Evidence map›Paper›PMID 33489255›Full record

ArticleRoyal Society open science2020

Dynamic social learning in temporally and spatially variable environments.

Dominik Deffner, Vivien Kleinow, Richard McElreath

Abstract read
In one paragraph

Article in Royal Society open science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing 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

29 citing papers in PubMed.

  1. Article
  2. Article
  3. How social learning enhances-or undermines-efficiency and flexibility in collective decision-making under uncertainty.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. Decoding the Dynamics of Cultural Change: A Cultural Evolution Approach to the Psychology of Acculturation.Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc · 2025
    Review
  7. Article
  8. Article
  9. Article
  10. Trade-offs, control conditions, and alternative designs in the experimental study of cultural evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Review
  11. Bridging theory and data: A computational workflow for cultural evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  12. Article
  13. Humans flexibly integrate social information despite interindividual differences in reward.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. A Cognitive Computational Approach to Social and Collective Decision-Making.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024
    Article
  19. Cultural transmission of move choice in chess.Proceedings. Biological sciences · 2023
    Article
  20. 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

3 authors.

Dominik DeffnerMax Planck Institute for Evolutionary Anthropology, Department of Human Behavior, Ecology and Culture, Leipzig, Germany.ORCID 0000-0002-1649-3861
Vivien KleinowMax Planck Institute for Evolutionary Anthropology, Department of Human Behavior, Ecology and Culture, Leipzig, Germany.
Richard McElreathMax Planck Institute for Evolutionary Anthropology, Department of Human Behavior, Ecology and Culture, Leipzig, Germany.ORCID 0000-0002-0387-5377

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cultural evolution is partly driven by the strategies individuals use to learn behaviour from others. Previous experiments on strategic learning let groups of participants engage in repeated rounds of a learning task and analysed how choices are affected by individual payoffs and the choices of group members. While groups in such experiments are fixed, natural populations are dynamic, characterized by overlapping generations, frequent migrations and different levels of experience. We present a preregistered laboratory experiment with 237 mostly German participants including migration, differences in expertise and both spatial and temporal variation in optimal behaviour. We used simulation and multi-level computational learning models including time-varying parameters to investigate adaptive time dynamics in learning. Confirming theoretical predictions, individuals relied more on (conformist) social learning after spatial compared with temporal changes. After both types of change, they biased decisions towards more experienced group members. While rates of social learning rapidly declined in rounds following migration, individuals remained conformist to group-typical behaviour. These learning dynamics can be explained as adaptive responses to different informational environments. Summarizing, we provide empirical insights and introduce modelling tools that hopefully can be applied to dynamic social learning in other systems.

Indexed as

collective behaviourcomputational modellingcultural evolutiondecision-makingsocial learning

Identifiers

PMID33489255
PMCPMC7813247

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.