Focus on the positive: Computational simulations implicate asymmetrical reward prediction error signals in childhood attention-deficit/hyperactivity disorder

A number of hypotheses have suggested that the principal neurological dysfunction responsible for the behavioural symptoms associated with Attention-Deficit/Hyperactive Disorder (ADHD) is likely rooted in abnormal phasic signals coded by the firing rate of midbrain dopamine neurons. We present a formal investigation of the impact atypical phasic dopamine signals have on behaviour by applying a TD(λ) reinforcement learning model to simulations of operant conditioning tasks that have been argued to quantify the hyperactive, inattentive and impulsive behaviour associated with ADHD. The results presented here suggest that asymmetrically effective dopamine signals encoded by a punctate increase or decrease in dopamine levels provide the best account for the behaviour of children with ADHD as well as an animal model of ADHD, the spontaneously hypertensive rat (SHR). The biological sources of this asymmetry are considered, as are other computational models of ADHD.

Research Highlights
► ADHD is thought to be associated with abnormal dopaminergic activity.
► Dopamine function is well understood within a normative reinforcement learning framework.
► Behaviour of an animal model of ADHD, and children with ADHD were explored using a TD(lambda) model.
► A learning asymmetry, with more effective positive learning signals, best explained ADHD symptoms.