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Deficient reinforcement learning in medial frontal cortex as a model of dopamine-related motivational deficits in ADHD

Neural Networks
DOI: 10.1016/j.neunet.2013.05.008
  • Adhd
  • Acc
  • Reinforcement Learning
  • Dopamine
  • Prediction Error
  • Reward Expectation
  • Biology
  • Computer Science
  • Law
  • Medicine


Abstract Attention Deficit/Hyperactivity Disorder (ADHD) is a pathophysiologically complex and heterogeneous condition with both cognitive and motivational components. We propose a novel computational hypothesis of motivational deficits in ADHD, drawing together recent evidence on the role of anterior cingulate cortex (ACC) and associated mesolimbic dopamine circuits in both reinforcement learning and ADHD. Based on findings of dopamine dysregulation and ACC involvement in ADHD we simulated a lesion in a previously validated computational model of ACC (Reward Value and Prediction Model, RVPM). We explored the effects of the lesion on the processing of reinforcement signals. We tested specific behavioral predictions about the profile of reinforcement-related deficits in ADHD in three experimental contexts; probability tracking task, partial and continuous reward schedules, and immediate versus delayed rewards. In addition, predictions were made at the neurophysiological level. Behavioral and neurophysiological predictions from the RVPM-based lesion-model of motivational dysfunction in ADHD were confirmed by data from previously published studies. RVPM represents a promising model of ADHD reinforcement learning suggesting that ACC dysregulation might play a role in the pathogenesis of motivational deficits in ADHD. However, more behavioral and neurophysiological studies are required to test core predictions of the model. In addition, the interaction with different brain networks underpinning other aspects of ADHD neuropathology (i.e., executive function) needs to be better understood.

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