Drug-heuristics (Link Bibliography)

“Drug-heuristics” links:

  1. 1969-jensen.pdf: ⁠, Arthur R. Jensen (1969-05-01; iq):

    Arthur Jensen argues that the failure of recent compensatory education efforts to produce lasting effects on children’s IQ and achievement suggests that the premises on which these efforts have been based should be reexamined. He begins by questioning a central notion upon which these and other educational programs have recently been based: that IQ differences are almost entirely a result of environmental differences and the cultural bias of IQ tests. After tracing the history of IQ tests, Jensen carefully defines the concept of IQ, pointing out that it appears as a common factor in all tests that have been devised thus far to tap higher mental processes. Having defined the concept of intelligence and related it to other forms of mental ability, Jensen employs an analysis of model to explain how IQ can be separated into genetic and environmental components. He then discusses the concept of “heritability”, a statistical tool for assessing the degree to which individual differences in a trait like intelligence can be accounted for by genetic factors. He analyzes several lines of evidence which suggest that the heritability of intelligence is quite high (ie., genetic factors are much more important than environmental factors in producing IQ differences). After arguing that environmental factors are not nearly as important in determining IQ as are genetic factors, Jensen proceeds to analyze the environmental influences which may be most critical in determining IQ. He concludes that prenatal influences may well contribute the largest environmental influence on IQ. He then discusses evidence which suggests that social class and racial variations in intelligence cannot be accounted for by differences in environment but must be attributed partially to genetic differences. After he has discussed the influence on the distribution of IQ in a society on its functioning, Jensen examines in detail the results of educational programs for young children, and finds that the changes in IQ produced by these programs are generally small. A basic conclusion of Jensen’s discussion of the influence of environment on IQ is that environment acts as a “threshold variable.” Extreme environmental deprivation can keep the child from performing up to his genetic potential, but an enriched educational program cannot push the child above that potential. Finally, Jensen examines other mental abilities that might be capitalized on in an educational program, discussing recent findings on diverse patterns of mental abilities between ethnic groups and his own studies of associative learning abilities that are independent of social class. He concludes that educational attempts to boost IQ have been misdirected and that the educational process should focus on teaching much more specific skills. He argues that this will be accomplished most effectively if educational methods are developed which are based on other mental abilities besides IQ.

  2. DNB-FAQ

  3. Nootropics

  4. Iodine

  5. DNB-meta-analysis

  6. Replication#pygmalion-effect

  7. https://www.lesswrong.com/posts/rHBdcHGLJ7KvLJQPk/the-logical-fallacy-of-generalization-from-fictional

  8. http://web.archive.org/web/200102021712/http://sysopmind.com/algernon.html

  9. 1993-ericsson.pdf: ⁠, K. Anders Ericsson, Ralf T. Krampe, Clemens Tesch-Römer (1993-07; psychology  /​ ​​ ​writing):

    The theoretical framework presented in this article explains expert performance as the end result of individuals’ prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities () designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 yrs. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.

  10. https://www.amazon.com/Packing-Mars-Curious-Science-Life/dp/B00AR2BCLW/

  11. http://www.bronxbanterblog.com/2013/10/01/the-power-and-the-gory/

  12. https://www.cell.com/current-biology/fulltext/S0960-9822(17)31093-X

  13. http://www.impactaging.com/papers/v3/n12/full/100415.html

  14. http://philsci-archive.pitt.edu/5314/1/Griffiths_%26_Wilkins.doc

  15. https//www.edge.org/conversation/the-evolved-self-management-system

  16. http://findarticles.com/p/articles/mi_hb4384/is_3_38/ai_n29083511/

  17. #loopholes

  18. ⁠, S. Peter Apell, Michael Neidrauer, Elisabeth S. Papazoglou, Vincent Pizziconi (2012-12-16):

    Wound healing is a complex process with many components and interrelated processes on a microscopic level. This paper addresses a macroscopic view on wound healing based on an energy conservation argument coupled with a general scaling of the metabolic rate with body mass M as Mγ where 0<γ<1.

    Our three main findings are:

    1. the wound healing rate peaks at a value determined by γ alone, suggesting a concept of wound acceleration to monitor the status of a wound.
    2. We find that the time-scale for wound healing is a factor 1/​​​​​(1—γ) longer than the average internal timescale for producing new material filling the wound cavity in correspondence with that it usually takes weeks rather than days to heal a wound.
    3. The model gives a prediction for the maximum wound mass which can be generated in terms of measurable quantities related to wound status.

    We compare our model predictions to experimental results for a range of different wound conditions (healthy, lean, diabetic and obese rats) in order to delineate the most important factors for a positive wound development trajectory. On this general level our model has the potential of yielding insights both into the question of local metabolic rates as well as possible diagnostic and therapeutic aspects.

  19. https://www.nickbostrom.com/papers/converging.pdf

  20. Spaced-repetition

  21. http://www.wired.com/magazine/2012/02/ff_forgettingpill/all/1

  22. http://www.scq.ubc.ca/files/63401nature.pdf

  23. http://www.jneurosci.org/content/12/3/854.full.pdf

  24. http://paincenter.wustl.edu/c/BasicResearch/documents/Chennature2001.pdf

  25. 2011-hills.pdf: ⁠, Thomas Hills, Ralph Hertwig (2011-12-05; algernon):

    Pharmacological enhancers of cognition promise a bright new future for humankind: more focus, more willpower, and better memory, with applications ranging from education to military combat. Underlying such promises is a linear, more-is-better vision of cognition that makes intuitive sense. This vision is at odds, however, with our understanding of cognition’s evolutionary origins. The mind has evolved under various constraints and consequently represents a delicate balance among these constraints. Evidence of the trade-offs that have shaped cognition include (a) inverted U-shaped performance curves commonly found in response to pharmacological interventions and (b) unintended side effects of enhancement on other traits. Taking an evolutionary perspective, we frame the above two sets of findings in terms of within-task (exemplified by optimal-control problems) and between-task (associated with a gain/​​​​loss asymmetry) trade-offs, respectively. With this framework, psychological science can provide much-needed guidance to enhancement development, a field that still lacks a theoretical foundation.

