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  • 1964-simpson.pdf (backlinks)

  • 1989-phelan.pdf: “natural selection.pdf” (backlinks)

  • 1992-berry.pdf (backlinks)

  • 1993-everson.pdf (backlinks)

  • 1995-aiello.pdf: ⁠, Leslie C. Aiello, Peter Wheeler (1995; backlinks):

    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.

  • 1995-rechtschaffen.pdf (backlinks)

  • 1996-bergmann.pdf (backlinks)

  • 1998-henneberg.pdf (backlinks)

  • 1998-ricklefs.pdf (backlinks)

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

    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]

  • 2002-beracochea.pdf (backlinks)

  • 2003-beracochea.pdf (backlinks)

  • 2004-ward.pdf (backlinks)

  • 2005-waters.pdf (backlinks)

  • 2006-abma.pdf (backlinks)

  • 2006-harris.pdf (backlinks)

  • 2007-morgan.pdf (backlinks)

  • 2007-wittman.pdf (backlinks)

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

  • 2009-ibanez.pdf (backlinks)

  • 2009-silberberg.pdf (backlinks)

  • 2010-cook.pdf (backlinks)

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

    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]

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

  • 2011-scarf.pdf (backlinks)

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

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

  • 2012-klein.pdf (backlinks)

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

  • 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 (backlinks)

  • 2014-kuzawa.pdf (backlinks)

  • 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, Homozygosity 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; backlinks):

    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.

  • 2015-yuen.pdf (backlinks)