biobank) to discover and validate the common genetic variants influencing intrinsic brain activity.
We identified hundreds of novel genetic loci associated with intrinsic functional signatures (p < 2.8 × 10−11), including associations to the central executive, default mode, and salience networks involved in the triple network model of psychopathology. A number of intrinsic brain activity associated loci colocalized with brain disorder GWAS (eg. Alzheimer’s disease, Parkinson’s disease, schizophrenia) and cognition, such as 19q13.32, 17q21.31, and 2p16.1. Particularly, we detected a colocalization between one (rs429358) of the two variants in the APOE ε4 locus and function of the default mode, central executive, attention, and visual networks. Genetic correlation analysis demonstrated shared genetic influences between brain function and brain structure in the same regions. We also detected statistically-significant genetic correlations with 26 other complex traits, such as ADHD, major depressive disorder, schizophrenia, intelligence, education, sleep, subjective well-being, and neuroticism.
Common variants associated with intrinsic brain activity were enriched within regulatory element in brain tissues.
Objective: Deleterious copy number variants (CNVs) are identified in up to 20% of individuals with autism. However, levels of autism risk conferred by most rare CNVs remain unknown. The authors recently developed statistical models to estimate the effect size on IQ of all CNVs, including undocumented ones. In this study, the authors extended this model to autism susceptibility.
Methods: The authors identified CNVs in two autism populations (Simons Simplex Collection and MSSNG) and two unselected populations (IMAGEN and Saguenay Youth Study). Statistical models were used to test nine quantitative variables associated with genes encompassed in CNVs to explain their effects on IQ, autism susceptibility, and behavioral domains.
Results: The “probability of being loss-of-function intolerant” (pLI) best explains the effect of CNVs on IQ and autism risk. Deleting 1 point of pLI decreases IQ by 2.6 points in autism and unselected populations. The effect of duplications on IQ is threefold smaller. Autism susceptibility increases when deleting or duplicating any point of pLI. This is true for individuals with high or low IQ and after removing de novo and known recurrent neuropsychiatric CNVs. When CNV effects on IQ are accounted for, autism susceptibility remains mostly unchanged for duplications but decreases for deletions. Model estimates for autism risk overlap with previously published observations. Deletions and duplications differentially affect social communication, behavior, and phonological memory, whereas both equally affect motor skills.
Conclusions: Autism risk conferred by duplications is less influenced by IQ compared with deletions. The model applied in this study, trained on CNVs encompassing >4,500 genes, suggests highly polygenic properties of gene dosage with respect to autism risk and IQ loss. These models will help to interpret CNVs identified in the clinic.
It has been known since 1904 that, in humans, diverse cognitive traits are positively intercorrelated. This forms the basis for the general factor of intelligence (g). Here, we directly test whether there is a partial genetic basis for individual differences in g using data from seven different cognitive tests (n = 11,263–331,679) and genome-wide autosomal single-nucleotide polymorphisms. A genetic g factor accounts for an average of 58.4% (s.e. = 4.8%) of the genetic variance in the cognitive traits considered, with the proportion varying widely across traits (range, 9–95%).
We distill genetic loci that are broadly relevant for many cognitive traits (g) from loci associated specifically with individual cognitive traits. These results contribute to elucidating the aetiology of a long-known yet poorly understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits.
Executive functions cannot be reduced to intellectual abilities.
Certain part of the genetic variance is common to these 2 phenomena.
Some aspects of executive control have higher heritability than others.
Executive functions include a series of specific abilities, with specific genetic foundations.
The first aim of this study was to explore the aetiology of phenotypic relationships between different measures of executive functions. The second objective was to examine sources of the covariation between different measures of executive functions and the measure of general cognitive ability.
The study sample consisted of 468 twins (154 pairs of monozygotic twins and 80 pairs of dizygotic twins) of the same and different gender who grew up together. Executive functions were evaluated by the Wisconsin Card Sorting Test, the Trail Making Test—form B, and verbal fluency tests. Raven’s Advanced Progressive Matrices were used as a measure of general cognitive ability.
The study results suggest a primarily genetic origin of the mutual covariation of different executive measures and their covariation with the general cognitive ability construct. While the shared genetic variance primarily lies in the bases of similarity/unity of the used cognitive measures, their particularity/difference is determined by a specific unshared environment.
The obtained result on the presence of a single general genetic factor, which can be singled out in the case of different executive measures, at least partially speaks in favor of the thesis about the unity of various executive measures and the existence of a common basic ability. Together with the specific unshared environment, the specific genetic influence speaks in favor of a difference between each of the individual measures.
[Keywords: behavioral genetics, cognitive abilities, executive functions, general cognitive ability]
Objective: This study examined how the lower cognitive skills in children who consumed iron-fortified formula in infancy relate to outcomes in young adulthood.
Methods: Participants were 443 Chilean young adults (Mage = 21.2y, 55% female) who took part in a randomized controlled iron-deficiency anemia preventive trial during infancy (6–12 m). Slightly over half of participants (n = 237) received iron-fortified formula (12.7 mg/L) and 206 received a low-iron formula (2.3 mg/L). Spatial memory, IQ, and visual-motor integration were measured at age 10, and neurocognition, emotion regulation, educational level, and attainment of adult developmental milestones were assessed at age 21.
Results: Consumption of iron-fortified formula in infancy was associated with poorer performance on neurocognitive tests in childhood, and these effects related to poorer neurocognitive, emotional, and educational outcomes in young adulthood. Dosage effects associated with consumption of iron-fortified formula were found for lower educational attainment and, marginally, slower mental processing. Those who received iron-fortified formula and had low age 10 cognitive abilities performed most poorly on neurocognitive tests at age 21.
Conclusion: Findings suggest that the long-term development of infants who consume iron-fortified formula may be adversely affected.
Clinical Trials number: NCT01166451.
[Keywords: Iron supplementation, neurocognition, emotion regulation, memory, executive function, Chile]
Terman’s study was the first to systematically document the lives of the intellectually gifted. This cross-sectional study replicates and extends some of Terman’s findings on characteristics of the gifted in childhood, comparing largely unselected samples of gifted (n = 50) and average-ability (n = 50) adolescents matched by means of propensity score matching. Students were compared on their school performance (standardized math and reading tests and grades), motivation (math ability self-concept, intrinsic motivation, vocational interests, and educational aspirations), parental educational expectations, students’ evaluation of school instruction (perceived quality and pressure), and subjective well-being. The gifted scored higher on math performance (rank-biserial r = 0.66/0.81), math ability self-concept (0.71), intrinsic motivation (0.62), and investigative vocational interests (0.65). Some smaller differences were found for realistic (0.42) and social interests (−0.37) and for pressure in math lessons (−0.52). Results support Terman’s findings on gifted individuals’ psychological functioning and contradict negative stereotypes about the gifted.
We compare the relative contribution of grit and intelligence to educational and job-market success in a representative sample of the American population. We find that, in terms of Δ R 2, intelligence contributes 48–90× more than grit to educational success and 13× more to job-market success. Conscientiousness also contributes to success more than grit but only twice as much. We show that the reason our results differ from those of previous studies which showed that grit has a stronger effect on success is that these previous studies used nonrepresentative samples that were range restricted on intelligence. Our findings suggest that although grit has some effect on success, it is negligible compared to intelligence and perhaps also to other traditional predictors of success.
Student sex can often be predicted based on a set of achievement and attitude data.
Student sex can often be predicted based on classification models from other countries.
Universal patterns in academic sex differences are larger than hitherto thought.
Academic sex differences are stronger in societies with more socioeconomic equality.
The extent of sex differences in psychological traits is vigorously debated. We show that the overall sex difference in the pattern of adolescents’ achievement and academic attitudes is relatively large and similar across countries. We used a binomial regression modeling approach to predict the sex of 15 and 16 year olds based on sets of academic ability and attitude variables in three cycles of the Programme for International Student Assessment (PISA) data (n = 969,673 across 55 to 71 countries and regions). We found that the sex of students in any country can be reliably predicted based on regression models created from the data of all other countries, indicating a common (universal) sex-specific component. Averaged over three different PISA cycles (2009, 2012, 2015), the sex of 69% of students can be correctly classified using this approach, corresponding to a large effect. Moreover, the universal component of these sex differences is stronger in countries with relative income equality and women’s participation in the labor force and politics. We conclude that patterns in academic sex differences are larger than hitherto thought and appear to become stronger when societies have more socioeconomic equality. We explore reasons why this may be the case and possible implications.
We study the impact of lead exposure from birth to adulthood and provide evidence on the mechanisms producing these effects. Following 800,000 children differentially exposed to the phaseout of leaded gasoline in Sweden, we find that even a low exposure affects long-run outcomes, that boys are more affected, and that changes in noncognitive skills explain a sizeable share of the impact on crime and human capital. The effects are greater above exposure thresholds still relevant for the general population, and reductions in exposure equivalent to the magnitude of the recent redefinition of elevated blood lead levels can increase earnings by 4%.
Gender differences in mathematics are largely explained by a regional gradient in Italy.
Gender differences in reading are not influenced by a regional gradient and are stable across Italy.
A number of factors, could influence the gender gap in mathematics.
Whether males outperform females in mathematics is still debated. Such a gender gap varies across countries, but the determinants of the differences are unclear and could be produced by heterogeneity in the instructional systems or cultures and may vary across school grades. To clarify this issue, we took advantage of the INVALSI dataset, that offered over 13 million observations covering one single instructional system (ie. the Italian system) in grades 2, 5, and 8, in the period 2010–2018. Results showed that males outperformed females in mathematics (and vice versa in reading), with gaps widening from the 2nd through to the 8th grade. The gender gap in mathematics was larger in the richer northern Italian regions (also characterized by greater gender equality) than in southern regions. This was not explained by average performance or fully accounted for by economic factors. No such north-south difference of the gap emerged in reading. Results are discussed with reference to the literature showing that the gender gap varies across world regions.
In a meta-analysis, we determine the average correlation among broad intelligences.
Based on model type, the average correlation was between r = 0.58 and r = 0.65.
We conduct factor analyses on a composite correlation matrix of broad intelligences.
Our results indicate the degree and nature of relations among broad intelligences.
The broad intelligences include a group of mental abilities such as comprehension knowledge, quantitative reasoning, and visuospatial processing that are relatively specific in their focus and fall at the second stratum of the Cattell-Horn-Carroll (CHC) model of intelligence. In recent years, the field has seen a proliferation of mental abilities being considered for inclusion among the broad intelligences, which poses challenges in terms of their effective and efficient assessment. We conducted a meta-analysis of 61 articles that reported correlations among the broad intelligences. Results indicated that the average correlation among broad intelligences fell between r = 0.58, 95% CI [0.53, 0.64], and r = 0.65, 95% CI [0.62, 0.68], depending upon the model employed to estimate the relations. Applying factor analysis to a composite correlation matrix drawn from the studies, we obtained dimensions of broad intelligence that may be useful to organizing the group. Finally, we discuss the implications of the correlations among broad intelligences as an evaluative tool for candidate intelligences.