    [Keywords: cognitive enhancements, trade-offs, constraints, evolution, side effects]

  26. http://www.scientificamerican.com/article.cfm?id=trying-to-forget&print=true

  27. Spaced-repetition#when-to-review

  28. http://www.bbc.co.uk/news/health-16086233

  29. ⁠, Woollett, Katherine Maguire, Eleanor A (2011):

    The last decade has seen a burgeoning of reports associating brain structure with specific skills and traits (e.g., [1-8]). Although these cross-sectional studies are informative, cause and effect are impossible to establish without longitudinal investigation of the same individuals before and after an intervention. Several longitudinal studies have been conducted (e.g., [9-18]); some involved children or young adults, potentially conflating brain development with learning, most were restricted to the motor domain, and all concerned relatively short timescales (weeks or months). Here, by contrast, we utilized a unique opportunity to study average-IQ adults operating in the real world as they learned, over four years, the complex layout of London’s streets while training to become licensed taxi drivers. In those who qualified, acquisition of an internal spatial representation of London was associated with a selective increase in gray matter (GM) volume in their posterior hippocampi and concomitant changes to their memory profile. No structural brain changes were observed in trainees who failed to qualify or control participants. We conclude that specific, enduring, structural brain changes in adult humans can be induced by biologically relevant behaviors engaging higher cognitive functions such as spatial memory, with significance for the “nature versus nurture” debate.

  30. http://www.indiana.edu/~abcwest/pmwiki/pdf/hertwig.thinkingpsypatterns.2003.pdf

  31. http://diyhpl.us/~bryan/papers2/neuro/The%20nature%20of%20individual%20differences%20in%20working%20memory%20capacity%20-%20active%20maintenance%20in%20primary%20memory%20and%20controlled%20search%20from%20secondary%20memory.pdf

  32. http://journals2005.pasteur.ac.ir/CELL/120(4).pdf

  33. 1989-phelan.pdf: “natural selection.pdf”

  34. 1992-berry.pdf

  35. 1998-ricklefs.pdf

  36. http://rstb.royalsocietypublishing.org/content/364/1522/1399.full

  37. Melatonin#fn42

  38. Replication

  39. https://www.overcomingbias.com/tag/medicine

  40. https://www.overcomingbias.com/2007/05/rand_health_ins.html

  41. https://www.overcomingbias.com/2007/06/disagreement_ca.html

  42. https://www.cato-unbound.org/2007/09/10/robin-hanson/cut-medicine-in-half/

  43. https://www.cato-unbound.org/issues/is-more-medicine-better/

  44. https://www.amazon.com/Overtreated-Medicine-Making-Sicker-Poorer/dp/1582345791/

  45. https://www.overcomingbias.com/2007/11/hospice-beats-h.html

  46. https://www.overcomingbias.com/2008/02/eternal-medicin.html

  47. https://www.overcomingbias.com/2008/05/beware-blood-tr.html

  48. https://www.overcomingbias.com/2008/09/beware-high-sta.html

  49. https://www.overcomingbias.com/2009/01/free-medicine-no-help-for-ghanaian-kids.html

  50. https://www.overcomingbias.com/2009/01/avoid-vena-cava-filters.html

  51. https://www.overcomingbias.com/2009/03/question-medical-findings.html

  52. https://www.overcomingbias.com/2009/04/medical-ideology.html

  53. https://www.overcomingbias.com/2009/07/meds-to-cut.html

  54. https://www.overcomingbias.com/2009/08/our-nutrition-ignorance.html

  55. https://www.overcomingbias.com/2009/12/wasted-cancer-hope.html

  56. https://www.overcomingbias.com/2010/02/africa-hiv-perverts-or-bad-med.html

  57. https://www.overcomingbias.com/2010/02/megan_on_med.html

  58. https://www.amazon.com/Facts-Dangerous-Half-Truths-Total-Nonsense/dp/1591398622

  59. https://www.overcomingbias.com/2010/03/hard-facts-med.html

  60. https://www.overcomingbias.com/2010/08/favoringfever.html

  61. https://www.overcomingbias.com/2010/08/death-panels-add-life.html

  62. https://www.overcomingbias.com/2011/02/how-med-harms.html

  63. https://www.overcomingbias.com/2011/02/the-bias-to-cut.html

  64. https://www.overcomingbias.com/2011/07/the-oregon-health-insurance-experiment.html

  65. https://www.overcomingbias.com/2011/05/beware-cancer-screens.html

  66. https://www.overcomingbias.com/2011/05/avoid-drugless-cancer-med.html

  67. https://www.overcomingbias.com/2011/11/forget-salt.html

  68. https://www.overcomingbias.com/2012/01/all-in-their-heads.html

  69. https://www.overcomingbias.com/2012/02/dont-torture-mom-dad.html

  70. https://www.overcomingbias.com/2012/02/dog-vs-cat-medicine.html

  71. https://www.overcomingbias.com/2012/02/farm-vs-pet-medicine.html

  72. https://www.overcomingbias.com/2013/09/16-of-us-deaths-from-hospital-errors.html

  73. http://www.gutenberg.org/files/9105/9105-h/9105-h.htm

  74. http://pcdb.santafe.edu/

  75. https://www.amazon.com/Rationality-Reflective-Mind-Keith-Stanovich/dp/0195341147/

  76. ⁠, Lars Penke, Jaap J. A. Denissen, Geoffrey F. Miller (2007-04-27):

    Genetic influences on personality differences are ubiquitous, but their nature is not well understood. A theoretical framework might help, and can be provided by evolutionary genetics.

    We assess three evolutionary genetic mechanisms that could explain genetic variance in personality differences: selective neutrality, mutation-selection balance, and balancing selection. Based on evolutionary genetic theory and empirical results from behaviour genetics and personality psychology, we conclude that selective neutrality is largely irrelevant, that mutation-selection balance seems best at explaining genetic variance in intelligence, and that balancing selection by environmental heterogeneity seems best at explaining genetic variance in personality traits. We propose a general model of heritable personality differences that conceptualises intelligence as fitness components and personality traits as individual reaction norms of genotypes across environments, with different fitness consequences in different environmental niches. We also discuss the place of mental health in the model.

    This evolutionary genetic framework highlights the role of gene-environment interactions in the study of personality, yields new insight into the person-situation-debate and the structure of personality, and has practical implications for both quantitative and molecular genetic studies of personality.

