Stimulus-Driven Affective Change: Evaluating Computational Models of Affect Dynamics in Conjunction with Input

The way in which emotional experiences change over time can be studied through the use of computational models. An important question with regard to such models is which characteristics of the data a model should account for in order to adequately describe these data. Recently, attention has been drawn on the potential importance of nonlinearity as a characteristic of affect dynamics. However, this conclusion was reached through the use of experience sampling data in which no information was available about the context in which affect was measured. However, affective stimuli may induce some or all of the observed nonlinearity. This raises the question of whether computational models of affect dynamics should account for nonlinearity, or whether they just need to account for the affective stimuli a person encounters. To investigate this question, we used a probabilistic reward task in which participants either won or lost money at each trial. A number of plausible ways in which the experimental stimuli played a role were considered and applied to the nonlinear Affective Ising Model (AIM) and the linear Bounded Ornstein-Uhlenbeck (BOU) model. In order to reach a conclusion, the relative and absolute performance of these models were assessed. Results suggest that some of the observed nonlinearity could indeed be attributed to the experimental stimuli. However, not all nonlinearity was accounted for by these stimuli, suggesting that nonlinearity may present an inherent feature of affect dynamics. As such, nonlinearity should ideally be accounted for in the computational models of affect dynamics.