Systematic mapping and bibliometric analysis of meta-analyses on animal cognition

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 1,824 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. DOI https://doi.org/10.32942/X2XW7M Subjects Ecology and Evolutionary Biology, Psychiatry and Psychology

Comparative Psychology, open science, research synthesis, reproducibility, Scientific inequality, Reporting quality, Geographic bias Dates Published: 2025-06-04 01:30 Last Updated: 2025-06-04 01:30 License CC BY Attribution 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: https://github.com/Ayumi-495/systematic_mapping_AnimCogn Language: English