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Record W2108179838 · doi:10.1017/s0714980809990183

Manque du nom propre et effet de la modalité sur la capacité à reconnaître des personnes connues au cours du vieillissement normal

2009· article· en· W2108179838 on OpenAlexaff
Roxane Langlois, Francine Fontaine, Caroline Hamel, Sven Joubert

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2009
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsPsychologyModality (human–computer interaction)HumanitiesCognitionSemantic memoryContrast (vision)ArtDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACTThe aim of this study was to investigate the impact of aging on the ability a) to name famous faces and b) to access biographical knowledge about famous people from different modalities of presentation (faces and names). Healthy elderly subjects (n = 117) divided into three different age groups were assessed using a semantic memory test of famous people. Results of this study indicate a decline in naming performance between oldest and youngest groups. In contrast, no difference between groups was found in terms of the ability to access semantic knowledge about famous people. Finally, a main effect of modality of presentation (faces vs. names) was found, revealing greater ability to identify famous people in the verbal (names) than in the visual modality (faces). Taken together, results of this study may contribute to developing new cognitive intervention programs in older adults presenting with proper-name anomia.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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
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

Citations10
Published2009
Admission routes1
Has abstractyes

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