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Record W4392601655 · doi:10.3390/brainsci14030264

Adapting to Changes in Communication: The Orbitofrontal Cortex in Language and Speech Processing

2024· article· en· W4392601655 on OpenAlexaff
Xiaoming Jiang, Xiquan Ma, Ryan Sanford, Xun Li

Bibliographic record

VenueBrain Sciences · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of ChinaShanghai Education Development FoundationNatural Science Foundation of Shanghai
KeywordsOrbitofrontal cortexPsychologyNeuroscienceCognitive psychologyComprehensionCognitive scienceCognitionPrefrontal cortexLinguistics

Abstract

fetched live from OpenAlex

Despite most studies on the neurobiology of language demonstrating the central part of the perisylvian network involved in language and speech function, this review attempts to complement this view by focusing on the role of the orbitofrontal cortex (OFC). This region is primarily involved in goal-directed adaptive behavior. Recently, there has been increasing evidence that the OFC is involved in language and speech tasks. This review demonstrates that not only the linguistic tasks that involve the processing of socially, pragmatically and emotionally relevant information engage OFC and its neurobiological mechanisms, but also specific receptive and expressive language performances rely on specific neurophysiological properties of this region (e.g., the gray matter volume and the functional activation of OFC and the uncinate fasciculus that connects OFC), which in many cases, demand executive functions. These findings highlight: (1) The OFC plays a relevant role in the adaptive neurobiological function of language; (2) the neurobiological mechanisms beyond linguistic and speech processes complement and interplay with the language-unique processes to achieve successful comprehension and production in the changing communicative contexts.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.239

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 head0.045
GPT teacher head0.347
Teacher spread0.302 · 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 designBench or experimental
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

Citations14
Published2024
Admission routes1
Has abstractyes

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