Description
In this project we want to achieve a thorough understanding of emotion dynamics through stochastic nonlinear dynamical systems models. The models we use are based on underlying Ising-type networks and can thus help to relate basic neurophysiological principles on the one hand with complex behavior on the other hand. In particular, we want to model the nonlinear response of a person’s emotions regulatory network to the most important affective features of a stimulus (e.g., pleasantness and unpleasantness). The PhD research will consist of studying the mathematical properties of the model, developing a methodology for statistical inference (because the parameters of the model are different for every individual and need to be determined based on behavioral data) and programming a user-friendly software so that other researchers can easily fit the model to their data. The model will be tested on a number of prototypical data sets. The data are collected both in the lab and in daily life, from both normal persons and individuals with an emotional disorder.
For details and application visit original source
No comments:
Post a Comment