General cognitive ability (g) does not explain sex differences in academic test performance by the end of compulsory education. Instead, individual differences in specific reasoning abilities, after removing the effects of g, may contribute to the observed gender gaps. Associations between general or specific cognitive abilities, sex, and educational attainments were analysed in a cross-sectional study of 11-year-olds (M = 133.5 months, SD = 3.5), at an age before substantive gender-related selection-bias occurred. The 178,599 pupils (89,545 girls and 89,054 boys) attending English state schools represented 93% of the UK’s local education authorities. In 2004 each student completed the Cognitive Abilities Test—Third Edition (CAT3), assessing verbal, quantitative, and nonverbal reasoning abilities. These data were linked to each child’s attainment scores on national Key Stage 2 tests in English, mathematics and science. A sex difference in g, favoring girls, was statistically-significant but of negligible effect size (Cohen’s d = 0.01). Girls scored 26% of a SD higher than boys on a verbal residual factor, and boys scored 28% of a SD higher than girls on a quantitative residual factor, with negligible sex differences on a nonverbal residual factor (1% of a SD). In education, 10% more girls than boys achieved UK government targets in English. In mathematics and science, sex differences were less apparent at the government target grade (Level 4), although a 5% greater proportion of boys than girls performed at the highest level in mathematics (Level 5). General cognitive ability (g) was strongly related to an educational factor score (r = 0.83) as expected, and did not explain sex differences in academic performance. In general linear models, a verbal residual factor explained up to 29% of girls’ higher English attainment, and better quantitative skills among boys explained 50% of their higher attainment in mathematics. Besides the substantial contributions of specific cognitive abilities to gender differences in English and mathematics, there remains substantive variance of the educational gender gap left to explain.
We investigated intergenerational educational and occupational mobility in a sample of 2,594 adult offspring and 2,530 of their parents. Participants completed assessments of general cognitive ability and five noncognitive factors related to social achievement; 88% were also genotyped, allowing computation of educational-attainment polygenic scores. Most offspring were socially mobile. Offspring who scored at least 1 standard deviation higher than their parents on both cognitive and noncognitive measures rarely moved down and frequently moved up. Polygenic scores were also associated with social mobility. Inheritance of a favorable subset of parent alleles was associated with moving up, and inheritance of an unfavorable subset was associated with moving down. Parents’ education did not moderate the association of offspring’s skill with mobility, suggesting that low-skilled offspring from advantaged homes were not protected from downward mobility. These data suggest that cognitive and noncognitive skills as well as genetic factors contribute to the reordering of social standing that takes place across generations.
Individual differences in cognitive control have been suggested to act as a domain-general bottleneck constraining performance in a variety of cognitive ability measures, including but not limited to fluid intelligence, working memory capacity, and processing speed. However, owing to psychometric problems associated with the measurement of individual differences in cognitive control, it has been challenging to empirically test the assumption that individual differences in cognitive control underlie individual differences in cognitive abilities.
In the present study, we addressed these issues by analyzing the chronometry of intelligence-related differences in midfrontal global theta connectivity, which has been shown to reflect cognitive control functions. We demonstrate in a sample of 98 adults, who completed a cognitive control task while their electroencephalogram was recorded, that individual differences in midfrontal global theta connectivity during stages of higher-order information-processing explained 65% of the variance in fluid intelligence. In comparison, task-evoked theta connectivity during earlier stages of information processing was not related to fluid intelligence. These results suggest that more intelligent individuals benefit from an adaptive modulation of theta-band synchronization during the time-course of information processing. Moreover, they emphasize the role of interregional goal-directed information-processing for cognitive control processes in human intelligence and support theoretical accounts of intelligence, which propose that individual differences in cognitive control processes give rise to individual differences in cognitive abilities.
There are no undebated definitions of “creativity”, and any definition will reflect how this rich topic is treated. Nearly 20 years ago I discussed how behavior analysis might contribute—or not—to an understanding of creativity. I revisit this topic, expanding on some issues and reconsidering others. As before, my focus is on scientific and mathematical accomplishments, which, though tied closely to Weisberg’s placement of creative achievements in the domains of problem posing and problem solving, places emphasis on the extraordinary and productive giftedness of certain individuals. From the massive empirical, theoretical, and historical literature at least three essential and dynamically interlocking dimensions of their creative achievements emerge: talent, expertise, and motivation. I emphasize “interlocking” because the productive expression of each of these elements depends on the others. The role of behavior analysis in these elements is modest at best. It has nothing to say about talent—and even in some cases might deny its role altogether. As for expertise, with some notable exceptions, behavior analysis has had little to say about the acquisition of truly complex performances; this has been left to other fields. As for motivation, one must go well beyond naïve “pleasure and pain” accounts to more elusive, yet more powerful behavior-consequence relations. Many challenges to understanding remain for all behavioral scientists.
Intelligence quotient (IQ) is a common measure of intelligence that associates with many important life outcomes. Research over several decades has indicated that the average IQ test score among Black Americans is lower than the average IQ test score among White Americans, but in weighted results from a national nonprobability survey, only about 41% of US adults indicated awareness of this IQ gap. Results from a follow-up convenience survey indicated that, in the aggregate, White participants’ rating of White Americans’ average IQ and average intelligence is higher than Blacks Americans’ average IQ test score and average intelligence and was not driven by White participants’ belief in an universal White intellectual superiority. These and other results could have implications regarding the US public’s perceptions about the reasons for Black/White inequality and implications for the use of intelligence stereotype scales as measures of racial prejudice.
With just one exception, all of the volumes in Terman’s Genetic Studies of Genius report the results of a longitudinal study of more than a thousand intellectually gifted children. That single exception is Volume II, Cox’s single-authored The Early Mental Traits of Three Hundred Geniuses, which instead was a retrospective study of 301 eminent creators and leaders, using historiometric methods to estimate their IQs (as well as to assess a subset of 100 on 67 character traits). This article discusses how this volume actually fits with the other four volumes in the set. After giving the historical background, discussion turns to the emergence of Cox’s doctoral dissertation. Then comes a narrative of the aftermath, including subsequent contributions by Cox, Terman, and numerous other researchers extending into the 21st century. The article closes by treating the ways that the intellectually gifted and the historic geniuses are not comparable, thus indicating the need for more recent replications and extensions of her work.
[Keywords: archival, biographical, historical analysis, early childhood, gifted, intelligence]
Several previous studies reported relationships between speed of information processing as measured with the drift parameter of the diffusion model (Ratcliff, 1978) and general intelligence. Most of these studies utilized only few tasks and none of them used more complex tasks. In contrast, our study (n = 125) was based on a large battery of 18 different response time tasks that varied both in content (numeric, figural, and verbal) and complexity (fast tasks with mean RTs of ca. 600 ms vs. more complex tasks with mean RTs of ca. 3,000 ms). Structural equation models indicated a strong relationship between a domain-general drift factor and general intelligence. Beyond that, domain-specific speed of information processing factors were closely related to the respective domain scores of the intelligence test. Furthermore, speed of information processing in the more complex tasks explained additional variance in general intelligence. In addition to these theoretically relevant findings, our study also makes methodological contributions showing that there are meaningful interindividual differences in content specific drift rates and that not only fast tasks, but also more complex tasks can be modeled with the diffusion model.
Evolutionary rates of G vs. neuroanatomical proxies are compared.
G exhibits greater evolutionary lability.
This suggests different selection regimes.
cerebellar volume residualised against body size is the best proxy.
Various neuroanatomical volume measures (NVMs) are frequently used as proxies for intelligence in comparative studies, such as the size of the brain, neocortex, and hippocampus, either absolute or controlled for other size measures (eg. body size, or rest of the brain). Mean species NVMs are moderately correlated with aggregate general intelligence (G), however G and NVMs are yet to be compared in their evolutionary patterns (eg. conservatism and evolutionary rates) and processes (ie. their fit to diverse models of evolution reflecting selection regimes).
Such evolutionary information is valuable for examining convergence in the evolutionary history among traits and is not available from simple correlation coefficients. Considering accumulating evidence that non-volumetric neurological measures may be as important as (or more so than) volumetric measures as substrates of intelligence, and that certain NVMs negatively predict neuronal density, we hypothesized that discrepancies would be found in evolutionary patterns and processes of G compared to NVMs.
We collated data from the literature on primate species means for G, the volumes of the brain, neocortex, cerebellum, and hippocampus, and body mass, and employed phylogenetic comparative methods that examine phylogenetic signal (λ, K), evolutionary rates (σ2), and several parameters of evolutionary models (Brownian motion, Early-burst, acceleration, and Ornstein-Uhlenbeck).
Evolutionary rates and acceleration trends were up to an order of magnitude higher for G than for most NVMs, and a strong selection optimum toward which clades evolved was found for G, whereas NVMs conformed mostly to Brownian motion. Brain size was the most contrasting NVM compared to intelligence across most phylogenetic indices examined, showing signs of deceleration and extreme conservativeness. Only certain operationalizations of neocortical and hippocampal volume showed convergence with G, albeit still notably weakly. The NVM with results that most strongly approached the patterns identified for G is residual cerebellar size (relative to body size).
In comparison to the most commonly used volumetric measures (operationalization of brain and neocortex size), G must be seen as an evolutionarily labile trait under considerable selection pressure, necessitating that the role of the cerebellum be more aptly recognized and that other neurological factors be invoked as potential substrates for its evolutionary trajectory.
[Keywords: general intelligence, phylogenetic comparative methods, cerebellum, brain size, neocortex]
Conventional tests of the Dunning-Kruger hypothesis are shown to be confounded.
The Glejser test is argued to be a valid test of the Dunning-Kruger hypothesis.
Nonlinear regression is argued to be a valid test of the Dunning-Kruger hypothesis.
Failed to identify the Dunning-Kruger effect with IQ data and both valid tests.
The Dunning-Kruger hypothesis states that the degree to which people can estimate their ability accurately depends, in part, upon possessing the ability in question. Consequently, people with lower levels of the ability tend to self-assess their ability less well than people who have relatively higher levels of the ability. The most common method used to test the Dunning-Kruger hypothesis involves plotting the self-assessed and objectively assessed means across four categories (quartiles) of objective ability. However, this method has been argued to be confounded by the better-than-average effect and regression toward the mean. In this investigation, it is argued that the Dunning-Kruger hypothesis can be tested validly with two inferential statistical techniques: the Glejser test of heteroscedasticity and nonlinear (quadratic) regression. On the basis of a sample of 929 general community participants who completed a self-assessment of intelligence and the Advanced Raven’s Progressive Matrices, we failed to identify statistically-significant heteroscedasticity, contrary to the Dunning-Kruger hypothesis. Additionally, the association between objectively measured intelligence and self-assessed intelligence was found to be essentially entirely linear, again, contrary to the Dunning-Kruger hypothesis. It is concluded that, although the phenomenon described by the Dunning-Kruger hypothesis may be to some degree plausible for some skills, the magnitude of the effect may be much smaller than reported previously.
