Lack of an Attention Bias Away From Relatively Negative Faces in Dysphoria Is Not Related to Biased Emotion Identification.


Journal

Behavior therapy
ISSN: 1878-1888
Titre abrégé: Behav Ther
Pays: England
ID NLM: 1251640

Informations de publication

Date de publication:
03 2022
Historique:
received: 06 04 2021
revised: 18 07 2021
accepted: 19 07 2021
entrez: 1 3 2022
pubmed: 2 3 2022
medline: 6 5 2022
Statut: ppublish

Résumé

Eye-tracking-based attention research has consistently shown a lack of a normative attentional bias away from dysphoric face stimuli in depression, characterizing the attention system of non-depressed individuals. However, this more equal attention allocation pattern could also be related to biased emotion identification, namely, an inclination of depressed individuals to attribute negative emotions to non-negative stimuli when processing mood-congruent stimuli. Here, we examined emotion identification as a possible mechanism associated with attention allocation when processing emotional faces in depression. Attention allocation and emotion identification of participants with high (HD; n = 30) and low (LD; n = 30) levels of depression symptoms were assessed using two corresponding tasks previously shown to yield significant findings in depression, using the same face stimuli (sad, happy, and neutral faces) across both tasks. We examined group differences on each task and possible between-task associations. Results showed that while LD participants dwelled longer on relatively positive faces compared with relatively negative faces on the attention allocation task, HD participants showed no such bias, dwelling equally on both. Trait anxiety did not affect these results. No group differences were noted for emotion identification, and no between-task associations emerged. Present results suggest that depression is characterized by a lack of a general attention bias toward relatively positive faces over relatively negative faces, which is not related to a corresponding bias in emotion identification.

Identifiants

pubmed: 35227397
pii: S0005-7894(21)00098-8
doi: 10.1016/j.beth.2021.07.001
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

182-195

Informations de copyright

Copyright © 2021. Published by Elsevier Ltd.

Auteurs

Dana Basel (D)

Tel Aviv University.

Tamar Aviram (T)

Tel Aviv University.

Amit Lazarov (A)

Tel Aviv University. Electronic address: amitlaza@tauex.tau.ac.il.

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Classifications MeSH