Affective dynamics in context: Testing the contextual parameter of the Affective Ising Model

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Understanding the dynamics of our emotional lives has proven a complex challenge, but one that has benefited greatly from recent methodological and technological developments. Based on high intensity affective data and statistical mechanics, Loossens et al. (in press) have proposed a computational model to capture these complexities. The Affective Ising Model (AIM) describes the dynamics of positive and negative affect, and can capture nonlinear, non-Gaussian tendencies in the data. However, contextual information has not yet been integrated into the model. We propose an experimental paradigm to elicit affective fluctuations within known contextual conditions and test the model's ability to capture affective dynamics in context. Participants engage in a simple gambling task with real monetary rewards, and report on their affective state between trials

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