Research in educational psychology consistently finds a relationship between intelligence and academic performance. However, in recent decades, educational fields, including gifted education, have resisted intelligence research, and there are some experts who argue that intelligence tests should not be used in identifying giftedness. Hoping to better understand this resistance to intelligence research, we created a survey of beliefs about intelligence and administered it online to a sample of the general public and a sample of teachers. We found that there are conflicts between currently accepted intelligence theory and beliefs from the American public and teachers, which has important consequences on gifted education, educational policy, and the effectiveness of interventions.
On a planet experiencing global environmental change, the governance of natural resources depends on sustained collective action by diverse populations. Engaging in such collective action can only build upon the foundation of human cognition in social-ecological settings. To help understand this foundation, we assess the effect of cognitive abilities on the management of a common pool resource. We present evidence that two functionally distinct cognitive abilities, general and social intelligence, improve the ability of groups to manage a common pool resource. Groups high in both forms of intelligence engage in more effective collective action that is also more consistent, despite social or ecological change. This result provides a foundation for integrating the effects of cognitive abilities with other dimensions of cognitive diversity to explain when groups will and will not sustainably govern natural resources.
The human cerebral cortex is important for cognition, and it is of interest to see how genetic variants affect its structure. Grasby et al 2020 combined genetic data with brain magnetic resonance imaging from more than 50,000 people to generate a genome-wide analysis of how human genetic variation influences human cortical surface area and thickness. From this analysis, they identified variants associated with cortical structure, some of which affect signaling and gene expression. They observed overlap between genetic loci affecting cortical structure, brain development, and neuropsychiatric disease, and the correlation between these phenotypes is of interest for further study.
Introduction: The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure.
Rationale: To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations.
Results: We identified 306 nominally genome-wide statistically-significant loci (p < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained statistically-significant after replication, with 199 loci passing multiple testing correction (p < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness).
Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rg = −0.32, SE = 0.05, p = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness.
To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity.
We observed statistically-significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism.
Conclusion: This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function.
Foreign language learning in older age has been proposed as a promising avenue for combatting age-related cognitive decline. We tested this hypothesis in a randomized controlled study in a sample of 160 healthy older participants (aged 65–75 years) who were randomized to 11 weeks of either language learning or relaxation training. Participants in the language learning condition obtained some basic knowledge in the new language (Italian), but between-groups differences in improvements on latent factors of verbal intelligence, spatial intelligence, working memory, item memory, or associative memory were negligible. We argue that this is not due to either poor measurement, low course intensity, or low statistical power, but that basic studies in foreign languages in older age are likely to have no or trivially small effects on cognitive abilities. We place this in the context of the cognitive training and engagement literature and conclude that while foreign language learning may expand the behavioral repertoire, it does little to improve cognitive processing abilities.
Religious belief is a topic of longstanding interest to psychological science, but the psychology of religious disbelief is a relative newcomer. One prominently discussed model is analytic atheism, wherein analytic thinking overrides religious intuitions and instruction. Consistent with this model, performance-based measures of reliance on analytic thinking predict religious disbelief in WEIRD (Western, Educated, Industrialized, Rich, & Democratic) samples. However, the generality of analytic atheism remains unknown.
Drawing on a large global sample (n = 3,461) from 13 religiously, demographically, and culturally diverse societies, we find that analytic atheism is in fact quite fickle cross-culturally, only appearing robustly in aggregate analyses and in 3 individual countries.
Such complexity implies a need to revise simplistic theories of religious disbelief as primarily grounded in cognitive style. The results provide additional evidence for culture’s effects on core beliefs, highlighting the power of comparative cultural evidence to clarify core mechanisms of human psychological variation.
A two-year old rat, R222, survived a life-time of extreme hydrocephaly affecting the size and organization of its brain. Much of the cortex was severely thinned and replaced by cerebrospinal fluid, yet R222 had normal motor function, could hear, see, smell, and respond to tactile stimulation. The hippocampus was malformed and compressed into the lower hindbrain together with the hypothalamus midbrain and pons, yet R222 showed normal spatial memory as compared to age-matched controls. BOLD MRI was used to study the reorganization of R222’s brain function showing global activation to visual, olfactory and tactile stimulation, particularly in the brainstem/cerebellum. The results are discussed in the context of neuroadaptation in the face of severe hydrocephaly and subsequent tissue loss, with an emphasis on what is the “bare minimum” for survival.
The lottery ticket hypothesis, initially proposed by researchers Jonathan Frankle and Michael Carbin at MIT, suggests that by training deep neural networks (DNNs) from “lucky” initializations, often referred to as “winning lottery tickets”, we can train networks which are 10–100× smaller with minimal losses—or even while achieving gains—in performance. This work has exciting implications for potentially finding ways to not only train with fewer resources, but also run faster inference of models on smaller devices, like smartphones and VR headsets. But the lottery ticket hypothesis is not yet fully understood by the AI community. In particular, it has remained unclear whether winning tickets are dependent on specific factors or rather represent an intrinsic feature of DNNs.
New research from Facebook AI finds the first definitive evidence that lottery tickets generalize across related, but distinct datasets and can extend to reinforcement learning (RL) and natural language processing (NLP). We’re sharing details on the results of our experiments using winning tickets, and we’re also introducing a new theoretical framework on the formation of lottery tickets to help researchers advance toward a better understanding of lucky initializations.
…there are many more open questions about the underlying properties and behaviors of neural networks, such as how do these winning tickets form, why do they exist, and how do they work?
To begin to analyze these questions in the context of deep ReLU networks, we used a student-teacher setting, in which a larger student network must learn to mimic exactly what the smaller teacher is doing. Since we can define the teacher network with fixed parameters in this setting, we can quantitatively measure the student network’s learning progress, and, critical to our investigation of lottery tickets, how the student network’s initialization affects the learning process.
In the student-teacher setting, we see that after training, the activity patterns of select student neurons correlate more strongly with those of teacher neurons than with the activity of other student neurons—a concept that is referred to as “student specialization.” This stronger correlation suggests that, during training, the student network not only learns the teacher’s network output but also the internal structure of the teacher by mimicking individual teacher neurons.
In our analysis, we show this occurrence happens locally in a 2-layer ReLU network: if the initial weights of a student neuron happen to be similar to those of some teacher neurons, then specialization will follow. The size of the neural network is important because the larger the student network, the more likely that one of the student neurons will start out close enough to a teacher neuron to learn to mimic its activity during training. What’s more, if a student neuron’s initial activation region has a more substantial overlap with a teacher neuron, then that student neuron specializes faster. This behavior corroborates the lottery ticket hypothesis, which similarly proposes that some lucky subset of initializations exist within neural networks, and “winning tickets” are the lucky student neurons that happen to be in the right location at the beginning of training. In our follow-up research, we strengthen our results by removing many mathematical assumptions, including independent activations and locality, and still prove that student specialization happens in the lowest layer in deep ReLU networks after training. From our analysis, we find certain mathematical properties in the training dynamics resonate with the lottery ticket phenomenon: those weights with a slight advantage in the initialization may have a greater chance of being the winning tickets after training converges.
Her son, Henry, endured hundreds of seizures a day. Despite receiving high doses of medication, his little body seemed like a rag doll as one episode blended into another. He required several surgeries, starting when he was 3 1⁄2 months old, eventually leading to a complete anatomical hemispherectomy, or the removal of half of his brain, when he turned 3. The procedure was first developed in the 1920s to treat malignant brain tumors. But its success in children who have brain malformations, intractable seizures or diseases where damage is confined to half the brain, has astonished even seasoned scientists. After the procedure, many of the children are able to walk, talk, read and do everyday tasks. Roughly 20% of patients who have the procedure go on to find gainful employment as adults.
Now, research published Tuesday in the journal Cell Reports suggests that some individuals recover so well from the surgery because of a reorganization in the remaining half of the brain. Scientists identified the variety of networks that pick up the slack for the removed tissue, with some of the brain’s specialists learning to operate like generalists. “The brain is remarkably plastic”, said Dorit Kliemann, a cognitive neuroscientist at the California Institute of Technology, and the first author of the study. “It can compensate for dramatic loss of brain structure, and in some cases the remaining networks can support almost typical cognition.”
…Instead, researchers found that while the type of connections remained the same in the individuals with just one hemisphere, different regions responsible for processing sensorimotor information, vision, attention and social cues strengthened existing connections, communicating more frequently with each other compared with ordinary brains. It was almost as if parts of the brain that may have normally been specialized, say, as trumpet players, had talked to the rest of the band and taken additional responsibilities to play percussion instruments as well, Dr. Behrmann said. “Their brain networks seem to be multitasking.”
The results are encouraging for researchers and families trying to understand how the brain adapts and functions after a hemispherectomy. “I think there’s more and more evidence to suggest that brain plasticity is a really long-lasting phenomena”, said Dr. Ajay Gupta, a pediatric neurologist at the Cleveland Clinic, who has followed nearly 200 children after the surgery. Until recently, the scientific consensus has been that hemispherectomy surgery is best performed at a very young age, before a child reaches the age of 4 or 5. That way, they can regain normal function as they grow older. While neuroplasticity is stronger in early childhood, the new study suggests that surgery should not be withheld after an arbitrary end date, Dr. Gupta said. Adults in the study had undergone hemispherectomy surgery at ages ranging from 3 months to 11 years old.
A factor that may play a more important role in patient outcomes is the age at which seizures begin to occur. The surgery is still considered a last resort after medical treatment. But if the duration of seizures and resulting brain damage can be limited, patients may recover more function. “The other hemisphere is already having to handle extra responsibilities before patients get treated”, said Lynn K. Paul, a neuroscientist at California Institute of Technology and a co-author of the study. “It continues to do so when you take out the damaged hemisphere. So what we really want is to protect the hemisphere that’s working.”
…After the operation, children become substantially weaker in their hands and arms on the side opposite the operation. Their vision becomes blocked on that side, and they may also lose some ability to recognize where sounds are coming from. “There are some things that definitely require a higher level of rehab and learning. For example, reading and writing and math”, Dr. Gupta said. In many cases, however, those skills have already been compromised by the underlying diseases…For now, she is happy that her son can walk independently, communicate with an iPad and eat meals without a feeding tube.
[Summary of investigation into David Rosenhan: like the Robbers Cave or Stanford Prison Experiment, his famous fake-insane patients experiment cannot be verified and many troubling anomalies have come to light. Cahalan is unable to find almost all of the supposed participants, Rosenhan hid his own participation & his own medical records show he fabricated details of his case, he throw out participant data that didn’t match his narrative, reported numbers are inconsistent, Rosenhan abandoned a lucrative book deal about it and avoided further psychiatric research, and showed some character traits of a fabulist eager to please.]
Numerous studies have found a negative relationship between religiousness and IQ. It is in the region of −0.2, according to meta-analyses. The reasons for this relationship are, however, unknown. It has been suggested that higher intelligence leads to greater attraction to science, or that it helps to override evolved cognitive dispositions such as for religiousness. Either way, such explanations assume that the religion-IQ nexus is on general intelligence (g), rather than some subset of specialized cognitive abilities. In other words, they assume it is a Jensen effect.
