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An intraspecific appraisal of the social intelligence hypothesis.

ABSTRACT: The prevailing hypotheses for the evolution of cognition focus on either the demands associated with group living (the social intelligence hypothesis (SIH)) or ecological challenges such as finding food. Comparative studies testing these hypotheses have generated highly conflicting results; consequently, our understanding of the drivers of cognitive evolution remains limited. To understand how selection shapes cognition, research must incorporate an intraspecific approach, focusing on the causes and consequences of individual variation in cognition. Here, we review the findings of recent intraspecific cognitive research to investigate the predictions of the SIH. Extensive evidence from our own research on Australian magpies (Cracticus tibicen dorsalis), and a number of other taxa, suggests that individuals in larger social groups exhibit elevated cognitive performance and, in some cases, elevated reproductive fitness. Not only do these findings demonstrate how the social environment has the potential to shape cognitive evolution, but crucially, they demonstrate the importance of considering both genetic and developmental factors when attempting to explain the causes of cognitive variation.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

SUBMITTER: Ashton BJ

PROVIDER: S-EPMC6107571 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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An intraspecific appraisal of the social intelligence hypothesis.

Ashton Benjamin J BJ Thornton Alex A Ridley Amanda R AR

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20180901 1756

The prevailing hypotheses for the evolution of cognition focus on either the demands associated with group living (the social intelligence hypothesis (SIH)) or ecological challenges such as finding food. Comparative studies testing these hypotheses have generated highly conflicting results; consequently, our understanding of the drivers of cognitive evolution remains limited. To understand how selection shapes cognition, research must incorporate an intraspecific approach, focusing on the causes ...[more]

PMID: 30104433

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Project description:Phylogenetic comparative methods have become standard for investigating evolutionary hypotheses, including in studies of human evolution. While these methods account for the non-independence of trait data due to phylogeny, they often fail to consider intraspecific variation, which may lead to biased or erroneous results. We assessed the degree to which intraspecific variation impacts the results of comparative analyses by investigating the "social brain" hypothesis, which has provided a framework for explaining complex cognition and large brains in humans. This hypothesis suggests that group life imposes a cognitive challenge, with species living in larger social groups having comparably larger neocortex ratios than those living in smaller groups. Primates, however, vary considerably in group size within species, a fact that has been ignored in previous analyses. When within-species variation in group size is high, the common practice of using a mean value to represent the species may be inappropriate. We conducted regression and resampling analyses to ascertain whether the relationship between neocortex ratio and group size across primate species persists after controlling for within-species variation in group size. We found that in a sample of 23 primates, 70% of the variation in group size was due to between-species variation. Controlling for within-species variation in group size did not affect the results of phylogenetic analyses, which continued to show a positive relationship between neocortex ratio and group size. Analyses restricted to non-monogamous primates revealed considerable intraspecific variation in group size, but the positive association between neocortex ratio and group size remained even after controlling for within-species variation in group size. Our findings suggest that the relationship between neocortex size and group size in primates is robust. In addition, our methods and associated computer code provide a way to assess and account for intraspecific variation in other comparative analyses of primate evolution.

Project description:BackgroundResearch on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC.ObjectiveWe intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings.MethodsWe conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data.ResultsWe retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%).ConclusionsWe observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Dataset Information

An intraspecific appraisal of the social intelligence hypothesis.

Publications

An intraspecific appraisal of the social intelligence hypothesis.

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