    [Keywords: evolutionary psychology, personality differences, behaviour genetics, intelligence, personality traits, gene-environment interactions, ⁠, mutation-selection balance, mutational cross-section, ⁠, frequency-dependent selection]

  77. https://www.nytimes.com/2009/01/11/magazine/11Genome-t.html?pagewanted=all

  78. ⁠, Davies, G. Tenesa, A. Payton, A. Yang, J. Harris, S. E Liewald, D. Ke, X. Le Hellard, S. Christoforou, A. Luciano, M. McGhee, K. Lopez, L. Gow, A. J Corley, J. Redmond, P. Fox, H. C Haggarty, P. Whalley, L. J McNeill, G. Goddard, M. E Espeseth, T. Lundervold, A. J Reinvang, I. Pickles, A. Steen, V. M Ollier, W. Porteous, D. J Horan, M. Starr, J. M Pendleton, N. Visscher, P. M Deary, I. J (2011):

    General intelligence is an important human quantitative trait that accounts for much of the variation in diverse cognitive abilities. Individual differences in intelligence are strongly associated with many important life outcomes, including educational and occupational attainments, income, health and lifespan. Data from twin and family studies are consistent with a high heritability of intelligence, but this inference has been controversial. We conducted a genome-wide analysis of 3511 unrelated adults with data on 549,692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits. We partitioned genetic variation on individual chromosomes and found that, on average, longer chromosomes explain more variation. Finally, using just SNP data we predicted ~1% of the variance of crystallized and fluid cognitive phenotypes in an independent sample (p = 0.009 and 0.028, respectively). Our results unequivocally confirm that a substantial proportion of individual differences in human intelligence is due to genetic variation, and are consistent with many genes of small effects underlying the additive genetic influences on intelligence.

  79. https://www.amazon.com/Science-Selection-Biological-Evolution-Philosophy/dp/0521644054/

  80. https://www.amazon.com/Not-Genes-Alone-Transformed-Evolution/dp/0226712125/

  81. https://www.nickbostrom.com/evolution.pdf

  82. http://www.chesterton.org/discover-chesterton/frequently-asked-questions/taking-a-fence-down/

  83. https://www.lesswrong.com/posts/pLRogvJLPPg6Mrvg4/an-alien-god

  84. https://www.lesswrong.com/posts/gDNrpuwahdRrDJ9iY/evolving-to-extinction

  85. https://www.lesswrong.com/posts/QsMJQSFj7WfoTMNgW/the-tragedy-of-group-selectionism

  86. Nicotine

  87. http://rstb.royalsocietypublishing.org/content/365/1544/1195.full

  88. http://darwin-online.org.uk/content/frameset?itemID=F1548.1&viewtype=text&pageseq=1

  89. https://www.lesswrong.com/posts/jAToJHtg39AMTAuJo/evolutions-are-stupid-but-work-anyway

  90. ⁠, Sisodiya, Sanjay M. Thompson, Pamela J. Need, Anna Harris, Sarah E. Weale, Michael E. Wilkie, Susan E. Michaelides, Michel Free, Samantha L. Walley, Nicole Gumbs, Curtis Gerrelli, Dianne Ruddle, Piers Whalley, Lawrence J. Starr, John M. Hunt, David M. Goldstein, David B. Deary, Ian J. Moore, Anthony T (2007):

    Background: The genetic basis of variation in human cognitive abilities is poorly understood. RIMS1 encodes a synapse active-zone protein with important roles in the maintenance of normal synaptic function: mice lacking this protein have greatly reduced learning ability and memory function.

    Objective: An established paradigm examining the structural and functional effects of mutations in genes expressed in the eye and the brain was used to study a kindred with an inherited retinal dystrophy due to RIMS1 mutation.

    Materials and Methods: Neuropsychological tests and high-resolution MRI brain scanning were undertaken in the kindred. In a population cohort, neuropsychological scores were associated with common variation in RIMS1. Additionally, RIMS1 was sequenced in top-scoring individuals. Evolution of RIMS1 was assessed, and its expression in developing human brain was studied.

    Results: Affected individuals showed significantly enhanced cognitive abilities across a range of domains. Analysis suggests that factors other than RIMS1 mutation were unlikely to explain enhanced cognition. No association with common variation and verbal IQ was found in the population cohort, and no other mutations in RIMS1 were detected in the highest scoring individuals from this cohort. RIMS1 protein is expressed in developing human brain, but RIMS1 does not seem to have been subjected to accelerated evolution in man.

    Conclusions: A possible role for RIMS1 in the enhancement of cognitive function at least in this kindred is suggested. Although further work is clearly required to explore these findings before a role for RIMS1 in human cognition can be formally accepted, the findings suggest that genetic mutation may enhance human cognition in some cases.

  91. 2002-baker.pdf

  92. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  93. 2012-rauch.pdf: “Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study”⁠, Anita Rauch, Dagmar Wieczorek, Elisabeth Graf, Thomas Wiel, Sabine Endele, Thomas Schwarzmayr, Beate Albrecht, Deborah Bartholdi, Jasmin Beygo, Nataliya Di Donato, Andreas Dufke, Kirsten Cremer, Maja Hempel, Denise Horn, Juliane Hoyer, Pascal Joset, Albrecht Röpke, Ute Moog, Angelika Riess, Christian T. ⁠, Andreas Tzschach, Antje Wiesener, Eva Wohlleber, Christiane Zweier, Arif B. Ekici, Alexander M. Zink, Andreas Rump, Christa Meisinger, Harald Grallert, Heinrich Sticht, Annette Schenck, Hartmut Engels, Gudrun Rappold, Evelin Schröck, Peter Wieacker, Olaf Riess, Thomas Meitinger, André Reis, Tim M. Strom

  94. 2014-fitzgerald.pdf

  95. http://repository.cshl.edu/30528/1/JC_June2014(6).pdf

  96. http://www.genetikum.de/images/PDF/Diverses/TIG-High-density-on-X-linked-genes.pdf

  97. 2014-iossifov.pdf: “The contribution of de novo coding mutations to autism spectrum disorder”⁠, Ivan Iossifov, Brian J. O’Roak, Stephan J. Sanders, Michael Ronemus, Niklas Krumm, Dan Levy, Holly A. Stessman, Kali T. Witherspoon, Laura Vives, Karynne E. Patterson, Joshua D. Smith, Bryan Paeper, Deborah A. Nickerson, Jeanselle Dea, Shan Dong, Luis E. Gonzalez, Jeffrey D. Mandell, Shrikant M. Mane, Michael T. Murtha, Catherine A. Sullivan, Michael F. Walker, Zainulabedin Waqar, Liping Wei, A. Jeremy Willsey, Boris Yamrom, Yoon-ha Lee, Ewa Grabowska, Ertugrul Dalkic, Zihua Wang, Steven Marks, Peter Andrews, Anthony Leotta, Jude Kendall, Inessa Hakker, Julie Rosenbaum, Beicong Ma, Linda Rodgers, Jennifer Troge, Giuseppe Narzisi, Seungtai Yoon, Michael C. Schatz, Kenny Ye, W. Richard McCombie, Jay Shendure, Evan E. Eichler, Matthew W. State, Michael Wigler

