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Record W2097348739 · doi:10.1002/hbm.22718

Personal experience with narrated events modulates functional connectivity within visual and motor systems during story comprehension

2014· article· en· W2097348739 on OpenAlexaff
Ho Ming Chow, Raymond A. Mar, Yisheng Xu, Siyuan Liu, Suraji Wagage, Allen R. Braun

Bibliographic record

VenueHuman Brain Mapping · 2014
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsYork University
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsFunctional magnetic resonance imagingEmbodied cognitionComprehensionNarrativePsychologyPerceptionCognitive psychologyCognitive scienceFunctional connectivityCognitionHumNeuroscienceComputer scienceArtificial intelligenceLinguisticsHistory

Abstract

fetched live from OpenAlex

Past experience of everyday life activities, which forms the basis of our knowledge about the world, greatly affects how we understand stories. Yet, little is known about how this influence is instantiated in the human brain. Here, we used functional magnetic resonance imaging to investigate how past experience facilitates functional connectivity during the comprehension of stories rich in perceptual and motor details. We found that comprehenders' past experience with the scenes and actions described in the narratives selectively modulated functional connectivity between lower- and higher-level areas within the neural systems for visual and motor processing, respectively. These intramodal interactions may play an important role in integrating personal knowledge about a narrated situation with an evolving discourse representation. This study provides empirical evidence consistent with the idea that regions related to visual and motor processing are involved in the reenactment of experience as proposed by theories of embodied cognition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.607

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 studies0.0010.000
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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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