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Developing and testing a computational model of fear generalization

In anxiety disorders, fear does not remain specific to the stimulus that was originally linked to danger but generalises to a broad set of stimuli, resulting in a snowballing of complaints. Even though generalisation is a well-established phenomenon, the precise mechanisms underlying the spreading of fear remain unclear. The project aims to elucidate these mechanisms, building on previous work, demonstrating that fear generalisation can be experimentally induced through Pavlovian learning processes and that both intra- and interindividual variations in perception strongly affect fear generalisation via various manners. The aim of the project is to develop a computational model of response generalisation that disentangles 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). Apart from the development, the task is to design innovative and effective empirical tests of the model to rigorously test and validate it before implementing it in a clinical context.

Date:24 Nov 2020  →  Today
Keywords:Conditioned learning, Generalization, Perceptual discrimination, Cognitive modelling
Disciplines:Sensory processes and perception, Learning and behaviour, Mathematical psychology
Project type:PhD project