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Project

DynAMo: A computational model for affective dynamics

To a large extent, our well-being is determined by how we feel. As a result, affect has been an important topic of research but there remains a large explanatory gap between neurobiological findings and findings on both functional and dysfunctional human affective behavior. In this project, we propose DynAMo to close this gap: a biologically inspired and mathematically well-understood computational model that can be fitted to data. DynAMo is a network containing stochastic binary units that compute the key affective features of a stimulus, such as pleasantness and unpleasantness. DynAMo is able to explain in a unified way several major empirical findings at the behavioral level. In this project, it will be studied how the latent constructs in DynAMo can be measured through manifest variables, and a sound Bayesian methodology for statistical inference will be developed. The statistical methods will further be implemented efficiently in user-friendly software so that substantive affect researchers can fit DynAMo to their data. DynAMo will be put to the test in a number of prototypical data sets. The data are collected both in daily life and in the laboratory, from both normal persons and individuals with an affective disorder.

Date:1 Jan 2019 →  31 Dec 2021
Keywords:Psychopathologie
Disciplines:Data models, Health psychology