Two large datasets comparing groups with different levels of religiousness show that their IQ differences are not on g and must, therefore, be attributed to specialized abilities [but see Dutton et al 201 which finds the opposite, using much stronger IQ testing]. An analysis of the specialized abilities on which the religious and non-religious groups differ reveals no clear pattern.
We cautiously suggest that this may be explicable in terms of autism spectrum disorder traits among people with high IQ scores, because such traits are negatively associated with religiousness.
We conduct field experiments on time preferences with children ages 3–12.
Time preferences evolve statistically-significantly during this period, with older children displaying more patience.
Neither assignment to early schooling or parent preferences can explain child time preferences.
Interestingly, we observe that black children are more impatient than white or Hispanic children.
Time preferences have been correlated with a range of life outcomes, yet little is known about their early development. We conduct a field experiment to elicit time preferences of over 1200 children ages 3–12, who make several intertemporal decisions. To shed light on how such primitives form, we explore various channels that might affect time preferences, from background characteristics to the causal impact of an early schooling program that we developed and operated. Our results suggest that time preferences evolve substantially during this period, with younger children displaying more impatience than older children. We also find a strong association with race: black children, relative to white or Hispanic children, are more impatient. Finally, assignment to different schooling opportunities is not statistically-significantly associated with child time preferences.
[Keywords: Time preferences, Child behavior, Experiment, Inter-generational transmission]
We used a large sample from UK Biobank (N = 29,004, age range = 44–81 years).
The association between brain volume and intelligence (‘g’) was r = 0.276.
Multiple global tissue measures explained twice the g variance in older than middle age.
The size of the association between g and global brain measures did not vary by sex.
We investigate the regional cortical, subcortical and white matter correlates of g.
The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies.
Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants’ age range was 44–81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age-corrected and sex-corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macrostructure and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5.4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.
Studies of expertise have traditionally had a strong focus on the role of one single factor, i.e. long-term deliberate practice, for expert performance. However, recent empirical and theoretical work strongly suggests that expertise is a function of many variables that may have practice-independent effects on performance, but also moderate the efficacy of practice itself.
Here we study such interaction effects in a large cohort (n > 4,500) of Swedish twins, using music as a model domain, and measured expert performance (musical auditory discrimination) as well as self-reported real-life achievement as indices of expertise. Specifically, we test 2 recently proposed hypotheses, i.e. 1. that the efficacy of practice increases if the individual also takes part in teacher-led lessons, and 2. that practice efficacy increases with higher intelligence.
The results did not support the first hypothesis. Both practice and frequency of music lessons had positive associations with the 2 measures of expertise but, contrary to predictions, the interaction between them was negative, i.e. the effect of each practiced hour decreased with more lessons. In contrast, the second hypothesis was supported by the data, i.e. we found a positive interaction between practice and intelligence, suggesting that higher cognitive ability is related to more efficient practice behaviors.
Together the results further support that domain-specific expertise is a complex outcome, which depends on an interplay of a variety of factors.
The ability of parasites to manipulate host behavior to their advantage has been studied extensively, but the impact of parasite manipulation on the evolution of neural and endocrine mechanisms has remained virtually unexplored. If selection for countermeasures has shaped the evolution of nervous systems, many aspects of neural functioning are likely to remain poorly understood until parasites—the brain’s invisible designers—are included in the picture.
This article offers the first systematic discussion of brain evolution in light of parasite manipulation. After reviewing the strategies and mechanisms employed by parasites, the paper presents a taxonomy of host countermeasures with four main categories, namely: restrict access to the brain; increase the costs of manipulation; increase the complexity of signals; and increase robustness. For each category, possible examples of countermeasures are explored, and the likely evolutionary responses by parasites are considered.
The article then discusses the metabolic, computational, and ecological constraints that limit the evolution of countermeasures. The final sections offer suggestions for future research and consider some implications for basic neuroscience and psychopharmacology.
The paper aims to present a novel perspective on brain evolution, chart a provisional way forward, and stimulate research across the relevant disciplines.
Although the accomplishments of the 1,528 subjects of the Genetic Studies of Genius are impressive, they do not represent the pinnacle of human achievement. Since the early 1990s, commentators (eg. Bond, 2014; Gladwell, 2006; Heilman, 2016; Shurkin, 1992) have drawn attention to the fact that two future Nobelists—William Shockley and Luis Alvarez—were among the 168,000 candidates screened for the study; but they were rejected because their IQ scores were too low. Critics see this as a flaw of Terman’s methodology and/or intelligence testing. However, events with a low base rate (such as winning a Nobel prize) are difficult to predict (Taylor & Russell 1939).
This study simulates the Terman’s sampling procedure to estimate the probability that Terman’s sampling procedure would have selected one or both future Nobelists from a population of 168,000 candidates. Using data simulations, we created a model that realistically reflected the test-retest and split-half reliability of the IQ scores used to select individuals for the Genetic Studies of Genius and the relationship between IQ and Nobelist status.
Results showed that it was unlikely for Terman to identify children who would later earn Nobel prizes, mostly due to the low base rates of such high future achievement and the high minimum IQ needed to be selected for Terman’s study.
Changes to the methodology that would have been required to select one or both Nobelists for the longitudinal study were not practical. Therefore, Alvarez’s and Shockley’s absence from the Genetic Studies of Genius sample does not invalidate intelligence testing or Terman’s landmark study.
The relative importance of different factors in the development of human skills has been extensively discussed. Research on expertise indicates that focused practice may be the sole determinant of skill, while intelligence researchers underline the relative importance of abilities at even the highest level of skill. There is indeed a large body of research that acknowledges the role of both factors in skill development and retention. It is, however, unknown how intelligence and practice come together to enable the acquisition and retention of complex skills across the life span. Instead of focusing on the 2 factors, intelligence and practice, in isolation, here we look at their interplay throughout development. In a longitudinal study that tracked chess players throughout their careers, we show that both intelligence and practice positively affect the acquisition and retention of chess skill. Importantly, the nonlinear interaction between the 2 factors revealed that more intelligent individuals benefited more from practice. With the same amount of practice, they acquired chess skill more quickly than less intelligent players, reached a higher peak performance, and arrested decline in older age. Our research demonstrates the futility of scrutinizing the relative importance of highly intertwined factors in human development.
Did John Calhoun’s 1960s Mouse Utopia really show that animal (and human) populations will expand to arbitrary densities, creating socially-driven pathology and collapse? Reasons for doubt.
Did John Calhoun’s 1960s Mouse Utopia really show that animal (and human) populations will expand to arbitrary densities, creating socially-driven pathology and collapse? I give reasons for doubt about its replicability, interpretation, and meaningfulness.
One of the most famous experiments in psychology & sociology was John Calhoun’s Mouse Utopia experiments in the 1960s–1970s. In the usual telling, Mouse Utopia created ideal mouse environments in which the mouse population was permitted to increase as much as possible; however, the overcrowding inevitably resulted in extreme levels of physical & social dysfunctionality, and eventually population collapse & even extinction. Looking more closely into it, there are reasons to doubt the replicability of the growth & pathological behavior & collapse of this utopia (“no-place”), and if it does happen, whether it is driven by the social pressures as claimed by Calhoun or by other causal mechanisms at least as consistent with the evidence like disease or mutational meltdown.
UK Biobank is a health resource with data from over 500,000 adults. The participants have been assessed on cognitive function since baseline. The cognitive tests in UK Biobank are brief and bespoke, and are administered without supervision on a touchscreen computer. Psychometric information on the tests is limited.
The present study examined their concurrent validity and short-term test-retest reliability. A sample of 160 participants (mean age = 62.59, SD = 10.24) completed the UK Biobank cognitive assessment and a range of well-validated cognitive tests (‘reference tests’). 52 participants returned 4 weeks later to repeat the UK Biobank tests. Correlations were calculated between UK Biobank tests and the reference tests. 4-week test-retest correlations were calculated for UK Biobank tests.
UK Biobank cognitive tests showed a range of correlations with their respective reference tests, i.e. those tests that are thought to assess the same underlying cognitive ability (mean Pearson r = 0.53, range = 0.22 to 0.83, p ≤ 0.005). For test-retest reliability of the UK Biobank tests were moderate-to-high (mean Pearson r = 0.55, range = 0.40 to 0.89, p ≤ 0.003).
Despite the brief, non-standard nature of the UK Biobank cognitive tests, some showed substantial concurrent validity and test-retest reliability. These psychometric results provide currently-lacking information on the validity of the UK Biobank cognitive tests.
There exists a moderate correlation between MRI-measured brain size and the general factor of IQ performance (g), but the question of whether the association reflects a theoretically important causal relationship or spurious confounding remains somewhat open. Previous small studies (n < 100) looking for the persistence of this correlation within families failed to find a tendency for the sibling with the larger brain to obtain a higher test score. We studied the within-family relationship between brain volume and intelligence in the much larger sample provided by the Human Connectome Project (n = 1022) and found a highly statistically-significant correlation (disattenuated ρ = 0.18, p < 0.001). We replicated this result in the Minnesota Center for Twin and Family Research (n = 2698), finding a highly statistically-significant within-family correlation between head circumference and intelligence (disattenuated ρ = 0.19, p < 0.001). We also employed novel methods of causal inference relying on summary statistics from genome-wide association studies (GWAS) of head size (n ≈ 10,000) and measures of cognition (257,000 < n < 767,000). Using bivariate LD Score regression, we found a genetic correlation between intracranial volume (ICV) and years of education (EduYears) of 0.41 (p < 0.001). Using the Latent Causal Variable (LCV) method, we found a genetic causality proportion of 0.72 (p < 0.001); thus the genetic correlation arises from an asymmetric pattern, extending to sub-significant loci, of genetic variants associated with ICV also being associated with EduYears but many genetic variants associated with EduYears not being associated with ICV. This is the pattern of genetic results expected from a causal effect of brain size on intelligence. These findings give reason to take up the hypothesis that the dramatic increase in brain volume over the course of human evolution has been the result of natural selection favoring general intelligence.
The ability to recognise others’ emotions from nonverbal cues (emotion recognition ability, ERA) is measured with performance-based tests and has many positive correlates. Although researchers have long proposed that ERA is related to general mental ability or intelligence, a comprehensive analysis of this relationship is lacking. For instance, it remains unknown whether the magnitude of the association varies by intelligence type, ERA test features, as well as demographic variables.
The present meta-analysis examined the relationship between ERA and intelligence based on 471 effect sizes from 133 samples and found a statistically-significant mean effect size (controlled for nesting within samples) of r = 0.19.
Different intelligence types (crystallized, fluid, spatial, memory, information processing speed and efficiency) yielded similar effect sizes, whereas academic achievement measures (eg. SAT scores) were unrelated to ERA. Effect sizes were higher for ERA tests that simultaneously present facial, vocal, and bodily cues (as compared to tests using static pictures) and for tests with higher reliability and more emotions. Results were unaffected by most study and sample characteristics, but effect size increased with higher mean age of the sample.
These findings establish ERA as sensory-cognitive ability that is distinct from, yet related to, intelligence.
