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Record W3151128203 · doi:10.1017/9781316882764.008

Utterances as Communicative Acts

2021· book-chapter· en· W3151128203 on OpenAlexaff
Victor J. Boucher

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEntrainment (biomusicology)Hebbian theorySensory systemVocal learningComputer sciencePsychologyMotor learningFocus (optics)CommunicationCognitive scienceCognitive psychologyArtificial intelligenceNeuroscienceArtificial neural networkRhythmAcousticsPhysics

Abstract

fetched live from OpenAlex

Utterances are communicative acts. They bear observable structures that relate to constraints on actions and the processing of sequences of actions. In viewing utterances this way, rather than as sentences on a page, it is essential to consider that oral communication rests on a basic speaker–listener parity, which is achieved through motor-sensory coupling. This coupling not only applies to articulatory-acoustic features but also, at a basic level, to multimodal information that binds to structures of motor speech and which serves to constitute semantic representations. Research on motor-sensory coupling is discussed with a focus on the adaptation of couplings with speech experience. These adaptations entail different types of learning, including reinforcement and supervised and Hebbian learning, that relate cortical and subcortical processes. Whereas motor-sensory coupling at cortical levels is well known, an outline of proposals is provided bearing on the role of subcortical systems. A process of neural entrainment is presented as a pivotal principle by which multisensory information couples to structures of motor speech.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.931
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.269
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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