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Record W2032949149 · doi:10.1080/13825585.2011.568047

The time course of access to semantic information in high-performing older adults: Behavioral evidence for the hemispheric asymmetry reduction in OLDer individuals

2011· article· en· W2032949149 on OpenAlexafffund
Ensie Abbassi, Yves Joanette

Bibliographic record

VenueAging Neuropsychology and Cognition · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
FundersCanadian Institutes of Health Research
KeywordsPsychologyPriming (agriculture)Young adultCognitive psychologyDevelopmental psychologyTask (project management)Audiology

Abstract

fetched live from OpenAlex

The possibility that the HAROLD phenomenon (i.e., Hemispheric Asymmetry Reduction in OLDer adults) is manifested in the course of access to semantic information, in particular the meaning of emotional words, was investigated using the visual half-field priming paradigm. The time course of priming was tracked in the cerebral hemispheres across three SOAs: 150, 300, and 750 ms. The results showed older and young adults had the same level of accuracy. While priming occurred unilaterally in young participants, the pattern of priming in older participants appeared to be bilateral whenever it was present, that is, at the 300- and 750-ms SOAs. The delay in the appearance of priming in older adults may be due to an increase in sensory thresholds that causes older adults to need more time to encode stimuli and fully activate their semantic network. It is concluded that the bilateral pattern of priming in the presence of an equivalent level of performance in older adults provides behavioral evidence supporting the compensatory role of the HAROLD phenomenon for this particular task.

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)0.000
metaresearch head score (Gemma)0.001
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
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.120
GPT teacher head0.384
Teacher spread0.264 · 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

Citations4
Published2011
Admission routes2
Has abstractyes

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