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Record W2062438012 · doi:10.2466/pr0.95.2.615-630

Effects of Manipulating Valence and Arousal Components of Mood on Specificity of Autobiographical Memory

2004· article· en· W2062438012 on OpenAlexaff
Carolina McBride, Philippe Cappeliez

Bibliographic record

VenuePsychological Reports · 2004
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArousalPsychologyAutobiographical memoryValence (chemistry)MoodInternational Affective Picture SystemCognitionCognitive psychologyDevelopmental psychologyClinical psychologyRecallPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

An important cognitive deficit in clinical depression is the inability to be specific in recalling personal memories, a phenomenon coined "overgeneral memory" by Williams and Broadbent. Although there is general consensus that overgeneral memory is not state-dependent, most of the evidence originates from studies of this effect in clinical populations. The two components of mood, valence and arousal, were manipulated to examine their influence on memory specificity in a nonclinical sample of university undergraduate students. In Exp. 1, a Velten procedure was used to induce elated, depressed, or neutral mood states. No difference was found in autobiographical memory specificity among the three groups. In Exp. 2, high and low arousal states were induced through physical exercise. A low arousal state resulted in an increased proportion of overgeneral memories, suggesting that this memory phenomenon may be influenced by the arousal component of mood.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.330
Teacher spread0.291 · 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

Citations18
Published2004
Admission routes1
Has abstractyes

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