Simulating Hyperactivity in ADHD Using Reinforcement Learning

Hyperactivity is a key symptom in those diagnosed with Attention Deficit Hyperactivity Disorder. In the present work, we model hyperactivity in terms of a twoarmed bandit task from Reinforcement Learning, where initial state-values are set abnormally high. Extinction of these state-values when neither action is very rewarding induces repetitive switching between actions over a series of trials with a frequency that is proportional to the initial state-value. Here we propose that although setting initial values may be a useful exploration strategy, switching can become overly frequent or “hyperactive” when they are set too high. Keywords— ADHD, Hyperactivity, Reinforcement Learning, Exploration Strategy