Social Learning Strategies in Networked Groups
Open accessAbstract
When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem‐solving task in which participants search a complex space, and each can view and imitate others' solutions. Results showed that participants combined multiple sources of information to guide learning, including payoffs of peers' solutions, popularity of solution elements among peers, similarity of peers' solutions to their own, and relative payoffs from individual exploration. Furthermore, performance was positively associated with imitation rates at both the individual and group levels. When peers' payoffs were hidden, popularity and similarity biases reversed, participants searched more broadly and randomly, and both quality and equity of exploration suffered. We conclude that when peers' solutions can be effectively compared, imitation does not simply permit scrounging, but it can also facilitate propagation of good solutions for further cumulative exploration.
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The same individuals arranged in a different network can exhibit different emergent behavior.
“In other words, the same individuals arranged in a different network can exhibit different emergent behavior”
From Stewardship of global collective behavior (2021) · cites this paper as [67]
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- Authors
- Wisdom, T. N., Song, X., & Goldstone, R. L.
- Affiliations
- Indiana University Bloomington (US)
- Indiana University (US)
- Corresponding author
- Wisdom, Thomas N.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2013-07-11
- Volume
- 37
- Issue
- 8
- Pages
- 1383-1425
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W2108871946
- Cited by
- 83
- References
- 61
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 2 · 2023: 11 · 2022: 6 · 2021: 11 · 2020: 11 · 2019: 7 · 2018: 6 · 2017: 3 · 2016: 6 · 2015: 6 · 2014: 5 · 2013: 3
- FWCI
- 13.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Cultural Studies › Language and cultural evolution
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Keywords
- social learning, imitation, solution search, payoff information, popularity bias, similarity bias, exploration, cumulative learning, problem solving
- MeSH terms
- Decision Making, Group Processes, Humans, Imitative Behavior, Learning, Problem Solving, Social Behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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