Not all neural network architectures are created equal, some perform much better than others for certain tasks. But how important are the weight parameters of a neural network compared to its architecture? In this work, we question to what extent neural network architectures alone, without learning any weight parameters, can encode solutions for a given task. We propose a search method for neural network architectures that can already perform a task without any explicit weight training. To evaluate these networks, we populate the connections with a single shared weight parameter sampled from an uniform random distribution, and measure the expected performance. We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training. On a supervised learning domain, we find network architectures that achieve much higher than chance accuracy on MNIST using random weights. Interactive version of this paper at https: / / weightagnostic.github.io /
The present register-based study investigated the influence of familial factors on the association of IQ with educational and occupational achievement among young men in Denmark. The study population comprised all men with at least one full brother where both the individual and his brothers were born from 1950 and appeared before a draft board in 1968–1984 and 1987–2015 (n = 364,193 individuals). Intelligence was measured by Børge Priens Prøve at age 18. Educational and occupational achievement were measured by grade point average (GPA) in lower secondary school, time to receiving social benefits at ages 18–30, and gross income at age 30. The statistical analyses comprised two distinct statistical analyses of the investigated associations: A conventional cohort analysis and a within-sibship analysis in which the association under investigation was analysed within siblings while keeping familial factors shared by siblings fixed. The results showed that an appreciable part of the associations of IQ with educational and occupational achievement could be attributed to familial factors shared by siblings. However, only the within sibling association between IQ and GPA in lower secondary school clearly differed from the association observed in the cohort analysis after covariates had been taken into account.
The study of human individual differences has matured substantially, in the last decade or so owing, in part, to the notable advances in neuroimaging techniques. There are three major domains of inquiry within individual differences research: personality, creativity, and intelligence. Each has a discrete, testable definition (a new definition for intelligence is offered: rapid and accurate problem solving), and each has been associated with distinct brain regions and interactive networks.
Here, we outline commonalities between these constructs, which appear to conform to two major axes: exploratory behavior and restraint. These axes, in turn, conform largely to two major brain networks dedicated to novelty generation (ie. default mode network—DMN), and refinement of ideas (ie. cognitive control network—CCN).
Thus, human individual differences represent the expression of adaptive behaviors leading to exploratory and/or restrained action arising from brain structure and function.
Demonstration tutorial of retraining OpenAI’s GPT-2 (a text-generating Transformer neural network) on large poetry corpuses to generate high-quality English verse.
In February 2019, following up on my 2015–2016 text-generation experiments with char-RNNs, I experiment with the cutting-edge Transformer NN architecture for language modeling & text generation. Using OpenAI’s GPT-2-117M (117M) model pre-trained on a large Internet corpus and nshepperd’s finetuning code, I retrain GPT-2-117M on a large (117MB) Project Gutenberg poetry corpus. I demonstrate how to train 2 variants: “GPT-2-poetry”, trained on the poems as a continuous stream of text, and “GPT-2-poetry-prefix”, with each line prefixed with the metadata of the PG book it came from. In May 2019, I trained the next-largest GPT-2, GPT-2-345M, similarly, for a further quality boost in generated poems. In October 2019, I retrained GPT-2-117M on a Project Gutenberg corpus with improved formatting, and combined it with a contemporary poem dataset based on Poetry Foundation’swebsite.
With just a few GPU-days on 1080ti GPUs, GPT-2-117M finetuning can produce high-quality poetry which is more thematically consistent than my char-RNN poems, capable of modeling subtle features like rhyming, and sometimes even a pleasure to read. I list the many possible ways to improve poem generation and further approach human-level poems. For the highest-quality AI poetry to date, see my followup pages, “GPT-3 Creative Writing”/“GPT-3 Non-Fiction”.
Bifactor and other hierarchical models [in factor analysis] have become central to representing and explaining observations in psychopathology, health, and other areas of clinical science, as well as in the behavioral sciences more broadly. This prominence comes after a relatively rapid period of rediscovery, however, and certain features remain poorly understood.
Here, hierarchical models are compared and contrasted with other models of superordinate structure, with a focus on implications for model comparisons and interpretation. Issues pertaining to the specification and estimation of bifactor and other hierarchical models are reviewed in exploratory as well as confirmatory modeling scenarios, as are emerging findings about model fit and selection.
Bifactor and other hierarchical models provide a powerful mechanism for parsing shared and unique components of variance, but care is required in specifying and making inferences about them.
[Keywords: hierarchical model, higher order, bifactor, model equivalence, model complexity]
Figure 1: Hierarchical and related models. (a) Spearman’s (1904a, 1904b) 2-factor model, a precursor to hierarchical and bifactor models. The 2-factor model includes a general factor (G) as well as systematic specific factors (S) and random error factors (e). As originally formulated, Spearman’s 2-factor model cannot be estimated, but it established the idea of a superordinate general factor plus subordinate specific factors that account for systematic residual influences not accounted for by the general factor. (b) The hierarchical or bifactor model, which includes superordinate general factors (G) as well as subordinate specific factors (S); error factors are not shown. Bifactor models are a subtype of hierarchical model with one superordinate factor and multiple subordinate factors. The 2-factor model and hierarchical model are examples of top-down models, in that subordinate factors instantiate residual effects that are unexplained by the superordinate factor.
Although bifactor and other hierarchical models are now commonplace, this was not always so. Their current ubiquity follows a long period of relative neglect (Reise 2012), having been derived in the early 20th century (Holzinger & Harman 1938, Holzinger & Swineford 1937) before being somewhat overlooked for a number of decades and then being rediscovered more recently. Bifactor models were mistakenly dismissed as equivalent to and redundant with other superordinate structural models (eg. Adcock 1964, Humphreys 1981, Wherry 1959, Reise 2012, Yung et al 1999); as differences between bifactor models and other types of superordinate structural models became more recognized (Yung et al 1999), interest in bifactor models reemerged.
…Summary Points:
Bifactor and other hierarchical models represent superordinate structure in terms of orthogonal general and specific factors representing distinct, non-nested components of shared variance among indicators. This contrasts with higher-order models, which represent superordinate structure in terms of specific factors that are nested in general factors, and correlated-factors models, which represent superordinate structure in terms of correlations among subordinate factors.
Higher-order models can be approached as a constrained form of hierarchical models, in which direct relationships between superordinate factors and observed variables in the hierarchical model are constrained to equal the products of superordinate-subordinate paths and subordinate-observed variable paths.
Multiple exploratory factor analytic approaches to the delineation of hierarchical structure are available, including rank-deficient transformations, analytic rotations, and targeted rotations. Among other things, these transformations and rotations differ in the number of factors being rotated, the nature of those factors, and how superordinate factor structures are approximated.
Misspecification or under-specification of confirmatory bifactor and hierarchical models can occur for multiple reasons. Problems with model identification may occur (1) with specific patterns of homogeneity in estimated or observed covariances, (2) if factors are allowed to correlate in inadmissible ways, or (3) if covariate paths imply inadmissible correlations. Signs of model misspecification may be evident in anomalous estimates, such as loading estimates near boundaries, or estimates that are suggestive of other types of models.
Common model fit statistics can overstate the fit of bifactor models due to the tendency of bifactor and other hierarchical models to overfit to data in general, regardless of plausibility or population structure. Hierarchical models are similar to exploratory factor models in their expansiveness of fit, and, in general, they are more expansive in fit than other confirmatory models.
Future Issues:
Research is needed to determine how to best account for the flexibility of hierarchical models when comparing models and evaluating model fit, given that the relative flexibility of hierarchical models can only partly be accounted for by the number of parameters. Approaches based on minimum description length and related paradigms, such as Bayesian inference with reference priors, are promising in this regard.
More research is needed to clarify the properties of hierarchical structures when they are embedded in longitudinal models and models with covariates. As with challenges of multicollinearity in regression, parsing unique general and specific factor components of explanatory paths may be inferentially challenging in the presence of strongly related predictors, covariates, and outcomes.
More can be learned about the specification and identification of hierarchical models and the relationships between hierarchical models and other types of models, such as exploratory factor models. Similarities in overfitting patterns between exploratory and hierarchical models, approaches to hierarchical structure through bifactor rotations, and patterns of anomalous estimates that are sometimes obtained with hierarchical models, point to important relationships between exploratory and hierarchical models. Further explication of model specification principles with hierarchical models would also help clarify the appropriate structures to consider when evaluating models.
Large-scale phylogenetic studies of animal cognition have revealed robust links between absolute brain volume and species differences in executive function. However, past comparative samples have been composed largely of primates, which are characterized by evolutionarily derived neural scaling rules. Therefore, it is currently unknown whether positive associations between brain volume and executive function reflect a broad-scale evolutionary phenomenon, or alternatively, an unique consequence of primate brain evolution.
Domestic dogs provide a powerful opportunity for investigating this question due to their close genetic relatedness, but vast intraspecific variation. Using citizen science data on more than 7000 purebred dogs from 74 breeds, and controlling for genetic relatedness between breeds, we identify strong relationships between estimated absolute brain weight and breed differences in cognition. Specifically, larger-brained breeds performed statistically-significantly better on measures of short-term memory and self-control. However, the relationships between estimated brain weight and other cognitive measures varied widely, supporting domain-specific accounts of cognitive evolution.
Our results suggest that evolutionary increases in brain size are positively associated with taxonomic differences in executive function, even in the absence of primate-like neuroanatomy. These findings also suggest that variation between dog breeds may present a powerful model for investigating correlated changes in neuroanatomy and cognition among closely related taxa.
The neural substrates of intelligence represent a fundamental but largely uncharted topic in human developmental neuroscience. Prior neuroimaging studies have identified modest but highly dynamic associations between intelligence and cortical thickness (CT) in childhood and adolescence. In a separate thread of research, quantitative genetic studies have repeatedly demonstrated that most measures of intelligence are highly heritable, as are many brain regions associated with intelligence. In the current study, we integrate these 2 streams of prior work by examining the genetic contributions to CT-intelligence relationships using a genetically informative longitudinal sample of 813 typically developing youth, imaged with high-resolution MRI and assessed with Wechsler Intelligence Scales (IQ). In addition to replicating the phenotypic association between multimodal association cortex and language centers with IQ, we find that CT-IQ covariance is nearly entirely genetically mediated. Moreover, shared genetic factors drive the rapidly evolving landscape of CT-IQ relationships in the developing brain.
How and when education improves cognitive capacity is an issue of profound societal importance. Education and later-life education-related factors, such as occupational complexity and engagement in cognitive-intellectual activities, are frequently considered indices of cognitive reserve, but whether their effects are truly causal remains unclear. In this study, after accounting for general cognitive ability (GCA) at an average age of 20 y, additional education, occupational complexity, or engagement in cognitive-intellectual activities accounted for little variance in late midlife cognitive functioning in men age 56–66 (n= 1009). Age 20 GCA accounted for 40% of variance in the same measure in late midlife and ~10% of variance in each of seven cognitive domains. The other factors each accounted for <1% of the variance in cognitive outcomes. The impact of these other factors likely reflects reverse causation—namely, downstream effects of early adult GCA. Supporting that idea, age 20 GCA, but not education, was associated with late midlife cortical surface area (n= 367). In our view, the most parsimonious explanation of our results, a meta-analysis of the impact of education, and epidemiologic studies of the Flynn effect is that intellectual capacity gains due to education plateau in late adolescence/early adulthood. Longitudinal studies with multiple cognitive assessments before completion of education would be needed to confirm this speculation. If cognitive gains reach an asymptote by early adulthood, then strengthening cognitive reserve and reducing later-life cognitive decline and dementia risk may really begin with improving educational quality and access in childhood and adolescence.
