Locals know more than it seems: a new method for revealing collective understanding, tested in three African communities

In the study of human cultural evolution, many theorists hold that technologies are transmitted and improved more through imitation rather than causal understanding. This view stems from results of studies that ask individuals in isolation about technologies they use, appearing to reveal a lack of causal understanding. Here, we introduce a new method to assess the knowledge of a group that allows individuals to view their neighbours' answers before deciding what the best answer might be from among those provided by the group. We asked individuals in three farming communities from Morocco (n = 203), Mali (n = 198) and Ghana (n = 120) to explain the causal processes behind local technologies. Our method reveals that when participants can review the answers provided by their peers, the most popular final answer is more reliable than when individuals provide answers in isolation. This indicates a division of labour in how causal knowledge is stored in the community-while most individuals may have poor causal knowledge, they recognize and defer to the best answer in the group. This shows that collectively the community is more knowledgeable than methods used up to now have indicated. This article is part of the theme issue 'The evolution of collective intelligence'.

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Publication Details

Journal
Philosophical Transactions of the Royal Society B Biological Sciences
Published
2026-04-16
DOI
https://doi.org/10.1098/rstb.2024.0453
Citations
1
Primary Topic
Language and cultural evolution
Type
article
Field-Weighted Citation Impact
62.48

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article

Locals know more than it seems: a new method for revealing collective understanding, tested in three African communities

Cristine H. Legare, Alejandro Erut, Cathal O’Madagain, Ghizlane Goubraim et al.
1 citations
Philosophical Transactions of the Royal Society B Biological Sciences
Language and cultural evolution
62.48
article

Locals know more than it seems: a new method for revealing collective understanding, tested in three African communities

Cristine H. Legare, Alejandro Erut, Cathal O’Madagain, Ghizlane Goubraim, George Tetteh, Emile Servan-Schreiber, Talib El Aissati
article en
1 citations

Abstract

In the study of human cultural evolution, many theorists hold that technologies are transmitted and improved more through imitation rather than causal understanding. This view stems from results of studies that ask individuals in isolation about technologies they use, appearing to reveal a lack of causal understanding. Here, we introduce a new method to assess the knowledge of a group that allows individuals to view their neighbours' answers before deciding what the best answer might be from among those provided by the group. We asked individuals in three farming communities from Morocco (n = 203), Mali (n = 198) and Ghana (n = 120) to explain the causal processes behind local technologies. Our method reveals that when participants can review the answers provided by their peers, the most popular final answer is more reliable than when individuals provide answers in isolation. This indicates a division of labour in how causal knowledge is stored in the community-while most individuals may have poor causal knowledge, they recognize and defer to the best answer in the group. This shows that collectively the community is more knowledgeable than methods used up to now have indicated. This article is part of the theme issue 'The evolution of collective intelligence'.

Philosophical Transactions of the Royal Society B Biological SciencesVol. 381(1948)
Université Mohammed VI Polytechnique (MA), The University of Texas at Austin (US)
Université Mohammed VI Polytechnique
No poverty, Decent work and economic growth
Openalex Percentile: Top 0%
Language and cultural evolution
62.48
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Locals know more than it seems: a new method for revealing collective understanding, tested in three African communities — Cristine H. Legare, Alejandro Erut, et al. · Philosophical Transactions of the Royal Society B Biological Sciences (2026) | TGRS Research Map | TGRS