Event rate effects on children with attention-deficit/hyperactive disorder: Test predictions from the moderate brain arousal model and the neuro-energetics theory using the diffusion decision model

Abstract

BackgroundConverging evidence has found that the inhibitory control of children with attention-deficit/hyperactive disorder (ADHD) is context-dependent and particularly susceptible to the event rate. The Moderate Brain Arousal (MBA) model predicts a U-shaped curve between event rate and performance as a modulation of brain arousal. The neuroenergetics theory (NeT) proposes that a smaller event rate results in neuronal fatigue and subsequent descent performance. However, previous work applied the traditional one-dimensional index of performance, such as accuracy rate and response time, which might limit the exploration of the event rate effect on the specific underlying process. AimsWe used a diffusion decision model (DDM) to study the influence of event rate on inhibition control in children with ADHD and verified the explanation of the MBA model and the NeT. Methods and proceduresThe Stop Signal Task manipulated by four event rate conditions was conducted with 24 children with ADHD (mean age=8.5, males=16) and 29 typical developmental children (TDC) (mean age=9.0, males=12). DDM was applied to compare the differences in the DDM parameters across different event rates. Outcomes and resultsCompared with TDC, children with ADHD had a smaller drift rate, longer non-decision time, and smaller boundary separation. Although the event rate had little influence on ADHD, the drift rate of the TDC was approximately linear with an increased event rate, and the Ter had a quadratic function relationship with the event rate. Conclusions and implicationsThe event rate effect may influence children’s performance through dual mechanisms. Neuronal energy supply could regulate information processing and brain arousal to regulate the activation of primary stimuli encoding and motor control. Insight into the multi-mechanism of ADHD cognition deficits would be helpful for clinicians in making objective diagnoses and effective targeted treatments.