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Looking While Unhappy

2008· article· en· W1985357152 on OpenAlexaff
Derek M. Isaacowitz, Kaitlin Toner, Deborah Goren, Hugh R. Wilson

Bibliographic record

VenuePsychological Science · 2008
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsYork UniversityUniversity of Waterloo
FundersNational Institute on Aging
KeywordsGazePsychologyMoodValence (chemistry)FeelingDevelopmental psychologyEye trackingYoung adultContrast (vision)Cognitive psychologySocial psychologyPsychoanalysis

Abstract

fetched live from OpenAlex

Recent findings that older adults gaze toward positively valenced stimuli and away from negatively valenced stimuli have been interpreted as part of their attempts to achieve the goal of feeling good. However, the idea that older adults use gaze to regulate mood, and that their gaze does not simply reflect mood, stands in contrast to evidence of mood-congruent processing in young adults. No previous study has directly linked age-related positive gaze preferences to mood regulation. In this eye-tracking study, older and younger adults in a range of moods viewed synthetic face pairs varying in valence. Younger adults demonstrated mood-congruent gaze, looking more at positive faces when in a good mood and at negative faces when in a bad mood. Older adults displayed mood-incongruent positive gaze, looking toward positive and away from negative faces when in a bad mood. This finding suggests that in older adults, gaze does not reflect mood, but rather is used to regulate it.

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)0.002
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 score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0200.003

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 head0.165
GPT teacher head0.464
Teacher spread0.299 · 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

Citations270
Published2008
Admission routes1
Has abstractyes

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