Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning

- The review analyses core neurobiological theories of ADHD.
- Derives their predictions for computational models of decision-making and learning.
- Reviews current empirical studies applying computational modelling in ADHD.
- Theories imply distinct combinations of computational modelling parameters.
- Empirical studies agree with theories' implied lowered DDM drift rate and reduced reinforcement learning choice sensitivity.

Attention deficit hyperactivity disorder (ADHD) is characterized by altered decision-making (DM) and reinforcement learning (RL), for which competing theories propose alternative explanations. Computational modelling contributes to understanding DM and RL by integrating behavioural and neurobiological findings, and could elucidate pathogenic mechanisms behind ADHD.This review of neurobiological theories of ADHD describes predictions for the effect of ADHD on DM and RL as described by the drift-diffusion model of DM (DDM) and a basic RL model. Empirical studies employing these models are also reviewed.While theories often agree on how ADHD should be reflected in model parameters, each theory implies a unique combination of predictions. Empirical studies agree with the theories' assumptions of a lowered DDM drift rate in ADHD, while findings are less conclusive for boundary separation. The few studies employing RL models support a lower choice sensitivity in ADHD, but not an altered learning rate.The discussion outlines research areas for further theoretical refinement in the ADHD field.