To Risk or Not to Risk: Anxiety and the Calibration Between Risk Perception and Danger Mitigation

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Abstract

Anxiety prepares an organism for dealing with threats by recruiting cognitive resources to process information about the threat, and by engaging physiological systems to prepare a response. Heightened trait anxiety is associated with biases in both these processes: high trait-anxious individuals tend to report heightened risk perceptions, and inappropriate engagement in danger mitigation behavior. However, no research has addressed whether the calibration between risk perception and danger mitigation behavior is affected by anxiety, though it is well recognized that this calibration is crucial for adaptive functioning. The current study aimed to examine whether anxiety is characterized by better or worse calibration of danger mitigation behavior to variations in risk magnitude. Low and high trait-anxious participants were presented with information about the likelihood and severity of a danger (loud noise burst) on each trial. Participants could decide to mitigate this danger by investing a virtual coin, at the cost of losing danger mitigation ability on subsequent trials. Importantly, level of risk likelihood and severity were varied independently, and the multiplicative relationship between the 2 defined total danger. Multilevel modeling showed that the magnitude of total danger predicted the probability of coin investments, over and above the effects of risk likelihood and severity, suggesting that participants calibrated their danger mitigation behavior to integrated risk information. Crucially, this calibration was affected by trait anxiety, indicating better calibration in high trait-anxious individuals. These results are discussed in light of existing knowledge and models of the effect of anxiety on risk perception and decision-making.
Original languageEnglish
Pages (from-to)1-11
JournalJOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION
Volume42
Issue number6
DOIs
Publication statusPublished - Jun 2016

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