Evidence map›Paper›PMID 39876719›Full record

ArticleProceedings. Biological sciences2025

Social learning preserves both useful and useless theories by canalizing learners' exploration.

Maxime Derex, Jean-François Bonnefon, Robert Boyd, Richard McElreath, Alex Mesoudi

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Maxime DerexToulouse School of Economics, CNRS, University of Toulouse Capitole, Toulouse, France.ORCID 0000-0002-1512-6496
Jean-François BonnefonToulouse School of Economics, CNRS, University of Toulouse Capitole, Toulouse, France.
Robert BoydInstitute of Human Origins, Arizona State University, Tempe, AZ 85287, USA.
Richard McElreathMax Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
Alex MesoudiHuman Behaviour and Cultural Evolution Group, Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK.ORCID 0000-0002-7740-1625

Funding

Agence Nationale de la Recherche
6 · The paper itself

Abstract

In many domains, learning from others is crucial for leveraging cumulative cultural knowledge, which encapsulates the efforts of successive generations of innovators. However, anecdotal and experimental evidence suggests that reliance on social information can reduce the exploration of the problem space. Here, we experimentally investigate the extent to which cultural transmission fosters the persistence of arbitrary solutions in a context where participants are incentivized to improve a physical system across multiple trials. Participants were exposed to various theories about the system, ranging from accurate to misleading. Our findings indicate that even under conditions conducive to exploration, the transmission of cultural knowledge canalizes learners' focus, limiting their consideration of alternative solutions. This effect was observed in both the theories produced and the solutions attempted by participants, irrespective of the accuracy of the provided theories. These results challenge the notion that arbitrary solutions persist only when they are efficient or intuitive and underscore the significant role of cultural transmission in shaping human knowledge and technologies.

Indexed as

LearningSocial LearningAdultFemaleHumansKnowledgeMaleYoung Adultcultural evolutioncultural inertiacumulative cultureinnovationsocial learning

Identifiers

PMID39876719
PMCPMC11775619

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.