Computational perspectives on human fear and anxiety. (January 2023)

• Record Type: Journal Article
• Authors: Yamamori, Yumeya; Robinson, Oliver J.
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 144(2023)
• Publication Date: 2023-01
• Subjects: Anxiety -- Fear -- Computational modelling -- Generative models -- Reinforcement learning -- Approach-avoidance conflict -- Uncertainty -- Decision-making; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2022.104959
• Languages: English
• ISSNs: 0149-7634

Abstract

Abstract: Fear and anxiety are adaptive emotions that serve important defensive functions, yet in excess, they can be debilitating and lead to poor mental health. Computational modelling of behaviour provides a mechanistic framework for understanding the cognitive and neurobiological bases of fear and anxiety, and has seen increasing interest in the field. In this brief review, we discuss recent developments in the computational modelling of human fear and anxiety. Firstly, we describe various reinforcement learning strategies that humans employ when learning to predict or avoid threat, and how these relate to symptoms of fear and anxiety. Secondly, we discuss initial efforts to explore, through a computational lens, approach-avoidance conflict paradigms that are popular in animal research to measure fear- and anxiety-relevant behaviours. Finally, we discuss negative biases in decision-making in the face of uncertainty in anxiety. Highlights: There is increasing interest in computational approaches to human fear and anxiety. We focus on 'generative models' of fear-/anxiety-related behaviours. Key themes of aversive learning, approach-avoidance conflict, and uncertainty. We discuss candidate computational mechanisms of pathological fear/anxiety.