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Area of Science:

  • Psychology
  • Computational Neuroscience
  • Affective Science

Background:

  • Computational models are used to study how emotional experiences change over time.
  • Nonlinearity in affect dynamics has been proposed as an important characteristic, but its source is unclear.
  • Previous studies lacked context, leaving ambiguity on whether observed nonlinearity stems from internal dynamics or external stimuli.

Purpose of the Study:

  • To investigate whether nonlinearity in affect dynamics is an inherent feature or solely induced by affective stimuli.
  • To determine if computational models of affect dynamics need to incorporate nonlinearity.

Main Methods:

  • A probabilistic reward task was employed to elicit affective responses.
  • The nonlinear Affective Ising Model (AIM) and the linear Bounded Ornstein-Uhlenbeck (BOU) model were used.
  • Model performance was assessed by comparing their ability to describe the data, considering the role of experimental stimuli.

Main Results:

  • Experimental stimuli accounted for some, but not all, of the observed nonlinearity in affect dynamics.
  • Nonlinearity appears to be a characteristic intrinsic to the dynamics of affect itself.

Conclusions:

  • Nonlinearity is a crucial feature of affect dynamics that should be included in computational models.
  • Future models of emotional experience dynamics should account for inherent nonlinearity.