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Record W2946537152 · doi:10.1080/13825585.2019.1606890

The role of semantic memory in the recognition of emotional valence conveyed by written words

2019· article· en· W2946537152 on OpenAlexaff
Joël Macoir, Robert Laforce, Maximiliano A. Wilson, M-P. Tremblay, Carol Hudon

Bibliographic record

VenueAging Neuropsychology and Cognition · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
Fundersnot available
KeywordsValence (chemistry)Emotional valenceSemantic memoryPsychologyAphasiaCognitive psychologyNatural language processingComputer scienceArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

The main goal of this study was to examine the role of semantic memory in the recognition of emotional valence conveyed by words. Eight participants presenting with the semantic variant of primary progressive aphasia (svPPA) and 33 healthy control participants were administered three tasks designed to investigate the formal association between the recognition of emotional valence conveyed by words and the lexical and semantic processing of these words. Results revealed that individuals with svPPA showed deficits in the recognition of negative emotional valence conveyed by words. Moreover, results evidenced that their performance in the recognition of emotional valence was better for correctly than for incorrectly retrieved lexical entries of words, while their performance was comparable for words that were correctly or incorrectly associated with semantic concepts. These results suggest that the recognition of emotional valence conveyed by words relies on the retrieval of lexical, but not semantic, representations of words.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.260
Teacher spread0.247 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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