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Record W2026693891 · doi:10.1037/1528-3542.6.3.511

Is there an age-related positivity effect in visual attention? A comparison of two methodologies.

2006· article· en· W2026693891 on OpenAlexaff
Derek M. Isaacowitz, Heather A. Wadlinger, Deborah Goren, Hugh R. Wilson

Bibliographic record

VenueEmotion · 2006
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsYork University
FundersNational Institute on Aging
KeywordsSocioemotional selectivity theoryPsychologyEye trackingDevelopmental psychologyCognitive psychologyVisual attentionCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

Research suggests a positivity effect in older adults' memory for emotional material, but the evidence from the attentional domain is mixed. The present study combined 2 methodologies for studying preferences in visual attention, eye tracking, and dot-probe, as younger and older adults viewed synthetic emotional faces. Eye tracking most consistently revealed a positivity effect in older adults' attention, so that older adults showed preferential looking toward happy faces and away from sad faces. Dot-probe results were less robust, but in the same direction. Methodological and theoretical implications for the study of socioemotional aging are discussed.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.102
GPT teacher head0.501
Teacher spread0.399 · 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

Citations299
Published2006
Admission routes1
Has abstractyes

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