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The Right Hemisphere's Contribution to the Processing of Semantic Relationships between Words

2008· article· en· W2039108699 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage and Linguistics Compass · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsRight hemisphereLateralization of brain functionSemantic memoryPsychologyCognitive psychologyNeuroimagingLinguisticsSemantics (computer science)Natural language processingComputer scienceCognitionNeurosciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract For more than a century, language has been assumed to be entirely dependent on left‐hemisphere‐based processing. However, since the early 1960s, evidence for the right hemisphere's involvement in language processing, in particular in the semantic processing of words, has emerged. At least three complementary approaches have provided evidence of this: behavioral data from neurologically intact participants, the study of brain‐damaged patients and the use of neuroimaging methods. The goal of this article is to review the major evidence from these three sources concerning the nature of the right hemisphere's contribution to the semantic processing of words. Overall, the data from these studies suggest that both the right hemisphere and the left hemisphere are crucial for semantic processing, with both hemispheres being involved in different ways in the processing of semantic knowledge.

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.

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)0.006
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 score0.334
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.031
GPT teacher head0.285
Teacher spread0.254 · 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