  98. 2014-rubeis.pdf: ⁠, Silvia De Rubeis, Xin He, Arthur P. Goldberg, Christopher S. Poultney, Kaitlin Samocha, A. Ercument Cicek, Yan Kou, Li Liu, Menachem Fromer, Susan Walker, Tarjinder Singh, Lambertus Klei, Jack Kosmicki, Shih-Chen Fu, Branko Aleksic, Monica Biscaldi, Patrick F. Bolton, Jessica M. Brownfeld, Jinlu Cai, Nicholas J. Campbell, Angel Carracedo, Maria H. Chahrour, Andreas G. Chiocchetti, Hilary Coon, Emily L. Crawford, Lucy Crooks, Sarah R. Curran, Geraldine Dawson, Eftichia Duketis, Bridget A. Fernandez, Louise Gallagher, Evan Geller, Stephen J. Guter, R. Sean Hill, Iuliana Ionita-Laza, Patricia Jimenez Gonzalez, Helena Kilpinen, Sabine M. Klauck, Alexander Kolevzon, Irene Lee, Jing Lei, Terho Lehtimäki, Chiao-Feng Lin, Avi Ma''ayan, Christian R. Marshall, Alison L. McInnes, Benjamin Neale, Michael J. Owen, Norio Ozaki, Mara Parellada, Jeremy R. Parr, Shaun Purcell, Kaija Puura, Deepthi Rajagopalan, Karola Rehnström, Abraham Reichenberg, Aniko Sabo, Michael Sachse, Stephan J. Sanders, Chad Schafer, Martin Schulte-Rüther, David Skuse, Christine Stevens, Peter Szatmari, Kristiina Tammimies, Otto Valladares, Annette Voran, Li-San Wang, Lauren A. Weiss, A. Jeremy Willsey, Timothy W. Yu, Ryan K. C. Yuen, the DDD Study, Mapping Collaborative for Autism, UKK Consortium, the Autism Sequencing Consortium, Edwin H. Cook, Christine M. Freitag, Michael Gill, Christina M. Hultman, Thomas Lehner, Aarno Palotie, Gerard D. Schellenberg, Pamela Sklar, Matthew W. State, James S. Sutcliffe, Christopher A. Walsh, Stephen W. Scherer, Michael E. Zwick, Jeffrey C. Barrett, David J. Cutler, Kathryn Roeder, Bernie Devlin, Mark J. Daly, Joseph D. Buxbaum (2014-10-29; algernon):

    The genetic architecture of autism spectrum disorder involves the interplay of common and rare variation and their impact on hundreds of genes.

    Using exome sequencing, analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, and a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects.

    Many of the genes implicated encode proteins for synaptic, transcriptional, and chromatin remodeling pathways. These include voltage-gated ion channels regulating propagation of action potentials, pacemaking, and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodelers, prominently histone post-translational modifications involving lysine methylation/​​​​demethylation.

  99. 2015-yuen.pdf

  100. https://www.lesswrong.com/posts/XPErvb8m9FapXCjhA/adaptation-executers-not-fitness-maximizers

  101. ⁠, Lynch, Gary Palmer, Linda C. Gall, Christine M (2011):

    Whether drugs that enhance cognition in healthy individuals will appear in the near future has become a topic of considerable interest. We address this possibility using a three variable system (psychological effect, neurobiological mechanism, and efficiency vs. capabilities) for classifying candidates. Ritalin and modafinil, two currently available compounds, operate on primary psychological states that in turn affect cognitive operations (attention and memory), but there is little evidence that these effects translate into improvements in complex cognitive processing. A second category of potential enhancers includes agents that improve memory encoding, generally without large changes in primary psychological states. Unfortunately, there is little information on how these compounds affect cognitive performance in standard psychological tests. Recent experiments have identified a number of sites at which memory drugs could, in principle, manipulate the cell biological systems underlying the learning-related long-term potentiation (LTP) effect; this may explain the remarkable diversity of memory promoting compounds. Indeed, many of these agents are known to have positive effects on LTP. A possible third category of enhancement drugs directed specifically at integrated cognitive operations is nearly empty. From a neurobiological perspective, two plausible candidate classes have emerged that both target the fast excitatory transmission responsible for communication within cortical networks. One acts on nicotinic receptors (alpha7 and alpha4) that regulate release of the neurotransmitter glutamate while the other (‘ampakines’) allosterically modulates the glutamate receptors mediating the post-synaptic response (EPSCs). Brain imaging in primates has shown that ampakines expand cortical networks engaged by a complex task; coupled with behavioral data, these findings provide evidence for the possibility of generating new cognitive capabilities. Finally, we suggest that continuing advances in behavioral sciences provide new opportunities for translational work, and that discussions of the social impact of cognitive enhancers have failed to consider the distinction between effects on efficiency vs. new capabilities.

  102. http://strdu.com/human_longevity_vs_mammals.html

  103. http://www.sjsu.edu/faculty/watkins/longevity.htm

  104. http://www.sciencenews.org/view/generic/id/69610/title/Adaptive_no_more

  105. Melatonin

  106. #melatonin

  107. 2012-woodley.pdf: “The social and scientific temporal correlates of genotypic intelligence and the Flynn effect”⁠, Michael A. Woodley

  108. http://www.psychologytoday.com/files/attachments/56143/the-flynn-effect-puzzle_0.pdf

  109. http://scialert.net/fulltext/?doi=pjbs.2008.1398.1400

  110. http://jn.nutrition.org/content/131/2/649S.full.pdf+html

  111. https://www.lesswrong.com/posts/EQkELCGiGQwvrrp3L/growing-up-is-hard

  112. 1981-bartus.pdf

  113. http://onesci.com/journals/science_journal_87.pdf

  114. http://onesci.com/journals/science_journal_90.pdf

  115. http://www.fasebj.org/content/18/3/545.full

  116. http://www.pediatricsdigest.mobi/content/111/1/e39.full

  117. 2008-helland.pdf: ⁠, Ingrid B. Helland, Lars Smith, Birgitta Blomén, Kristin Saarem, Ola D. Saugstad, Christian A. Drevon (2008-08-01; nootropic):

    Objectives: Arachidonic acid (20:4n-6) and docosahexaenoic acid (22:6n-3) are essential for brain growth and cognitive development. We have reported that supplementing pregnant and lactating women with n-3 very-long-chain polyunsaturated fatty acids promotes higher IQ scores at 4 years of age as compared with maternal supplementation with n-6 polyunsaturated fatty acids. In our present study, the children were examined at 7 years of age with the same cognitive tests as at 4 years of age. We also examined the relation between plasma fatty acid pattern and in children, because an association between arachidonic acid and adipose tissue size has been suggested.

