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Record W4307452238 · doi:10.31234/osf.io/duabj

Meaning in hand: Investigating shared mechanisms of motor imagery and sensorimotor simulation in language processing

2022· preprint· en· W4307452238 on OpenAlexafffund
Emiko J. Muraki, Stephan F. Dahm, Penny M. Pexman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEmbodied cognitionMotor imageryNounPsychologyCognitive psychologyAction (physics)Object (grammar)Representation (politics)Computer scienceNatural language processingBrain–computer interfaceArtificial intelligenceElectroencephalographyNeuroscience

Abstract

fetched live from OpenAlex

There is substantial evidence to support grounded theories of semantic representation, however the mechanisms of simulation in those theories are underspecified. In the present study, we tested whether motor imagery shares mechanisms with sensorimotor simulations engaged during semantic processing. We quantified individual differences in motor imagery ability using a series of implicit imagery tasks and explicit imagery questionnaires. We then tested the relationship between motor imagery ability and sensorimotor effects observed in four different syntactic classification tasks. In Experiment 1 (N = 185), we replicated an association between hand motor imagery and sensorimotor effects wherein individuals with higher scores on hand imagery ability measures had longer response times to words referring to objects difficult to interact with (low body-object interaction nouns) than to words referring to objects easy to interact with (high body-object interaction nouns). We also observed shorter response times and more accurate responses to foot/leg action verbs than non-foot/leg action verbs, but this sensorimotor effect in language processing did not significantly correlate with individual differences in motor imagery. In Experiment 2 (N = 195), we observed shorter response times and more accurate responses to hand/arm action verbs than non-hand/arm action verbs, as well as four associations between motor imagery and language processing of hand/arm action verbs. Further, we observed shorter response times and more accurate responses to embodied verbs than for non-embodied verbs, but this sensorimotor effect in language processing did not correlate with individual differences in motor imagery. The results suggest specific (and not general) associations, in that some, but not all forms of hand imagery and object-directed motor imagery are related to sensorimotor effects in language processing of hand/arm action verbs and nouns describing objects that are easy to interact with. As such, hand and object-directed motor imagery may share some mechanisms with sensorimotor simulation during semantic processing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.054
GPT teacher head0.347
Teacher spread0.293 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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