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Updated: Sep 11, 2025

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems
Published on: May 5, 2015
Systematic mapping and bibliometric analysis of meta-analyses on animal cognition
Ayumi Mizuno1, Malgorzata Lagisz1, Pietro Pollo2
1Evolution & Ecology Research Centre, School of Biological, Earth and Environmental, Sciences, University of New South Wales, Sydney, NSW, Australia; Department of Biological Sciences, Faculty of Science, University of Alberta, Edmonton, AB, Canada.
Abstract:
Meta-analyses play an important role in empirically synthesising research and guiding future directions. The field of animal cognition is rapidly expanding, with both empirical and review papers increasing at a faster rate than those in the life sciences overall. However, the use of meta-analyses, their methodological rigour, and the geographic distribution of research activity remain unclear. We systematically reviewed 49 meta-analytical studies encompassing 1824 primary studies on animal cognition. Half of the meta-analytical studies focused on the evolution and diversity of non-human animal cognition, while the other half used animals as models to understand human cognition. Most studies addressed factors affecting cognitive abilities, focusing on mammals and birds. Although many studies aimed to examine evolutionary or diversity-related questions, few analysed cognitive variation across species or tested evolutionary hypotheses, and even fewer incorporated phylogenetic relationships. While some studies investigated sex differences, many reported that they could not due to unbalanced sex ratios in the primary studies, notably a predominance of males. Both primary and meta-analytical studies often lacked adequate methodological reporting and rarely shared raw data or analysis scripts. Our bibliometric analysis showed that research is geographically concentrated, with authorship and collaboration mostly in high-income countries. To address current gaps, we recommend greater adherence to open science practices, improved regional inclusivity, and broader taxonomic and individual-level coverage. Finally, we highlight the complementary roles of meta-analyses and Big Team Science in advancing the field by improving its transparency, inclusivity, and reliability.
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