Department of Biology, University of Ottawa, Ottawa, Ontario, Canada.
Department of Biology, Villanova University, Villanova, Pennsylvania, USA.
Proc Biol Sci. 2020 Dec 9;287(1940):20201853. doi: 10.1098/rspb.2020.1853.
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 ( = 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.
一般性智力(General intelligence)是一个多世纪以来备受关注的话题。传统上,对一般性智力的研究基于主成分分析和其他降维方法。高速计算的出现提供了替代的统计工具,这些工具已被用于测试人类一般性智力的预测。相比之下,非人类动物的一般性智力研究还处于起步阶段,仍然主要依赖于因素分析程序。在这里,我们认为,当降维方法被错误地应用时,可能会导致虚假结果,并限制我们对动物认知中生态和进化原因的理解。我们使用元分析方法,基于 555 个双变量相关性,表明认知能力之间的平均相关性较低( = 0.185;95%CI:0.087-0.287),这表明动物的一般性智力支持相对较弱。然后,我们使用具有相关性(遗传)数据的案例研究来演示如何使用混合模型分析特征,而无需降维,从而为认知特征之间表型方差的结构提供新的见解,并揭示否则会隐藏的遗传关联。我们希望本文将激发在动物认知及其进化研究中使用替代工具。