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Record W2165257727 · doi:10.1901/jaba.2012.45-23

ACQUISITION OF SOCIAL REFERENCING VIA DISCRIMINATION TRAINING IN INFANTS

2012· article· en· W2165257727 on OpenAlexaff
Martha Peláez, Javier Virúes‐Ortega, Jacob L. Gewirtz

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2012
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPsychologyFacial expressionMultiple baseline designDevelopmental psychologyExtinction (optical mineralogy)Discriminative modelContext (archaeology)Object (grammar)ReinforcementExpression (computer science)Cognitive psychologyCommunicationSocial psychologyIntervention (counseling)Artificial intelligence

Abstract

fetched live from OpenAlex

This experiment investigated social referencing as a form of discriminative learning in which maternal facial expressions signaled the consequences of the infant's behavior in an ambiguous context. Eleven 4- and 5-month-old infants and their mothers participated in a discrimination-training procedure using an ABAB design. Different consequences followed infants' reaching toward an unfamiliar object depending on the particular maternal facial expression. During the training phases, a joyful facial expression signaled positive reinforcement for the infant reaching for an ambiguous object, whereas a fearful expression signaled aversive stimulation for the same response. Baseline and extinction conditions were implemented as controls. Mothers' expressions acquired control over infants' approach behavior for all participants. All participants ceased to show discriminated responding during the extinction phase. The results suggest that 4- and 5-month-old infants can learn social referencing via discrimination training.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.179
GPT teacher head0.382
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2012
Admission routes1
Has abstractyes

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