Evidence map›Paper›PMID 35946152›Full record

ArticleProceedings. Biological sciences2022

Behavioural specialization and learning in social networks.

Olof Leimar, Sasha R X Dall, Alasdair I Houston, John M McNamara

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Conformity to popular, not average, opinions: Models, data, and evolution.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Article
  4. The evolution of division of labour: preconditions and evolutionary feedback.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Review
  5. Conformity to continuous and discrete ordered traits.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. The evolutionary consequences of learning under competition.Proceedings. Biological sciences · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Behavioural specialization and learning in social networks.Proceedings. Biological sciences · 2022
    Article
  11. Heterogeneous responsiveness to environmental stimuli.Behavioral ecology : official journal of the International Society for Behavioral Ecology
    Article
  12. Flexible learning in complex worlds.Behavioral ecology : official journal of the International Society for Behavioral Ecology
    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

4 authors.

Olof LeimarDepartment of Zoology, Stockholm University, 106 91 Stockholm, Sweden.ORCID 0000-0001-8621-6977
Sasha R X DallCentre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK.
Alasdair I HoustonSchool of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK.ORCID 0000-0002-5769-7692
John M McNamaraSchool of Mathematics, University of Bristol, Bristol BS8 1UG, UK.ORCID 0000-0002-4235-3045

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Interactions in social groups can promote behavioural specialization. One way this can happen is when individuals engage in activities with two behavioural options and learn which option to choose. We analyse interactions in groups where individuals learn from playing games with two actions and negatively frequency-dependent payoffs, such as producer-scrounger, caller-satellite, or hawk-dove games. Group members are placed in social networks, characterized by the group size and the number of neighbours to interact with, ranging from just a few neighbours to interactions between all group members. The networks we analyse include ring lattices and the much-studied small-world networks. By implementing two basic reinforcement-learning approaches, action-value learning and actor-critic learning, in different games, we find that individuals often show behavioural specialization. Specialization develops more rapidly when there are few neighbours in a network and when learning rates are high. There can be learned specialization also with many neighbours, but we show that, for action-value learning, behavioural consistency over time is higher with a smaller number of neighbours. We conclude that frequency-dependent competition for resources is a main driver of specialization. We discuss our theoretical results in relation to experimental and field observations of behavioural specialization in social situations.

Indexed as

Game TheorySocial NetworkingHumansReinforcement, Psychologyanimal personalitybehavioural consistencygame theoryreinforcement learning

Identifiers

PMID35946152
PMCPMC9363987

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.