    Methods: The study was randomized and double-blinded. The mothers took 10 mL of cod liver oil or corn oil from week 18 of pregnancy until 3 months after delivery. Their children were tested with the Kaufman Assessment Battery for Children at 7 years of age, and their height and weight were measured.

    Results: We did not find any statistically-significant differences in scores on the Kaufman Assessment Battery for Children test at 7 years of age between children whose mothers had taken cod liver oil (n = 82) or corn oil (n = 61). We observed, however, that maternal plasma phospholipid concentrations of α-linolenic acid (18:3n-3) and docosahexaenoic acid during pregnancy were correlated to sequential processing at 7 years of age. We observed no correlation between fatty acid status at birth or during the first 3 months of life and BMI at 7 years of age.

    Conclusion: This study suggests that maternal concentration of n-3 very-long-chain polyunsaturated fatty acids during pregnancy might be of importance for later cognitive function, such as sequential processing, although we observed no statistically-significant effect of n-3 fatty acid intervention on global IQs. Neonatal fatty acid status had no influence on BMI at 7 years of age.

  118. Modafinil

  119. Modafinil#side-effects

  120. https://www.erowid.org/references/refs_view.php?A=ShowDocPartFrame&ID=6624&DocPartID=6148

  121. 2003-turner.pdf

  122. 2005-waters.pdf

  123. 2007-morgan.pdf

  124. 2002-beracochea.pdf

  125. 2004-baranski.pdf

  126. http://www-bmu.psychiatry.cam.ac.uk/publications/muller04eff.pdf

  127. 2008-minzenberg.pdf

  128. 2003-beracochea.pdf

  129. ⁠, Shuman, Tristan Wood, Suzanne C. Anagnostaras, Stephan G (2009):

    Modafinil has been shown to promote wakefulness and some studies suggest the drug can improve cognitive function. Because of many similarities, the mechanism of action may be comparable to classical psychostimulants, although the exact mechanisms of modafinil’s actions in wakefulness and cognitive enhancement are unknown. The current study aims to further examine the effects of modafinil as a cognitive enhancer on hippocampus-dependent memory in mice. A high dose of modafinil (75 mg/​​​​kg ip) given before training improved acquisition on a Morris water maze. When given only before testing, modafinil did not affect water maze performance. We also examined modafinil (0.075 to 75 mg/​​​​kg) on Pavlovian fear conditioning. A low dose of pretraining modafinil (0.75 mg/​​​​kg) enhanced memory of contextual fear conditioning (tested off-drug 1 week later) whereas a high dose (75 mg/​​​​kg) disrupted memory. Pretraining modafinil did not affect cued conditioning at any dose tested, and immediate posttraining modafinil had no effect on either cued or contextual fear. These results suggest that modafinil’s effects of memory are more selective than amphetamine or cocaine and specific to hippocampus-dependent memory.

  130. 2004-ward.pdf

  131. Nootropics#armodafinil

  132. Modafinil#tolerance

  133. ⁠, Gilestro, Giorgio F. Tononi, Giulio Cirelli, Chiara (2009):

    Sleep is universal, strictly regulated, and necessary for cognition. Why this is so remains a mystery, although recent work suggests that sleep, memory, and plasticity are linked. However, little is known about how wakefulness and sleep affect synapses. Using Western blots and confocal microscopy in ⁠, we found that protein levels of key components of central synapses were high after waking and low after sleep. These changes were related to behavioral state rather than time of day and occurred in all major areas of the Drosophila brain. The decrease of synaptic markers during sleep was progressive, and sleep was necessary for their decline. Thus, sleep may be involved in maintaining synaptic homeostasis altered by waking activities.

  134. http://citeseerx.ist.psu.edu/viewdoc/download?doi=

  135. 2006-tononi.pdf: ⁠, Giulio Tononi, Chiara Cirelli (2006-02-01; zeo):

    This paper reviews a novel hypothesis about the functions of slow wave sleep—the synaptic homeostasis hypothesis. According to the hypothesis, plastic processes occurring during wakefulness result in a net increase in synaptic strength in many brain circuits. The role of sleep is to downscale synaptic strength to a baseline level that is energetically sustainable, makes efficient use of gray matter space, and is beneficial for learning and memory. Thus, sleep is the price we have to pay for plasticity, and its goal is the homeostatic regulation of the total synaptic weight impinging on neurons. The hypothesis accounts for a large number of experimental facts, makes several specific predictions, and has implications for both sleep and mood disorders.

    [Keywords: Long-term depression, Synaptic scaling, Learning, Consolidation, Delta sleep, Slow waves, Slow oscillation]

  136. #gilestro-et-al-2009

  137. ⁠, Vyazovskiy, Vladyslav V. Olcese, Umberto Lazimy, Yaniv M. Faraguna, Ugo Esser, Steve K. Williams, Justin C. Cirelli, Chiara Tononi, Giulio (2009):

    The need to sleep grows with the duration of wakefulness and dissipates with time spent asleep, a process called sleep homeostasis. What are the consequences of staying awake on brain cells, and why is sleep needed? Surprisingly, we do not know whether the firing of cortical neurons is affected by how long an animal has been awake or asleep. Here, we found that after sustained wakefulness cortical neurons fire at higher frequencies in all behavioral states. During early NREM sleep after sustained wakefulness, periods of population activity (ON) are short, frequent, and associated with synchronous firing, while periods of neuronal silence are long and frequent. After sustained sleep, firing rates and synchrony decrease, while the duration of ON periods increases. Changes in firing patterns in NREM sleep correlate with changes in slow-wave activity, a marker of sleep homeostasis. Thus, the systematic increase of firing during wakefulness is counterbalanced by staying asleep.