Objective: This study examined the relation between polygenic scores (PGSs) for 5 major psychiatric disorders and 2 cognitive traits with brain magnetic resonance imaging morphologic measurements in a large population-based sample of children. In addition, this study tested for differences in brain morphology-mediated associations between PGSs for psychiatric disorders and PGSs for related behavioral phenotypes.
Method: Participants included 1,139 children from the Generation R Study assessed at 10 years of age with genotype and neuroimaging data available. PGSs were calculated for schizophrenia, bipolar disorder, major depression disorder, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, intelligence, and educational attainment using results from the most recent genome-wide association studies. Image processing was performed using FreeSurfer to extract cortical and subcortical brain volumes.
Results: Greater genetic susceptibility for ADHD was associated with smaller caudate volume (strongest prior = 0.01: β = −0.07, p = 0.006). In boys, mediation analysis estimates showed that 11% of the association between the PGS for ADHD and the PGS attention problems was mediated by differences in caudate volume (n = 535), whereas mediation was not statistically-significant in girls or the entire sample. PGSs for educational attainment and intelligence showed positive associations with total brain volume (strongest prior = 0.5: β = 0.14, p = 7.12 × 10−8; and β = 0.12, p = 6.87 × 10−7, respectively).
Conclusion: The present findings indicate that the neurobiological manifestation of polygenic susceptibility for ADHD, educational attainment, and intelligence involve early morphologic differences in caudate and total brain volumes in childhood. Furthermore, the genetic risk for ADHD might influence attention problems through the caudate nucleus in boys.
Using newly available polygenic scores for educational attainment and cognitive ability, this paper investigates the possible presence and causes of a negative association between IQ and fertility in the Wisconsin Longitudinal Study sample, an issue that Retherford and Sewell first addressed 30 years ago. The effect of the polygenic score on the sample’s reproductive characteristics was indirect: a latent cognitive ability measure, comprised of both educational attainment and IQ, wholly mediated the relationship. Age at first birth mediated the negative effect of cognitive ability on sample fertility, which had a direct (positive) effect on the number of grandchildren. statistically-significantly greater impacts of cognitive ability on the sample’s fertility characteristics were found among the female subsample. This indicates that, in this sample, having a genetic disposition toward higher cognitive ability does not directly reduce number of offspring; instead, higher cognitive ability is a risk factor for prolonging reproductive debut, which, especially for women, reduces the fertility window and, thus, the number of children and grandchildren that can be produced. By estimating the effect of the sample’s reproductive characteristics on the strength of polygenic selection, it was found that the genetic variance component of IQ should be declining at a rate between −0.208 (95% CI [−0.020, −0.383]) and −0.424 (95% CI [−0.041, −0.766]) points per decade, depending on whether GCTA-GREML or classical behavior genetic estimates of IQ heritability are used to correct for ‘missing’ heritability.
Human intelligence can be broadly subdivided into fluid (gf) and crystallized (gc) intelligence, each tapping into distinct cognitive abilities. Although neuroanatomical correlates of intelligence have been previously studied, differential contribution of cortical morphologies to gf and gc has not been fully delineated. Here, we tried to disentangle the contribution of cortical thickness, cortical surface area, and cortical gyrification to gf and gc in a large sample of healthy young subjects (n = 740, Human Connectome Project) with high-resolution MRIs, followed by replication in a separate data set with distinct cognitive measures indexing gf and gc. We found that while gyrification in distributed cortical regions had positive association with both gf and gc, surface area and thickness showed more regional associations. Specifically, higher performance in gf was associated with cortical expansion in regions related to working memory, attention, and visuo-spatial processing, while gc was associated with thinner cortex as well as higher cortical surface area in language-related networks. We discuss the results in a framework where “horizontal” cortical expansion enables higher resource allocation, computational capacity, and functional specificity relevant to gf and gc, while lower cortical thickness possibly reflects cortical pruning facilitating “vertical” intracolumnar efficiency in knowledge-based tasks relevant mostly to gc.
MacCann et al 2014 explored various unidimensional, oblique, hierarchical and bifactor models to suggest that ability EI can represent a distinct set of cognitive abilities that can be placed within existing intelligence frameworks.
The current study presents a conceptual replication of these analyses from data collected using alternative (non-proprietary) measures. Using a data set of 830 individuals, the current study provides further evidence to suggest ability EI best represents a hierarchical construct formed of emotion perception, understanding and management factors, structured as a second stratum factor within broader models of cognitive ability.
Selection effects in media become increasingly strong as populations and media increase, meaning that rare datapoints driven by unusual processes such as the mentally ill or hoaxers are increasingly unreliable as evidence of anything at all and must be ignored. At scale, anything that can happen will happen a small but nonzero times.
Online & mainstream media and social networking have become increasingly misleading as to the state of the world by focusing on ‘stories’ and ‘events’ rather than trends and averages. This is because as the global population increases and the scope of media increases, media’s urge for narrative focuses on the most extreme outlier datapoints—but such datapoints are, at a global scale, deeply misleading as they are driven by unusual processes such as the mentally ill or hoaxers.
At a global scale, anything that can happen will happen a small but nonzero times: this has been epitomized as “Littlewood’s Law: in the course of any normal person’s life, miracles happen at a rate of roughly one per month.” This must now be extended to a global scale for a hyper-networked global media covering anomalies from 8 billion people—all coincidences, hoaxes, mental illnesses, psychological oddities, extremes of continuums, mistakes, misunderstandings, terrorism, unexplained phenomena etc. Hence, there will be enough ‘miracles’ that all media coverage of events can potentially be composed of nothing but extreme outliers, even though it would seem like an ‘extraordinary’ claim to say that all media-reported events may be flukes.
This creates an epistemic environment deeply hostile to understanding reality, one which is dedicated to finding arbitrary amounts of and amplifying the least representative datapoints.
Given this, it is important to maintain extreme skepticism of any individual anecdotes or stories which are selectively reported but still claimed (often implicitly) to be representative of a general trend or fact about the world. Standard techniques like critical thinking, emphasizing trends & averages, and demanding original sources can help fight the biasing effect of news.
Markets/evolution as backstops/ground truths for reinforcement learning/optimization: on some connections between Coase’s theory of the firm/linear optimization/DRL/evolution/multicellular life/pain/Internet communities as multi-level optimization problems.
One defense of free markets notes the inability of non-market mechanisms to solve planning & optimization problems. This has difficulty with Coase’s paradox of the firm, and I note that the difficulty is increased by the fact that with improvements in computers, algorithms, and data, ever larger planning problems are solved. Expanding on some Cosma Shalizi comments, I suggest interpreting phenomenon as multi-level nested optimization paradigm: many systems can be usefully described as having two (or more) levels where a slow sample-inefficient but ground-truth ‘outer’ loss such as death, bankruptcy, or reproductive fitness, trains & constrains a fast sample-efficient but possibly misguided ‘inner’ loss which is used by learned mechanisms such as neural networks or linear programming group selection perspective. So, one reason for free-market or evolutionary or Bayesian methods in general is that while poorer at planning/optimization in the short run, they have the advantage of simplicity and operating on ground-truth values, and serve as a constraint on the more sophisticated non-market mechanisms. I illustrate by discussing corporations, multicellular life, reinforcement learning & meta-learning in AI, and pain in humans. This view suggests that are inherent balances between market/non-market mechanisms which reflect the relative advantages between a slow unbiased method and faster but potentially arbitrarily biased methods.
A positive relationship between brain volume and intelligence has been suspected since the 19th century, and empirical studies seem to support this hypothesis. However, this claim is controversial because of concerns about publication bias and the lack of systematic control for critical confounding factors (eg. height, population structure). We conducted a preregistered study of the relationship between brain volume and cognitive performance using a new sample of adults from the United Kingdom that is about 70% larger than the combined samples of all previous investigations on this subject (N = 13,608). Our analyses systematically controlled for sex, age, height, socioeconomic status, and population structure, and our analyses were free of publication bias. We found a robust association between total brain volume and fluid intelligence (r = 0.19), which is consistent with previous findings in the literature after controlling for measurement quality of intelligence in our data. We also found a positive relationship between total brain volume and educational attainment (r = 0.12). These relationships were mainly driven by gray matter (rather than white matter or fluid volume), and effect sizes were similar for both sexes and across age groups.
[Keywords: intelligence, educational attainment, brain volume, preregistered analysis, UK Biobank, open data, open materials, preregistered]
The relationship between general cognitive ability and reproduction is reviewed.
There is an inverse relation between cognitive ability and number of children.
The effect is stronger among females than males.
The effect appears to be increasing in strength over time.
Notable limitations of the current literature are reviewed.
The purpose of this study is to conduct a systematic review of the literature on the relationship between general cognitive ability and fertility among modern humans. Our goals were to (a) evaluate the state of the extant literature, and (b) provide a quantitative summary of effect sizes to the extent possible (given the limitations of the literature). A thorough search identified 17 unique datasets that passed the inclusion criteria. Using a Random Effects Model to evaluate the data, the overall weighted effect was r = −0.11, although the data also indicated a sex effect (stronger correlations among females than males), and a race effect (stronger correlations among Black and Hispanic populations compared to Whites). Importantly, the data suggest the correlation has been increasing in strength throughout the 20th century (and early 21st). Finally, we discovered several notable limitations of the extant literature; limitations that currently prohibit a psychometric meta-analysis. We discuss these issues with emphasis on improving future primary studies to allow for more effective meta-analytic investigations.
In 19 (sub)samples from seven countries (United States, Austria, Germany, Costa Rica, Ecuador, Vietnam, Brazil), we analyzed the impact of parental education compared with wealth on the cognitive ability of children (aged 4–22 years, total n = 15,297). The background of their families ranged from poor indigenous remote villagers to academic families in developed countries, including parents of the gifted. Children’s cognitive ability was measured with mental speed tests, Culture Fair Intelligence Test (CFT), the Raven’s, Wiener Entwicklungstest (WET), Cognitive Abilities Test (CogAT), Piagetian tasks, Armed Forces Qualification Test (AFQT), Progress in International Reading Literacy Study (PIRLS), Trends in International Mathematics and Science Study (TIMSS), and Programme for International Student Assessment (PISA). Parental wealth was estimated by asking for income, indirectly by self-assessment of relative wealth, and by evaluating assets. The mean direct effect of parental education was greater than wealth. In path analyses, parental education (βEd) also showed a stronger impact on children’s intelligence than familial economic status (βIn, total effect averages: βEd = .30–.45, βIn = .09–.12; N = 15,125, k = 18). The effects on mental speed were smaller than for crystallized intelligence, but still larger for parental education than familial economic status (βEd → MS = .25, βIn → MS = .00, βEd → CI = .36, βIn → CI = .09; N = 394, k = 3). Additional factors affecting children’s cognitive ability are number of books, marital status, educational behavior of parents, and behavior of children. If added, a general background (ethnicity, migration) factor shows strong effects (βBg = .30–.36). These findings are discussed in terms of environmental versus hidden genetic effects.
