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Record W4402213534 · doi:10.1038/s41467-024-51982-7

Infants expect some degree of positive and negative reciprocity between strangers

2024· article· en· W4402213534 on OpenAlexaff
Kyong‐sun Jin, Fransisca Ting, Zijing He, Renée Baillargeon

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsUniversity of Toronto
FundersUniversity of Illinois at Urbana-ChampaignNational Research Foundation of KoreaNational Research FoundationJohn Templeton Foundation
KeywordsReciprocity (cultural anthropology)Degree (music)PsychologySocial psychologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Social scientists from different disciplines have long argued that direct reciprocity plays an important role in regulating social interactions between unrelated individuals. Here, we examine whether 15-month-old infants (N = 160) already expect direct positive and negative reciprocity between strangers. In violation-of-expectation experiments, infants watch successive interactions between two strangers we refer to as agent1 and agent2. After agent1 acts positively toward agent2, infants are surprised if agent2 acts negatively toward agent1 in a new context. Similarly, after agent1 acts negatively toward agent2, infants are surprised if agent2 acts positively toward agent1 in a new context. Both responses are eliminated when agent2's actions are not knowingly directed at agent1. Additional results indicate that infants view it as acceptable for agent2 either to respond in kind to agent1 or to not engage with agent1 further. By 15 months of age, infants thus already expect a modicum of reciprocity between strangers: Initial positive or negative actions are expected to set broad limits on reciprocal actions. This research adds weight to long-standing claims that direct reciprocity helps regulate interactions between unrelated individuals and, as such, is likely to depend on psychological systems that have evolved to support reciprocal reasoning and behavior.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.048
GPT teacher head0.365
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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