  138. http://www.phys.mcw.edu/documents/Special%20Topics%20Neuroscience%20Fall%202008/Week%205/nat-neuro_2008_nrems-plasticity.pdf

  139. http://www.biomedcentral.com/content/pdf/1471-2202-13-S1-O6.pdf

  140. 2012-bom.pdf: “Sleep to Upscale, Sleep to Downscale: Balancing Homeostasis and Plasticity”⁠, Jan Born, Gordon B. Feld

  141. 2012-chauvette.pdf: “Sleep Oscillations in the Thalamocortical System Induce Long-Term Neuronal Plasticity”⁠, Sylvain Chauvette, Josée Seigneur, Igor Timofeev

  142. 1993-everson.pdf

  143. http://postcog.ucd.ie/files/Everson.pdf

  144. http://ajpregu.physiology.org/content/278/4/R905.full

  145. 1995-rechtschaffen.pdf

  146. 1996-bergmann.pdf

  147. http://ajpregu.physiology.org/content/280/2/R602.full

  148. http://www.journalsleep.org/Articles/250104.pdf

  149. ⁠, Jane Bradbury (2005-03-15):

    As a species, we pride ourselves on the uniqueness of our brain. Relative to our body size, the human brain is bigger than that of any other animal. It may also contain unique structures and patterns of organisation that presumably underlie our intelligence and ability to manipulate our environment. But how did our unique brain originate, and under what selective pressures did it evolve? Some of the answers may lie in the genetic differences that researchers are now uncovering between us and our closest relatives.

    ToC: Costs and Benefits · The Genetics of Human Brain Evolution · Enter · Gene Expression · Scratching at the Surface · Further Reading

  150. http://www.scientificamerican.com/article.cfm?id=why-does-the-brain-need-s

  151. https://www.discovermagazine.com/2011/jul-aug/06-body-fit-for-freaky-big-brain

  152. http://www.replicatedtypo.com/what-makes-humans-unique-i-the-evolution-of-the-human-brain/1372.html

  153. 1995-aiello.pdf: ⁠, Leslie C. Aiello, Peter Wheeler (1995; algernon):

    Brain tissue is metabolically expensive, but there is no statistically-significant correlation between relative basal metabolic rate and relative brain size in humans and other encephalized mammals. The expensive-tissue hypothesis suggests that the metabolic requirements of relatively large brains are offset by a corresponding reduction of the gut. The splanchnic organs (liver and gastro-intestinal tract) are as metabolically expensive as brains, and the gut is the only one of the metabolically expensive organs in the human body that is markedly small in relation to body size. Gut size is highly correlated with diet, and relatively small guts are compatible only with high-quality, easy-to-digest food. The often-cited relationship between diet and relative brain size is more properly viewed as a relationship between relative brain size and relative gut size, the latter being determined by dietary quality. No matter what is selecting for relatively large brains in humans and other primates, they cannot be achieved without a shift to a high-quality diet unless there is a rise in the metabolic rate. Therefore the incorporation of increasingly greater amounts of animal products into the diet was essential in the evolution of the large human brain.

  154. http://www.neuroscienceschool.ku.dk/cbfm_literature/attwell%20laughlin.pdf

  155. ⁠, Leslie C. Aiello, Jonathan C. K. Wells (2002-10):

    The genus Homo as represented by Homo ergaster (= early African Homo erectus) is characterized by a pattern of features that is more similar to modern humans than to the earlier and contemporaneous australopithecines and paranthropines. These features include larger relative brain sizes, larger bodies, slower rates of growth and maturation, dedicated bipedal locomotion, and smaller teeth and jaws. These features are phenotypic expressions of a very different lifestyle for the earliest members of the genus Homo. This paper considers the energetic correlates of the emergence of the genus Homo and suggests that there were three major changes in maintenance energy requirements. First, there was an absolute increase in energy requirements due to greater body size. Second, there was a shift in the relative requirements of the different organs, with increased energy diverted to brain metabolism at the expense of gut tissue, possibly mediated by changes in the proportion of weight comprised of fat. And third, there was a slower rate of childhood growth, offset by higher growth costs during infancy and adolescence. These changes, as well as energetic requirements of reproduction and bipedal locomotion, are considered in a discussion of one of the major transitions in adaptation in human evolution, the appearance of our own genus.

    [Keywords: human evolution; metabolic rate; diet; growth; Homo erectus; Homo ergaster; australopithecines; brain evolution.]

  156. http://herd.typepad.com/herd_the_hidden_truth_abo/files/Dunbar_etal_2007.pdf

  157. 2007-wittman.pdf

  158. 2011-lynch.pdf: “The likelihood of cognitive enhancement”⁠, Gary Lynch, Linda C. Palmer, Christine M. Gall

  159. http://www-psych.stanford.edu/~knutson/bad/semendeferi01.pdf

  160. 2001-finlay.pdf: ⁠, Barbara L. Finlay, Richard B. Darlington, Nicholas Nicastro (2001-04-01; algernon):

    How does evolution grow bigger brains? It has been widely assumed that growth of individual structures and functional systems in response to niche-specific cognitive challenges is the most plausible mechanism for brain expansion in mammals. Comparison of multiple regressions on allometric data for 131 mammalian species, however, suggests that for 9 of 11 brain structures taxonomic and body size factors are less important than covariance of these major structures with each other. Which structure grows biggest is largely predicted by a conserved order of neurogenesis that can be derived from the basic axial structure of the developing brain. This conserved order of neurogenesis predicts the relative scaling not only of gross brain regions like the isocortex or mesencephalon, but also the level of detail of individual thalamic nuclei. Special selection of particular areas for specific functions does occur, but it is a minor factor compared to the large-scale covariance of the whole brain. The idea that enlarged isocortex could be a “spandrel”, a by-product of structural constraints later adapted for various behaviors, contrasts with approaches to selection of particular brain regions for cognitively advanced uses, as is commonly assumed in the case of hominid brain evolution.

    [Keywords: allometry, brain size, cortex, development, heterochrony, hominid evolution, limbic system, neurogenesis]

  161. 2012-herculanohouzel.pdf: ⁠, Suzana Herculano-Houzel (2012-06-19; psychology):

    [] Neuroscientists have become used to a number of “facts” about the human brain: It has 100 billion neurons and 10- to 50-fold more glial cells; it is the largest-than-expected for its body among primates and mammals in general, and therefore the most cognitively able; it consumes an outstanding 20% of the total body energy budget despite representing only 2% of body mass because of an increased metabolic need of its neurons; and it is endowed with an overdeveloped cerebral cortex, the largest compared with brain size.

    These facts led to the widespread notion that the human brain is literally extraordinary: an outlier among mammalian brains, defying evolutionary rules that apply to other species, with a uniqueness seemingly necessary to justify the superior cognitive abilities of humans over mammals with even larger brains. These facts, with deep implications for neurophysiology and evolutionary biology, are not grounded on solid evidence or sound assumptions, however.

