Trait Anxiety Influences Negative Affect-modulated Distribution of Visuospatial Attention

心理学 认知心理学 厌恶 焦虑 特质焦虑 特质 情感(语言学) 发展心理学 愤怒 沟通 社会心理学 计算机科学 精神科 程序设计语言
作者
Gursimran Kaur,Rakshita Anand,Mrinmoy Chakrabarty
出处
期刊:Neuroscience [Elsevier]
卷期号:509: 145-156 被引量:2
标识
DOI:10.1016/j.neuroscience.2022.11.034
摘要

Visuospatial attention allows humans to selectively gate and prioritize visual (including salient, emotional) information for efficiently navigating natural visual environments. As emotions have been known to influence attentional performance, we asked if emotions also modulate the spatial distribution of visual attention and whether any such effect was further associated with individual differences in anxiety. Participants (n = 28) discriminated the orientation of target Gabor patches co-presented with distractors, speedily and accurately. The key manipulation was randomly presenting a task-irrelevant face emotion prime briefly (50 ms), conveying Neutral/Disgust/Scrambled (Null) emotion signal 150 ms preceding the target patches. We calculated attention gradient (change in negative inverse attentional efficiency with unit change in distance from the source of emotion signal) as a metric to answer our questions. Specifically, the Disgust signal modulated the direction of attention gradients differentially in individuals with varying degrees of trait - anxiety, such that the gradients correlated negatively with individual trait-anxiety scores. This implies spatial shifts in Disgust-signalled visual attention with varying trait - anxiety levels. Neutral yielded attention gradients comparable to Scrambled, implying no specific effect of this signal and there was no association with anxiety levels in both. No correlation was observed between state - anxiety and the emotion-cued attention gradients. In sum, the results suggest that individual trait - anxiety levels influence the effect of negative and physiologically arousing emotion signals (e.g., Disgust) on the spatial distribution of visual attention. The findings could be of relevance for understanding biases in visual behaviour underlying affective states and disorders.
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