Modeling Optimal Arousal Theory by Integrating Memory Activation, Motor Learning, and Temporal Cognition

Abstract

Mind wandering is caused by saturation (boredom) with the task. In other words, cognitive resources required to perform the task decrease with mastery on the task. The surplus resources, which increase with mastery, are naturally directed to thinking pattern that is usually used in default mode activity. Initially, mind wandering only consumes surplus resources. However, as it continues, the feedback loop accompanied with mind-wandering is strengthened and begins to affect task performance. In this study, we represent this process by combining functions of ACT-R (cognitive architecture) using declarative memory activation. In addition, we depict the task mastery by motor learning using feedback from online task performance. Furthermore, stochastic fluctuations are introduced into the memory activation to change the transition probability between the loops of thinking. These fluctuations are assumed to correspond to parasympathetic activity, which increase with time and decrease with novel stimuli. Simulations with implemented mechanism have shown the learning curve that is consistent with optimal level theory of arousal (inverted U-shaped curve).