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Record W4410403153 · doi:10.1037/emo0001538

Peripheral information’s effect on emotional intensity depends on depression level.

2025· article· en· W4410403153 on OpenAlexfundno aff
Tamar Amishav, Nilly Mor

Bibliographic record

VenueEmotion · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
FundersAzrieli FoundationJoseph and Harvey Meyerhoff Family Charitable Funds
KeywordsPsychologyDepression (economics)PeripheralIntensity (physics)Cognitive psychologyDevelopmental psychologyMedicineOpticsInternal medicine

Abstract

fetched live from OpenAlex

This research examined the effect of peripheral information on emotional responses and depression-related differences in this effect. In two experiments, undergraduate students, representing a subclinical sample with varying levels of depression, rated their emotional responses to neutral and negative target pictures. The target pictures were presented alone or with negative and neutral peripheral pictures (Study 1), or with negative and positive pictures (Study 2). As predicted, across studies, depressive symptoms were associated with more negative emotional responses to neutral pictures when these were presented in the context of peripheral negative pictures as compared to neutral or positive peripheral pictures. Contrary to predictions, positive peripheral pictures did not attenuate responses to negative target pictures, and depression did not moderate the effect of positive information on emotional responses. These results highlight the potential impact of contextual negative peripheral information on the emotional responses of individuals with depressive symptoms and suggest avenues for exploring interventions aimed at modifying negative affective responses. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.021
GPT teacher head0.332
Teacher spread0.311 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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