Using macroevolutionary patterns to distinguish primary from secondary cognitive modules in primate cross-species performance data on five cognitive ability measures.

Michael Woodley of Menie, Mateo Peñaherrera-Aguirre, JohnMichael Jurgensen

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    Species-level data on five cognitive ability measures from 69 primate species are used in conjunction with comparative phylogenetic methods to test for the existence of primary and secondary modules. The former are ‘hard wired’, and solve phylogenetically recurrent problems, whereas the latter are a function of domain general problem-solving mechanisms being applied to solving narrower problems, which yields the ability to spontaneously solve those problems once the solutions are learned. It is found that these abilities exhibit affinities for different macroevolutionary patterns relative to ‘Big G’, and positive associations with dietary breadth and brain size. The analyses were also conducted using each ability residualised for G. It was found that the Ornstein-Uhlenbeck (OU) model best captured the macroevolution of residual tactical deception, and White Noise (WN) best fit the remainder. Residual tactical deception positively associates with brain volume, whereas the extractive foraging and innovation residuals negatively associate with this and the innovation residual negatively associates with social group size. The affinity of residual tactical deception for the OU model indicates that it may be a primary module under adaptive optimization selection. The predominance of WN in characterizing the macroevolution of the remaining residuals indicates that they may be secondary modules, whose development is influenced by developmental and ecological (rather than phylogenetic) factors. Negative associations involving brain size (in two cases) and social group size
    (in one) suggest that the optimal conditions for cultivating these modules exist when these parameters are low.
    Original languageEnglish
    Article number101645
    Number of pages11
    JournalIntelligence
    Volume92
    DOIs
    Publication statusPublished - 1 May 2022

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