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Project

Can Bayes solve the conundrum of generalization? A computational account of fear generalization.

In anxiety disorders fear does not remain specific to the stimulus that was originally linked to danger but generalizes to a broad set of stimuli, resulting in a snowballing of complaints. Despite the fact that generalization is a well-established phenomenon, the precise mechanisms underlying the spreading of fear remain unclear, rendering its treatment a challenging endeavor. The project aims to elucidate these mechanisms, building on our previous work demonstrating that fear generalization can be experimentally induced through Pavlovian learning processes, and that both intra- and interindividual variations in perception strongly affect generalization. A computational approach will be used to disentangle the distinct contribution of perceptual, learning and memory processes both in healthy volunteers and anxiety patients. These effects will be studied at both the self-report level as well as the psychophysiological level (e.g., skin conductance). This project has the potential to dramatically increase our understanding of the mechanisms driving the spreading of fear. Such knowledge will foster the development of person-tailored clinical practices to treat excessive fear.

Date:1 Jan 2020 →  31 Dec 2023
Keywords:anxiety disorders, Pavlovian learning processes, spreading of fear, Bayes, fear generalization
Disciplines:Psychopathology, Psychological methods not elsewhere classified, Learning and behaviour, Cognitive processes, Sensory processes and perception