[Keywords: cognitive competence, intelligence development, fluid and crystallized intelligence, SES, number of books, marital status, smoking]
Decision analysis of whether cloning the most elite Special Forces dogs is a profitable improvement over standard selection procedures. Unless training is extremely cheap or heritability is extremely low, dog cloning is hypothetically profitable.
Cloning is widely used in animal & plant breeding despite steep costs due to its advantages; more unusual recent applications include creating entire polo horse teams and reported trials of cloning in elite police/Special Forces war dogs. Given the cost of dog cloning, however, can this ever make more sense than standard screening methods for selecting from working dog breeds, or would the increase in successful dog training be too low under all reasonable models to turn a profit?
I model the question as one of expected cost per dog with the trait of successfully passing training, success in training being a dichotomous liability threshold with a polygenic genetic architecture; given the extreme level of selection possible in selecting the best among already-elite Special Forces dogs and a range of heritabilities, this predicts clones’ success probabilities. To approximate the relevant parameters, I look at some reported training costs and success rates for regular dog candidates, broad dog heritabilities, and the few current dog cloning case studies reported in the media.
Since none of the relevant parameters are known with confidence, I run the cost-benefit equation for many hypothetical scenarios, and find that in a large fraction of them covering most plausible values, dog cloning would improve training yields enough to be profitable (in addition to its other advantages).
As further illustration of the use-case of screening for an extreme outcome based on a partial predictor, I consider the question of whether height PGSes could be used to screen the US population for people of NBA height, which turns out to be reasonably doable with current & future PGSes.
Little is known about the etiology of developmental change and continuity in educational achievement.
Here, we study achievement from primary school to the end of compulsory education for 6,000 twin pairs in the UK-representative Twins Early Development Study sample.
Results: showed that educational achievement is highly heritable across school years and across subjects studied at school (twin heritability ~60%; SNP heritability ~30%); achievement is highly stable (phenotypic correlations ~0.70 from ages 7 to 16). Twin analyses, applying simplex and common pathway models, showed that genetic factors accounted for most of this stability (70%), even after controlling for intelligence (60%). Shared environmental factors also contributed to the stability, while change was mostly accounted for by individual-specific environmental factors. polygenic scores, derived from a genome-wide association analysis of adult years of education, also showed stable effects on school achievement.
We conclude that the remarkable stability of achievement is largely driven genetically even after accounting for intelligence.
An annotated fulltext bibliography of publications on the Study of Mathematically Precocious Youth (SMPY), a longitudinal study of high-IQ youth.
SMPY (Study of Mathematically Precocious Youth) is a long-running longitudinal survey of extremely mathematically-talented or intelligent youth, which has been following high-IQ cohorts since the 1970s. It has provided the largest and most concrete findings about the correlates and predictive power of screening extremely intelligent children, and revolutionized gifted & talented educational practices.
Because it has been running for over 40 years, SMPY-related publications are difficult to find; many early papers were published only in long-out-of-print books and are not available in any other way. Others are digitized and more accessible, but one must already know they exist. Between these barriers, SMPY information is less widely available & used than it should be given its importance.
To fix this, I have been gradually going through all SMPY citations and making fulltext copies available online with occasional commentary.
Here we conducted a large-scale genetic association analysis of educational attainment in a sample of ~1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11–13% of the variance in educational attainment and 7–10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
The study aim was to explore the relationship between a developmental assessment at preschool age and an intelligence quotient (IQ) assessment at school age. One hundred sixty-two children were assessed at 2.5 years with the Bayley Scales of Infant and Toddler Development—Third Edition (Bayley-III) and then at 6.5 years with the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV). The Bayley-III Cognitive Index score was the Bayley entity that showed the highest correlation with WISC-IV Full-Scale IQ (FSIQ; r = 0.41). There was a statistically-significant difference between the individual WISC-IV FSIQ and the Bayley-III Cognitive Index scores. Analyses showed an average difference of −4 units and 95% limits of agreement of −18.5 to 26.4 units. A multivariate model identified the Bayley-III Cognitive Index score as the most important predictor for FSIQ and General Ability Index (GAI), respectively, in comparison with demographic factors. The model explained 24% of the total FSIQ variation and 26% of the GAI variation. It was concluded that the Bayley-III measurement was an insufficient predictor of later IQ.
Academically selective high schools are a polarizing topic in education policy, despite only having a small presence in some Australian states. They appear successful. The schools regularly top annual school rankings of university entrance results, but this is perhaps unsurprising given that their students are admitted based on their performances on an entrance exam.
This thesis asks whether selective high schools improve their students’ university entrance results beyond what they would have achieved otherwise. The main chapter is a case study from an anonymized Australian state that follows high-achievement students through high school. The key challenge is finding a group of non-selective students comparable to those who attend selective schools. For additional background, the thesis explored the following themes: the historical development of selective high schools, the premise that the schools cater to gifted and talented students, and the high levels of demand for the schools within current trends in educational policy.
The thesis provides the first estimates of the selective school effect (roughly contemporaneous with Zen 2016) from matching and regression discontinuity approaches in the Australian context, which are improved statistical methods compared with that of previous research (eg. regression analyses from Lu & Rickard 2014). The estimates point to small positive effects at best on university entrance results from attending the selective schools.
Overall, the small selective school effect appears to reflect the high levels of educational aspiration of both selective students as well as applicants who attended other schools. Both groups of students appear to be among the most driven and motivated, being disproportionately from immigrant and socio-economically advantaged backgrounds and having implicitly signaled an aspirational intent by applying to the schools.
Lastly, the thesis expands on one aspect of the selective schools, whereby many of their students experience a decrease in within-school achievement ranks from attending a school with high-achievement peers. In a more general context, the thesis assesses the effect from changes in local ranks on later achievement for students who transitioned from primary to secondary school. The results indicate that perceived increases in local rank have a negative effect on standardized test scores, suggesting that students reduced their allocation of effort in response to random increases in rank.
The new empirical evidence from the thesis supports the view that selective schools represent a positive achievement ideal for their students. Recent public policy discourse on the selective schools has included calls for expansion of the system to the primary school level in one state, and criticisms of a hyper-competitive culture at the schools, including suggestions of unfair entry due to excessive tutoring on the part of applicants. The research positively contributes to the discourse by providing historical context, identifying the relevant issues and articulating the potential indirect consequences of these policies.
Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (rg = 0.70). We used these findings as foundations for our use of a novel approach—multi-trait analysis of genome-wide association studies (MTAG; Turley et al 2017)—to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample.
By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination—as well as genes expressed in the synapse, and those involved in the regulation of the nervous system—may explain some of the biological differences in intelligence.
The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.
People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains.
We tested this hypothesis using four large imaging genetics studies (combined n = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (ie. brain size), and cognitive test performance.
Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains.
We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research.
Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.
Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3–7, but much about its genetic underpinnings remains to be discovered.
Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure.
We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer’s disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.
Importance;: Copy number variants (CNVs) classified as pathogenic are identified in 10% to 15% of patients referred for neurodevelopmental disorders. However, their effect sizes on cognitive traits measured as a continuum remain mostly unknown because most of them are too rare to be studied individually using association studies.
Objective: To measure and estimate the effect sizes of recurrent and nonrecurrent CNVs on IQ.
Design, Setting, and Participants: This study identified all CNVs that were 50 kilobases (kb) or larger in 2 general population cohorts (the IMAGEN project and the Saguenay Youth Study) with measures of IQ. Linear regressions, including functional annotations of genes included in CNVs, were used to identify features to explain their association with IQ. Validation was performed using intraclass correlation that compared IQ estimated by the model with empirical data.
Main Outcomes and Measures: Performance IQ (PIQ), verbal IQ (VIQ), and frequency of de novo CNV events.
Results: The study included 2090 European adolescents from the IMAGEN study and 1983 children and parents from the Saguenay Youth Study. Of these, genotyping was performed on 1804 individuals from IMAGEN and 977 adolescents, 445 mothers, and 448 fathers (484 families) from the Saguenay Youth Study. We observed 4928 autosomal CNVs larger than 50 kb across both cohorts. For rare deletions, size, number of genes, and exons affect IQ, and each deleted gene is associated with a mean (SE) decrease in PIQ of 0.67 (0.19) points (p = 6 × 10–4); this is not so for rare duplications and frequent CNVs. Among 10 functional annotations, haploinsufficiency scores best explain the association of any deletions with PIQ with a mean (SE) decrease of 2.74 (0.68) points per unit of the probability of being loss-of-function intolerant (p = 8 × 10–5). Results are consistent across cohorts and unaffected by sensitivity analyses removing pathogenic CNVs. There is a 0.75 concordance (95% CI, 0.39–0.91) between the effect size on IQ estimated by our model and IQ loss calculated in previous studies of 15 recurrent CNVs. There is a close association between effect size on IQ and the frequency at which deletions occur de novo (odds ratio, 0.86; 95% CI, 0.84–0.87; p = 2.7 × 10–88). There is a 0.76 concordance (95% CI, 0.41–0.91) between de novo frequency estimated by the model and calculated using data from the DECIPHER database.
Conclusions and Relevance: Models trained on nonpathogenic deletions in the general population reliably estimate the effect size of pathogenic deletions and suggest omnigenic associations of haploinsufficiency with IQ. This represents a new framework to study variants too rare to perform individual association studies and can help estimate the cognitive effect of undocumented deletions in the neurodevelopmental clinic.
Lewis Terman is widely seen as the “father of gifted education”, yet his work is controversial. Terman’s “mixed legacy” includes the pioneering work in the creation of intelligence tests, the first large-scale longitudinal study, and the earliest discussions of gifted identification, curriculum, ability grouping, acceleration, and more. However, since the 1950s, Terman has been viewed as a sloppy thinker at best and a racist, sexist, and/or classist at worst.
This article explores the most common criticisms of Terman’s legacy: an overemphasis on IQ, support for the meritocracy, and emphasizing genetic explanations for the origin of intelligence differences over environmental ones. Each of these criticisms is justified to some extent by the historical record, and each is relevant today. Frequently overlooked, however, is Terman’s willingness to form a strong opinion based on weak data.
The article concludes with a discussion of the important lessons that Terman’s work has for modern educators and psychologists, including his contributions to psychometrics and gifted education, his willingness to modify his opinions in the face of new evidence, and his inventiveness and inclination to experiment. Terman’s legacy is complex, but one that provides insights that can enrich modern researchers and practitioners in these areas.
Finnish elite high school students enrol in university and so-called elite fields of study more often than Finnish high school students on average. However, those who attend elite high schools are also higher-achieving in terms of baseline grade point average (GPA) from comprehensive school. This selection bias must be taken into account in studying the causal effects of elite high schools.
