MétaCan
Menu
Back to cohort
Record W4253899227 · doi:10.31219/osf.io/43dnx

The mechanisms of social evaluation in infancy: A preregistered exploration of infants' eye-movement and pupillary responses to prosocial and antisocial events

2021· preprint· en· W4253899227 on OpenAlexaff
Enda Tan, J. Kiley Hamlin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProsocial behaviorPsychologyPreferenceGazeDevelopmental psychologySocial psychologySocial preferencesEye trackingCognitive psychology

Abstract

fetched live from OpenAlex

Past research shows infants selectively touch and look longer at characters who help versus hinder others (Hamlin et al., 2007; 2010); however, the mechanisms underlying this tendency remain under-specified. The current preregistered experiment approaches this question by examining infants’ real-time looking behaviors during prosocial and antisocial events, and exploring how individual infants’ looking behaviors correlate with helper preferences. Using eye-tracking, 34 five-month-olds were familiarized with two blocks of the “hill” scenario originally developed by Kuhlmeier et al., (2003), in which a climber tries unsuccessfully to reach the top of a hill and is alternately helped or hindered. Infants’ visual preferences were assessed after each block of 6 helping and hindering events by proportional looking time to the helper versus hinderer in an image of the characters side-by-side. Results showed that, at the group level, infants looked longer at the helper after viewing 12 (but not after viewing 6) helping and hindering videos. Moreover, individual infants’ average preference for the helper was predicted by their looking behaviors, particularly those suggestive of an understanding of the climber’s unfulfilled goal. These results shed light on how infants process helping/hindering scenarios, and suggest that goal understanding is important for infants’ helper preferences.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.363
Teacher spread0.314 · 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 designObservational
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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicChild and Animal Learning DevelopmentFrench-language works237,207