    Our recent development of a method that allows rapid and reliable quantification of the numbers of cells that compose the whole brain has provided a means to verify these facts. Here, I review this recent evidence and argue that, with 86 billion neurons and just as many nonneuronal cells, the human brain is a scaled-up primate brain in its cellular composition and metabolic cost, with a relatively enlarged cerebral cortex that does not have a relatively larger number of brain neurons yet is remarkable in its cognitive abilities and metabolism simply because of its extremely large number of neurons.

  162. http://www.intelligence.org/files/HowHardIsAI.pdf

  163. http://www-personal.umich.edu/~wolpoff/Papers/Brain%20Size.pdf

  164. http://newswatch.nationalgeographic.com/2008/09/09/neanderthal/?q=/2008/09/neanderthal.html

  165. 1998-henneberg.pdf

  166. https://www.discovermagazine.com/2010/sep/25-modern-humans-smart-why-brain-shrinking

  167. https://www.pnas.org/content/101/30/10895.full.pdf+html

  168. http://firecenter.berkeley.edu/docs/bowmanetalscience.pdf

  169. http://www.iee.unibe.ch/unibe/philnat/biology/zoologie/content/e7493/e7854/e355359/e373691/KotrschalCurrentBiol2013.pdf

  170. https://www.nytimes.com/2012/05/22/science/why-are-chimps-stronger-than-humans.html

  171. https://www.discovermagazine.com/2011/jul-aug/06-body-fit-for-freaky-big-brain/article_view?b_start:int=1&-C=

  172. http://harpending.humanevo.utah.edu/Documents/ashkiq.webpub.pdf

  173. DNB-FAQ#aging

  174. https://www.pnas.org/content/early/2011/07/20/1016709108

  175. ⁠, Charles H. Lineweaver (2007-11-12):

    We critically examine the evidence for the idea that encephalization quotients increase with time. We find that human-like intelligence is not a convergent feature of evolution. Implications for the search for extraterrestrial intelligence are discussed.

  176. 1964-simpson.pdf

  177. http://news.harvard.edu/gazette/story/2010/10/thinking-like-an-octopus/

  178. 2009-ibanez.pdf

  179. https://www.overcomingbias.com/2011/07/whence-better-brains.html

  180. http://repository.kulib.kyoto-u.ac.jp/dspace/bitstream/2433/147262/1/j.cub.2011.07.019.pdf

  181. https://www.cell.com/current-biology/pdf/S0960-9822(07)02088-X.pdf

  182. 2009-silberberg.pdf

  183. 2010-cook.pdf

  184. http://www.replicatedtypo.com/super-smart-animals/4635.html

  185. https://slate.com/articles/health_and_science/science/2009/02/how_strong_is_a_chimpanzee.single.html

  186. ⁠, Soto, Paula C. Stein, Lance L. Hurtado-Ziola, Nancy Hedrick, Stephen M. Varki, Ajit (2010):

    Although humans and chimpanzees share >99% identity in alignable protein sequences, they differ surprisingly in the incidence and severity of some common diseases. In general, humans infected with various viruses, such as HIV and hepatitis C virus, appear to develop stronger reactions and long-term complications. Humans also appear to suffer more from other diseases associated with over-reactivity of the adaptive immune system, such as asthma, psoriasis, and rheumatoid arthritis. In this study, we show that human T cells are more reactive than chimpanzee T cells to a wide variety of stimuli, including anti-TCR Abs of multiple isotypes, l-phytohemagglutin, Staphylococcus aureus superantigen, a superagonist anti-CD28 Ab, and in MLRs. We also extend this observation to B cells, again showing a human propensity to react more strongly to stimuli. Finally, we show a relative increase in activation markers and cytokine production in human lymphocytes in response to uridine-rich (viral-like) ssRNA. Thus, humans manifest a generalized lymphocyte over-reactivity relative to chimpanzees, a finding that is correlated with decreased levels of inhibitory sialic acid-recognizing Ig-superfamily lectins (Siglecs; particularly Siglec-5) on human T and B cells. Furthermore, Siglec-5 levels are upregulated by activation in chimpanzee but not human lymphocytes, and human T cell reactivity can be downmodulated by forced expression of Siglec-5. Thus, a key difference in the immune reactivity of chimp and human lymphocytes appears to be related to the differential expression of Siglec-5. Taken together, these data may help explain human propensities for diseases associated with excessive activation of the adaptive immune system.

  187. ⁠, Stephen Budiansky (NYT) (1998-12-13):

    [Excerpts from If a Lion Could Talk: Animal Intelligence and the Evolution of Consciousness⁠, Budiansky 1998 (ISBN 0684837102).]

    How many of us have caught ourselves gazing into the eyes of a pet, wondering what thoughts lie behind those eyes? Or fallen into an argument over which is smarter, the dog or the ? Scientists have conducted elaborate experiments trying to ascertain whether animals from chimps to pigeons can communicate, count, reason, or even lie. So does science tell us what we assume—that animals are pretty much like us, only not as smart? Simply, no. Now, in this superb book, Stephen Budiansky poses the fundamental question: “What is intelligence?” His answer takes us on the ultimate wildlife adventure to animal consciousness. Budiansky begins by exposing our tendency to see ourselves in animals. Our anthropomorphism allows us to perceive intelligence only in behavior that mimics our own. This prejudice, he argues, betrays a lack of imagination. Each species is so specialized that most of their abilities are simply not comparable. At the mercy of our anthropomorphic tendencies, we continue to puzzle over pointless issues like whether a wing or an arm is better, or whether night vision is better than day vision, rather than discovering the real world of a winged nighthawk, a thoroughbred horse, or an African lion. Budiansky investigates the sometimes bizarre research behind animal intelligence experiments: from horses who can count or ace history quizzes, and primates who seem fluent in sign language, to rats who seem to have become self-aware, he reveals that often these animals are responding to our tiny unconscious cues. And, while critically discussing scientists’ interpretations of animal intelligence, he is able to lay out their discoveries in terms of what we know about ourselves. For instance, by putting you in the minds of dogs or bees who travel by dead reckoning, he demonstrates that this is also how you find your way down a familiar street with almost no conscious awareness of your navigation system. Modern cognitive science and the new science of evolutionary ecology are beginning to show that thinking in animals is tremendously complex and wonderful in its variety. A pigeon’s ability to find its way home from almost anywhere has little to do with comparative intelligence; rather it is due to the pigeon’s very different perception of the world. That’s why, as said, “If a lion could talk, we would not understand him.” In this fascinating book, Budiansky frees us from the shackles of our ideas about the natural world, and opens a window to the astounding worlds of the animals that surround us.

