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Record W7154614564 · doi:10.48448/d1wj-nf88

Examining the Robustness of Neural Correlates of Infants’ Sociomoral Evaluations

2025· other· W7154614564 on OpenAlexaff
Cognitive Science Society 2025, Lauren L. Emberson, J. Kiley Hamlin, Zohreh Soleimani, Enda Tan

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProsocial behaviorGeneralizability theoryNeural correlates of consciousnessPerceptionSocial cognitionAttributionEmpathy

Abstract

fetched live from OpenAlex

Research has shown that infants prefer prosocial characters over antisocial ones, suggesting that sociomoral evaluation is early-emerging. However, some have argued that infants’ preferential responses stem from low-level perceptual processes rather than true social understanding. Using electroencephalography (EEG), past work has suggested that motivational and social, but not attentional, processes are implicated in infants’ responses to prosocial versus antisocial acts and individuals, however, the majority of past work utilized a single type of prosocial/antisocial interactions: helping a character to climb a hill. To test the generalizability of past neural findings from the hill paradigm, here we examined infants' responses in a distinct helping/hindering scenario in which a character tries but fails to open a box and is alternatively helped or hindered. Largely replicating past work, infants showed greater activity in social (indexed by the P400) but not attentional (indexed by the Nc) ERP components when seeing hinderers versus helpers, consistent with claims that infants’ responses to prosocial and antisocial agents are social. No evidence of differential approach/avoidance motivation during prosocial/antisocial events was found. These findings support the role of social processes in infants’ sociomoral evaluations.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0010.022
Scholarly communication0.0000.001
Open science0.0050.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.067
GPT teacher head0.350
Teacher spread0.283 · 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.

Study designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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