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Record W4366771222 · doi:10.31234/osf.io/8ns5v

Neural Processing of Children’s Theory of Mind in a Naturalistic Story-Listening Paradigm

2023· preprint· en· W4366771222 on OpenAlexaff
Chi‐Lin Yu, Rachel L. Eggleston, Kehui Zhang, Nia Nickerson, Xin Sun, Rebecca A. Marks, Xiaosu Hu, Jonathan Brennan, Henry M. Wellman, Ioulia Kovelman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Child Health and Human DevelopmentUniversity of Michigan
KeywordsTheory of mindPsychologyActive listeningAdventureNaturalismCognitive scienceNeuroimagingNeural systemCognitive psychologyNeuroscienceComputer scienceCommunicationArtificial intelligenceCognitionEpistemology

Abstract

fetched live from OpenAlex

Studies of the neural mechanism of theory of mind (ToM) often face challenges in quality data collection. We developed and implemented an innovative naturalistic story-listening paradigm, which is child-friendly, engaging, and ecologically valid, to shed light on ToM neural mechanisms in childhood. Children (N = 51; age range = 6-12 years) listened to a chapter of Alice’s Adventures in Wonderland during functional near-infrared spectroscopy neuroimaging. Methodologically, we showed the feasibility and utility of our paradigm, which successfully captured the neural mechanisms of ToM in young children. Substantively, our findings confirm and extend previous results by revealing the same ToM brain regions found in the adult and adolescent literature, and we further show how ToM processing has its own specialized neural profile, with language processing potentially being an additional support for its deployment and development.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.303
Teacher spread0.252 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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