This study focuses on 5 elite high schools in the Helsinki region and aims to solve the problem of selection bias by using a regression discontinuity design (RDD). In our case RDD exploits the entrance thresholds of elite high schools as a rule which assigns applicants near the threshold into treatment and control groups. By comparing the outcomes (eg. the probability of enrolment in an university) of these groups we can estimate the causal effects of an elite high school offer on various educational outcomes, such as university enrolment.
We find that crossing the threshold of an elite high school leads to a higher-achieving peer group in terms of baseline GPA. However, the elite high school offer does not have a statistically-significant effect on the probability of enrolment in an university or on the probability of enrolment in an elite field of study. The only exception is Etelä-Tapiola high school, which has a positive effect on the probability of enrolment in an university.
[Keywords: education, regression discontinuity design, peer effects, school choice]
Intelligence—the ability to learn, reason and solve problems—is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.
It is well established that religiosity correlates inversely with intelligence. A prominent hypothesis states that this correlation reflects behavioral biases toward intuitive problem solving, which causes errors when intuition conflicts with reasoning.
We tested predictions of this hypothesis by analyzing data from 2 large-scale Internet-cohort studies (combined n = 63,235).
We report that atheists surpass religious individuals in terms of reasoning but not working-memory performance. The religiosity effect is robust across sociodemographic factors including age, education and country of origin. It varies statistically-significantly across religions and this co-occurs with substantial cross-group differences in religious dogmatism. Critically, the religiosity effect is strongest for tasks that explicitly manipulate conflict; more specifically, atheists outperform the most dogmatic religious group by a substantial margin (0.6 standard deviations) during a color-word conflict task but not during a challenging matrix-reasoning task.
These results support the hypothesis that behavioral biases rather than impaired general intelligence underlie the religiosity effect.
When the fungus infects a carpenter ant, it grows through the insect’s body, draining it of nutrients and hijacking its mind. Over the course of a week, it compels the ant to leave the safety of its nest and ascend a nearby plant stem. It stops the ant at a height of 25 centimeters—a zone with precisely the right temperature and humidity for the fungus to grow. It forces the ant to permanently lock its mandibles around a leaf. Eventually, it sends a long stalk through the ant’s head, growing into a bulbous capsule full of spores. And because the ant typically climbs a leaf that overhangs its colony’s foraging trails, the fungal spores rain down onto its sisters below, zombifying them in turn.
…It’s also an obsession of one David Hughes, an entomologist at Pennsylvania State University, who has been studying it for years. He wants to know exactly how this puppet master controls its puppets—and his latest experiments suggest that it’s even more ghoulish than it first appears.
…When the fungus first enters its host, it exists as single cells that float around the ant’s bloodstream, budding off new copies of themselves. But at some point, as Fredericksen’s images show, these single cells start working together. They connect to each other by building short tubes, of a kind that have only ever been seen before in fungi that infects plants. Hooked up in this way, they can communicate and exchange nutrients. They can also start invading the ant’s muscles, either by penetrating the muscle cells themselves or growing into the spaces between them. The result is what you can see in this video: a red muscle fiber, encircled and drained by a network of interconnected yellow fungal cells. This is something unique to Ophiocordyceps. Hughes’s team found that another parasitic fungus, which fatally infects ants but doesn’t manipulate their minds, also spreads into muscles but doesn’t form tubes between individual cells, and doesn’t wire itself into large networks.
Whenever Hughes or anyone else discusses the zombie-ant fungus, they always talk about it as a single entity, which corrupts and subverts a host. But you could also think of the fungus as a colony, much like the ants it targets. Individual microscopic cells begin life alone but eventually come to cooperate, fusing into a superorganism. Together, these brainless cells can commandeer the brain of a much larger creature. But surprisingly, they can do that without ever physically touching the brain itself. Hughes’s team found that fungal cells infiltrate the ant’s entire body, including its head, but they leave its brain untouched…“But manipulation of ants by Ophiocordyceps is so exquisitely precise that it is perhaps surprising that the fungus doesn’t invade the brain of its host”, Weinersmith says.
Analysis of Digit span and Corsi-block span data from 1754 independent samples (n = 139,677), covering a period of 43 years
Verbal and visuospatial short-term memory (STM) were positively correlated with year of publication.
Verbal and visuospatial working memory (WM) were negatively correlated with year of publication.
The Flynn effect has been investigated extensively for IQ, but few attempts have been made to study it in relation to working memory (WM). Based on the findings from a cross-temporal meta-analysis using 1754 independent samples (n = 139,677), the Flynn effect was observed across a 43-year period, with changes here expressed in terms of correlations (coefficients) between year of publication and mean memory test scores. Specifically, the Flynn effect was found for forward digit span (r = 0.12, p < 0.01) and forward Corsi block span (r = 0.10, p < 0.01). Moreover, an anti-Flynn effect was found for backward digit span (r = −0.06, p < 0.01) and for backward Corsi block span (r = −0.17, p < 0.01). Overall, the results support co-occurrence theories that predict simultaneous secular gains in specialized abilities and declines in g. The causes of the differential trajectories are further discussed.
[Keywords: Flynn effect, Short-term memory, Working memory, Forward and backward digit span, Forward and backward Corsi block span, Cross-temporal meta-analysis]
General cognitive function is a prominent human trait associated with many important life outcomes1,2, including longevity3. The substantial heritability of general cognitive function is known to be polygenic, but it has had little explication in terms of the contributing genetic variants4,5,6. Here, we combined cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total n = 280,360; age range = 16 to 102). We found 9,714 genome-wide statistically-significant SNPs (P<5 x 10−8) in 99 independent loci. Most showed clear evidence of functional importance. Among many novel genes associated with general cognitive function were SGCZ, ATXN1, MAPT, AUTS2, and P2RY6. Within the novel genetic loci were variants associated with neurodegenerative disorders, neurodevelopmental disorders, physical and psychiatric illnesses, brain structure, and BMI. Gene-based analyses found 536 genes statistically-significantly associated with general cognitive function; many were highly expressed in the brain, and associated with neurogenesis and dendrite gene sets. Genetic association results predicted up to 4% of general cognitive function variance in independent samples. There was significant genetic overlap between general cognitive function and information processing speed, as well as many health variables including longevity.
Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20 000 individuals in the Generation Scotland family cohort genotyped for ~700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.
2017-bo.pdf: “Who Becomes A Politician?”, Ernesto Dal Bó, Frederico Finan, Olle Folke, Torsten Persson, Johanna Rickne (2017-06-01; similar):
Can a democracy attract competent leaders, while attaining broad representation? Economic models suggest that free-riding incentives and lower opportunity costs give the less competent a comparative advantage at entering political life. Moreover, if elites have more human capital, selecting on competence may lead to uneven representation. This article examines patterns of political selection among the universe of municipal politicians and national legislators in Sweden, using extraordinarily rich data on competence traits and social background for the entire population.
We document 4 new facts that together characterize an “inclusive meritocracy.” First, politicians are on average statistically-significantly smarter and better leaders than the population they represent. Second, this positive selection is present even when conditioning on family (and hence social) background, suggesting that individual competence is key for selection. Third, the representation of social background, whether measured by parental earnings or occupational social class, is remarkably even. Fourth, there is at best a weak trade-off in selection between competence and social representation, mainly due to strong positive selection of politicians of low (parental) socioeconomic status. A broad implication of these facts is that it is possible for democracy to generate competent and socially representative leadership.
Intelligence is associated with important economic and health-related life outcomes1. Despite intelligence having substantial heritability2 (0.54) and a confirmed polygenic nature, initial genetic studies were mostly underpowered3,4,5. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL p < 5 × 10−8) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA p < 2.73 × 10−6), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive p = 3.5 × 10−6). Despite the well-known difference in twin-based heritability2 for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg = 0.89, LD score regressionp = 5.4 × 10−29). These findings provide new insight into the genetic architecture of intelligence.
The complex nature of human cognition has resulted in cognitive genomics lagging behind many other fields in terms of gene discovery using genome-wide association study (GWAS) methods. In an attempt to overcome these barriers, the current study utilized GWAS meta-analysis to examine the association of common genetic variation (~8M single-nucleotide polymorphisms (SNP) with minor allele frequency ⩾1%) to general cognitive function in a sample of 35 298 healthy individuals of European ancestry across 24 cohorts in the Cognitive Genomics Consortium (COGENT). In addition, we utilized individual SNP lookups and polygenic score analyses to identify genetic overlap with other relevant neurobehavioral phenotypes. Our primary GWAS meta-analysis identified two novel SNP loci (top SNPs: rs76114856 in the CENPO gene on chromosome 2 and rs6669072 near LOC105378853 on chromosome 1) associated with cognitive performance at the genome-wide statistical-significance level (p<5 × 10−8). Gene-based analysis identified an additional three Bonferroni-corrected statistically-significant loci at chromosomes 17q21.31, 17p13.1 and 1p13.3. Altogether, common variation across the genome resulted in a conservatively estimated SNP heritability of 21.5% (s.e. = 0.01%) for general cognitive function. Integration with prior GWAS of cognitive performance and educational attainment yielded several additional statistically-significant loci. Finally, we found robust polygenic correlations between cognitive performance and educational attainment, several psychiatric disorders, birth length/weight and smoking behavior, as well as a novel genetic association to the personality trait of Openness. These data provide new insight into the genetics of neurocognitive function with relevance to understanding the pathophysiology of neuropsychiatric illness.
Individually administered intelligence measures are commonly used in diagnostic work, but there is a continuing need for research investigating possible test bias among these measures. One current intelligence measure, the Differential Ability Scales, Second Edition (DAS-II), is a test with growing popularity. The issue of test bias, however, has not been thoroughly investigated with the DAS-II. The current study investigated whether the DAS-II demonstrates systematic construct bias when used with children from three racial and ethnic groups—African American, Asian, and Hispanic—when compared to non-Hispanic Caucasian children. Multi-group confirmatory factor analyses with data from the DAS-II standardization sample were used to assess whether the constructs and measurement of constructs were invariant across groups. Results indicate cross-group internal structure validity in the DAS-II, and thus a lack of construct bias. Minor differences were found, but these differences do not affect the calculation of composite scores on the DAS-II and thus would not result in unfair scoring for the groups involved. Results of this study support the appropriateness of the DAS-II for clinical use with these racial and ethnic groups.
G-factor models such as the bifactor model and the hierarchical G-factor model are increasingly applied in psychology. Many applications of these models have produced anomalous and unexpected results that are often not in line with the theoretical assumptions on which these applications are based. Examples of such anomalous results are vanishing specific factors and irregular loading patterns.
In this article, the authors show that from the perspective of stochastic measurement theory anomalous results have to be expected when G-factor models are applied to a single-level (rather than a 2-level) sampling process. The authors argue that the application of the bifactor model and related models require a 2-level sampling process that is usually not present in empirical studies.
We demonstrate how alternative models with a G-factor and specific factors can be derived that are more well-defined for the actual single-level sampling design that underlies most empirical studies. It is shown in detail how 2 alternative models, the bifactor-(S − 1) model and the bifactor-(S·I − 1) model, can be defined. The properties of these models are described and illustrated with an empirical example.
Finally, further alternatives for analyzing multidimensional models are discussed.
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