How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

Abstract

General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (r = 0.185; 95% CI: 0.087-0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.

Keywords: G × E interaction; PCA; cognition; g; mixed models; multi-level models.

Figure 1.

Results from the meta-analytic model. Average correlation coefficients (±95% CIs) are presented for each species. The total number of extracted correlations (r) and the median sample size (n) per correlation are also provided for each species. The multi-level model including random effects of species and study identity estimates an average correlation of 0.185 (95% CI: 0.087–0.287).

Figure 2.

(a) Among-litter variance, (b) within-litter variance and (c) broad-sense heritability in five learning tasks measured on male mice from 58 full-sib litters, each of which had two siblings maintained in standard laboratory conditions (control; 116 individuals), while the two other siblings had access to a running wheel and were exposed to novel environments on a daily basis (enriched; 115 individuals). Mice were tested on a battery of five tasks, including (i) Lashley maze (mean number of errors across three trials; black open circles), (ii) passive avoidance (ratio of avoidance latency by baseline latency; red triangles), (iii) T-maze alternation (trial at first four correct choices in a row; green plus sign), (iv) odour discrimination (mean errors across three trials; blue × sign) and (v) spatial water maze (mean path length in cm across trials 1–2; indigo diamond). Also shown are estimates for the factor scores extracted from an exploratory factor analysis and interpreted as ‘GCA’ in [13]. Note that standard errors are not shown (see electronic supplementary material, table S2). (Online version in colour.)

Figure 3.

Litter-specific reaction norms (grey lines; N = 58) in (a) the factor scores extracted from an exploratory factor analysis and interpreted as ‘GCA’ by Sauce et al. [13] and five learning tasks measured on male mice, including the (b) Lashley maze, (c) passive avoidance, (d) T-maze alternation, (e) odour discrimination and (f) spatial water maze. Grey lines in panels show predicted trajectories as function of the environment, relative to the population mean (indicated by ‘0’ on the y-axis).

Figure 4.

‘Heat maps’ displaying the (a) among-litter and (b) within-litter correlations between five learning tasks measured on 231 male mice, including the Lashley maze, passive avoidance, T-maze alternation, odour discrimination and spatial water maze. Correlations significantly different from 0 are denoted by asterisks (***; see electronic supplementary material, table S4 for 95% confidence intervals). (Online version in colour.)