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Record W2782016767 · doi:10.1371/journal.pone.0190185

Very young infants learn abstract rules in the visual modality

2018· article· en· W2782016767 on OpenAlexfundno aff
Brock Ferguson, Steven Franconeri, Sandra R. Waxman

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentSocial Sciences and Humanities Research Council of CanadaNational Institutes of HealthNational Science Foundation
KeywordsModality (human–computer interaction)ModalitiesPerceptionCognitionVisual perceptionStimulus modalityComputer scienceCognitive psychologySpace (punctuation)Auditory perceptionConcept learningTask (project management)PsychologyArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Abstracting the structure or 'rules' underlying observed patterns is central to mature cognition, yet research with infants suggests this far-reaching capacity is initially restricted to certain stimuli. Infants successfully abstract rules from auditory sequences (e.g., language), but fail when the same rules are presented as visual sequences (e.g., shapes). We propose that this apparent gap between rule learning in the auditory and visual modalities reflects the distinct requirements of the perceptual systems that interface with cognition: The auditory system efficiently extracts patterns from sequences structured in time, but the visual system best extracts patterns from sequences structured in space. Here, we provide the first evidence for this proposal with adults in an abstract rule learning task. We then reveal strong developmental continuity: infants as young as 3 months of age also successfully learn abstract rules in the visual modality when sequences are structured in space. This provides the earliest evidence to date of abstract rule learning in any modality.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.057
GPT teacher head0.308
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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

Citations27
Published2018
Admission routes1
Has abstractyes

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