  188. https://www.nytimes.com/2011/12/23/science/pigeons-can-learn-higher-math-as-well-as-monkeys-study-suggests.html

  189. 2011-scarf.pdf

  190. ⁠, Herbranson, Walter T. Schroeder, Julia (2010):

    The “Monty Hall Dilemma” (MHD) is a well known probability puzzle in which a player tries to guess which of three doors conceals a desirable prize. After an initial choice is made, one of the remaining doors is opened, revealing no prize. The player is then given the option of staying with their initial guess or switching to the other unopened door. Most people opt to stay with their initial guess, despite the fact that switching doubles the probability of winning. A series of experiments investigated whether pigeons (Columba livia), like most humans, would fail to maximize their expected winnings in a version of the MHD. Birds completed multiple trials of a standard MHD, with the three response keys in an operant chamber serving as the three doors and access to mixed grain as the prize. Across experiments, the probability of gaining reinforcement for switching and staying was manipulated, and birds adjusted their probability of switching and staying to approximate the optimal strategy. Replication of the procedure with human participants showed that humans failed to adopt optimal strategies, even with extensive training.

  191. 2012-klein.pdf

  192. http://news.bbc.co.uk/2/hi/uk_news/magazine/3270029.stm

  193. 2006-abma.pdf

  194. 2009-copplestone.pdf: “Does Having Children Create Happiness?”⁠, Samantha Copplestone, Patrick Dempsey, Alexa Hynes, Paul Hynes

  195. http://www.ditext.com/diamond/mistake.html

  196. http://library.mpib-berlin.mpg.de/ft/rh/RH_More_2003.pdf

  197. http://www.edge.org/3rd_culture/pinker07/pinker07_index.html

  198. https//www.edge.org/conversation/mc2011-history-violence-pinker

  199. https://www.amazon.com/Better-Angels-Our-Nature-Violence/dp/0143122010/

  200. https://www.nytimes.com/2001/01/21/magazine/experiencing-ecstasy.html

  201. https://www.amazon.com/Little-Book-Talent-Improving-Skills/dp/034553025X/

  202. http://www.google.com/search?q=%22rejection%20therapy%22%20site%3Alesswrong.com

  203. https://www.guilford.com/books/Handbook-of-Psychopathy/Christopher-Patrick/9781593855918/reviews

  204. 2006-harris.pdf

  205. https://www.lesswrong.com/posts/FDyMThqqX2s47e6rG/open-thread-may-2010?commentId=W77y6fzdLwJyJjqsB

  206. http://andrewducker.livejournal.com/2668970.html

  207. https://news.ycombinator.com/item?id=4201233

  208. https://news.ycombinator.com/item?id=6521885

  209. https://old.reddit.com/r/Nootropics/comments/1go60w/algernons_law/

  210. https://old.reddit.com/r/science/comments/1gnz8y/algernons_law_can_we_artificially_increase_iq/

  211. https://old.reddit.com/r/Nootropics/comments/1o8akq/algernons_law/

  212. https://old.reddit.com/r/Nootropics/comments/33w3mm/the_algernon_argument/

  213. https//www.edge.org/conversation/-brains-plus-brawn

  214. https://www.smbc-comics.com/?id=3169#comic

  215. https://slatestarcodex.com/2014/03/01/searching-for-one-sided-tradeoffs/

  216. https://slatestarcodex.com/2014/03/03/do-life-hacks-ever-reach-fixation/

  217. https://www.frontiersin.org/Journal/10.3389/fnsys.2014.00152/full

  218. https://www.lesswrong.com/posts/9Yc7Pp7szcjPgPsjf/the-brain-as-a-universal-learning-machine

  219. http://aleph.se/andart2/neuroscience/energetics-of-the-brain-and-ai/

  220. http://ecodevoevo.blogspot.com/2015/03/the-obstetric-dilemma-hypothesis.html

  221. https://www.cell.com/current-biology/abstract/S0960-9822%2815%2900958-6

  222. https://www.nature.com/neuro/journal/v1/n1/full/nn0598_36.html

  223. 2015-hofman.pdf: ⁠, Michel A. Hofman (2015; iq):

    Design principles and operational modes are explored that underlie the information processing capacity of the human brain.

    The hypothesis is put forward that in higher organisms, especially in primates, the complexity of the neural circuitry of the cerebral cortex is the neural correlate of the brain’s coherence and predictive power, and, thus, a measure of intelligence. It will be argued that with the evolution of the human brain we have nearly reached the limits of biological intelligence.

    [Keywords: biological intelligence, cognition, consciousness, cerebral cortex, primates, information processing, neural networks, cortical design, human brain evolution]

  224. https://www.bbc.com/earth/story/20150424-animals-that-lost-their-brains

  225. http://rspb.royalsocietypublishing.org/content/284/1851/20162562

  226. ⁠, J. Jeffrey Morris, Richard E. Lenski, Erik R. Zinser (2012-03-23):

    Reductive genomic evolution, driven by ⁠, is common in endosymbiotic bacteria. Genome reduction is less common in free-living organisms, but it has occurred in the numerically dominant open-ocean bacterioplankton Prochlorococcus and “Candidatus Pelagibacter”, and in these cases the reduction appears to be driven by rather than drift. Gene loss in free-living organisms may leave them dependent on co-occurring microbes for lost metabolic functions. We present the Black Queen Hypothesis (BQH), a novel theory of reductive evolution that explains how selection leads to such dependencies; its name refers to the queen of spades in the game Hearts, where the usual strategy is to avoid taking this card. Gene loss can provide a selective advantage by conserving an organism’s limiting resources, provided the gene’s function is dispensable. Many vital genetic functions are leaky, thereby unavoidably producing public goods that are available to the entire community. Such leaky functions are thus dispensable for individuals, provided they are not lost entirely from the community. The BQH predicts that the loss of a costly, leaky function is selectively favored at the individual level and will proceed until the production of public goods is just sufficient to support the equilibrium community; at that point, the benefit of any further loss would be offset by the cost. Evolution in accordance with the BQH thus generates “beneficiaries” of reduced genomic content that are dependent on leaky “helpers”, and it may explain the observed nonuniversality of prototrophy, stress resistance, and other cellular functions in the microbial world.