A computational account of threat-related attentional bias

Toby Wise, Jochen Michely, Peter Dayan and Raymond J Dolan

PLOS Computational Biology, 2019, vol. 15, issue 10, 1-21

Abstract: Visual selective attention acts as a filter on perceptual information, facilitating learning and inference about important events in an agent’s environment. A role for visual attention in reward-based decisions has previously been demonstrated, but it remains unclear how visual attention is recruited during aversive learning, particularly when learning about multiple stimuli concurrently. This question is of particular importance in psychopathology, where enhanced attention to threat is a putative feature of pathological anxiety. Using an aversive reversal learning task that required subjects to learn, and exploit, predictions about multiple stimuli, we show that the allocation of visual attention is influenced significantly by aversive value but not by uncertainty. Moreover, this relationship is bidirectional in that attention biases value updates for attended stimuli, resulting in heightened value estimates. Our findings have implications for understanding biased attention in psychopathology and support a role for learning in the expression of threat-related attentional biases in anxiety.Author summary: To make inferences and learn efficiently in the face of a multiplicity of stimuli we need to allocate attention preferentially to those that are most motivationally relevant. It is unclear how this is achieved in aversive environments. We investigated how value (the likelihood of an unpleasant event) and uncertainty (akin to ignorance about its probability) influence visual attention during aversive learning. Our results show that attention is influenced by value but not by uncertainty. Attention in turn results in heightened value estimates for attended stimuli. The findings have implications for understanding the development of pathological threat-related attentional biases that are a feature of anxiety disorders.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007341

DOI: 10.1371/journal.pcbi.1007341

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