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General Intelligence as a Major Source of Cognitive Variation Among Individuals of Three Species of Lemur, Uniting g with G

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Abstract

A significant open issue in the field of comparative psychology is the apparent inability to reconcile the existence of ‘little g’ (general intelligence) common factor variance among cognitive performance data involving individuals within species, with the existence of higher-level ‘Big G’ factor variance among species-level cognitive aggregates. Here, using a cognitive individual differences dataset of three Lemur species (grey mouse lemur; Microcebus murinus, ruffed lemur, Varecia variegata, and ring-tailed lemur, Lemur catta), we replicate a previously published solution to this problem. This is based on the hypothesis that there does exist g or g-like variance that is predictive of species differences, but that many of the measures employed in cross-species cognition tests impose floor or ceiling effects on one or more of the species being compared. These will obscure the alignment between g and G when individuals of multiple species are compared. An iterative latent variable moderation model is used, whereby sequentially removing subtests based on lowest coefficient of variance (CV) increases the degree to which g-loadings moderate the species differences among the remaining subtest pool. The correlation between moderator effect magnitude and rising CV across twelve iterations (from fourteen to three subtests) ranges from .710 to .854 based on which pairs of species are being compared. This result is consistent with the expectation that across species, g is highly predictive of species differences (and thus, g and G are one and the same), although significant ‘modular’ differences doubtlessly also exist. Predictions stemming from these observations are outlined using simulations. Finally, the implication of these findings for constructing trans-species valid measures of g and ‘IQ’ for use in future research (such as trans-species GWAS) is discussed.

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Availability of Data and Material

The data that were analyzed in this article are from Fichtel et al.’s (2020) publicly available Lemur Cognition Dataset.

Code Availability

All code will be made available upon request.

Notes

  1. The use of g vs. G to differentiate between the within vs. between-species levels of covariance among measures of general intelligence was first proposed in Fernandes et al. (2014), and has subsequently been adopted by comparative psychologists (e.g., Burkart et al., 2017a, b).

  2. The first detection of ‘Big G’ variance in a study of cross-species cognition was made by Deaner et al. (2006), who, after reviewing the relevant literature, performed a meta-analysis of cognitive studies in nonhuman primates. The authors grouped the various publications based on the experimental designs and attributes of the examined subjects. These researchers then used a Bayesian latent model, determining that although primate genera did not differ considerably in their performance within a specific experimental design, they nevertheless differed in their overall performance across tasks. Even though Bayesian latent analysis does not compute the proportion of variance accounted for by a particular dimension, the researchers evaluated the model’s fit by considering the frequency with which global variables correctly predicted the observed rankings in their dataset. Across the 229 genus-by-genus examinations, the analysis accurately predicted the outcomes in 85% of the comparisons supporting the presence of a single cognitive ability latent dimension, corresponding to a ‘Big G’ factor. The researchers also reported that none of the posterior means of their paradigm-genus bias effects reached statistical significance suggesting the absence of domain-specific abilities. The researchers also found that great apes generally performed better compared to other nonhuman primate clades.

  3. The very first examination of these associations appears to have been carried out by van Meerveld (2012) in a Bachelor’s degree thesis. Using a restricted pool of subtests (five) from the PCTB, this researcher reported a vector correlation of .595 between g loadings and the magnitude of performance differences between human children and chimpanzees, based on re-analysis of data published by Herrmann et al. (2007, 2010). van Meerveld (2012) also noted that “[a]s the factor analysis of the PCTB yielded some quite unusual outcomes – at least in comparison with factor analyses of IQ batteries taken by humans – we had to remove four tasks from the dataset, drastically lowering the reliability of the test [for g-loading moderation on species differences]” (p.16). It is clear that van Meerwald identified the range restriction effect that prevents recovery of g/G variance among these subtests; however, unlike Woodley of Menie et al. (2017), this researcher did not note the likely phylogenetic significance of these factor analytic anomalies (i.e., these being a function of floor or ceiling effects imposed on species performance by the presence of domain specific abilities). In employing data on three (out of five) subscales of the National Center for Toxicological Research Operant Test Battery, van Meerveld was also able to recover apparent indications of potent g/G variance moderation on species performance differences in comparisons involving humans and rhesus monkeys (r = .925), and in comparisons involving rhesus monkeys and rats (r = .895). Although not statistically significant with an N of three subscales each, these vector correlations are nevertheless very large in magnitude (i.e., r ≥ .70; Rosenthal, 1996). In estimating these vector correlations, van Meerveld excluded two subscales that exhibited apparently severe range restriction as evidenced by the presence of effectively no g loadings for these when estimated using correlation matrices. This particular finding should be interpreted extremely cautiously however, as it is clear from van Meerveld’s data that in the rat-monkey comparison, the former fairly consistently outperformed the latter across subscales, which indicates that the general factor variance among these scales is highly likely to reflect a narrower operant learning factor with respect to which rats might be highly specialised, rather than g/G.

  4. It should be noted that among chimpanzees, three of these four abilities also exhibit strong g loadings (Attention λ = .515; Gaze λ = .048; Spatial Memory λ = .270; Object Permanence λ = .659). Of these the g loading of Object Permanence was the highest of the 13 subtests examined in Woodley of Menie, Fernandes, and Hopkins (2015). Among human children (based on Woodley of Menie et al., 2017, who reanalysed data from Herrmann et al., 2007, 2010), three of these four abilities also exhibit strong g loadings (Attention λ = .610, Gaze λ = .396, Spatial Memory λ = − .009, and Object Permanence λ = .476). Of these, attention had the highest g loading of the 12 PCTB subtests examined. The r(d*g) for just these four abilities in the human-chimpanzee comparison (using the averaged g loadings) is .59, which is a large magnitude effect size (r between .50 and .69; Rosenthal, 1996).

  5. It should be stressed that these hypothetical trans-species IQ scores would only be meaningful for comparing individuals of different species with reference to a value that is fixed relative to the human mean of 100. They would not meaningfully correspond to adult human variation on conventional psychometric tests (such as the WAIS, or Ravens Progressive Matrices), which are much less range restricted than the sorts of tests that can be used to compare human (children) with other species, and are typically normed with reference to a standardization sample, not a species ‘Greenwich Meridian’ value.

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Authors and Affiliations

  1. Center Leo Apostel for Transdisciplinary Studies, Vrije Universiteit Brussel, Brussels, Belgium

    Michael A. Woodley of Menie

  2. Department of Psychology, University of Arizona, Tucson, AZ, USA

    Mateo Peñaherrera-Aguirre

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  1. Michael A. Woodley of Menie
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  2. Mateo Peñaherrera-Aguirre
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MWOM and MPA drafted the manuscript. MWOM and MPA prepared the data analyses. Both authors approved the final version.

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Correspondence to Michael A. Woodley of Menie.

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Woodley of Menie, M.A., Peñaherrera-Aguirre, M. General Intelligence as a Major Source of Cognitive Variation Among Individuals of Three Species of Lemur, Uniting g with G. Evolutionary Psychological Science 8, 241–253 (2022). https://doi.org/10.1007/s40806-021-00304-x

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