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

Dana Basel, Tamar Aviram, Amit Lazarov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)182-195
Number of pages14
JournalBehavior Therapy
Volume53
Issue number2
DOIs
StatePublished - Mar 2022

Funding

FundersFunder number
Israel Science Foundation374/20

    Keywords

    • attention allocation
    • attention bias
    • depression
    • emotion identification
    • eye-tracking

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