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Record W2140001076 · doi:10.3389/fpsyg.2011.00253

Motor Response Selection in Overt Sentence Production: A Functional MRI Study

2011· article· en· W2140001076 on OpenAlexfundno aff
Pascale Tremblay, Steven L. Small

Bibliographic record

VenueFrontiers in Psychology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
FundersNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health ResearchPritzker School of MedicineUniversity of Chicago
KeywordsSentencePsychologySelection (genetic algorithm)Inferior frontal gyrusTask (project management)Premotor cortexSupplementary motor areaContext (archaeology)GestureCognitive psychologyFunctional magnetic resonance imagingNeuroscienceSet (abstract data type)Artificial intelligenceComputer scienceMedicineDorsum

Abstract

fetched live from OpenAlex

Many different cortical areas are thought to be involved in the process of selecting motor responses, from the inferior frontal gyrus, to the lateral and medial parts of the premotor cortex. The objective of the present study was to examine the neural underpinnings of motor response selection in a set of overt language production tasks. To this aim, we compared a sentence repetition task (externally constrained selection task) with a sentence generation task (volitional selection task) in a group of healthy adults. In general, the results clarify the contribution of the pre-SMA, cingulate areas, PMv, and pars triangularis to the process of selecting motor responses in the context of sentence production, and shed light on the manner in which this network is modulated by selection mode. Further, the present study suggests that response selection in sentence production engages neural resources similar to those engaged in the production of isolated words and oral motor gestures.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.314
Teacher spread0.259 · 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

Citations52
Published2011
Admission routes1
Has abstractyes

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