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Record W4247136040 · doi:10.1037/e617242012-098

How the context matters. Literal and figurative meaning in the embodied language paradigm

2012· dataset· en· W4247136040 on OpenAlexaff
Valentina Cuccio, F. Ferri, Marianna Ambrosecchia, Leonardo Fogassi, Vittorio Gallese

Bibliographic record

VenuePsycEXTRA Dataset · 2012
Typedataset
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersEuropean Commission
KeywordsLiteral and figurative languageEmbodied cognitionMeaning (existential)Literal (mathematical logic)LinguisticsContext (archaeology)PsychologyComputer sciencePhilosophyArtificial intelligenceHistory

Abstract

fetched live from OpenAlex

The involvement of the sensorimotor system in language understanding has been widely demonstrated.However, the role of context in these studies has only recently started to be addressed.Though words are bearers of a semantic potential, meaning is the product of a pragmatic process.It needs to be situated in a context to be disambiguated.The aim of this study was to test the hypothesis that embodied simulation occurring during linguistic processing is contextually modulated to the extent that the same sentence, depending on the context of utterance, leads to the activation of different effector-specific brain motor areas.In order to test this hypothesis, we asked subjects to give a motor response with the hand or the foot to the presentation of ambiguous idioms containing action-related words when these are preceded by context sentences.The results directly support our hypothesis only in relation to the comprehension of hand-related action sentences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.015

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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2012
Admission routes1
Has abstractyes

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Same venuePsycEXTRA DatasetSame topicLanguage, Metaphor, and CognitionFrench-language works237,207