A neurocomputational account of reward and novelty processing and effects of psychostimulants in attention deficit hyperactivity disorder.

ABSTRACT: Computational models of reinforcement learning have helped dissect discrete components of reward-related function and characterize neurocognitive deficits in psychiatric illnesses. Stimulus novelty biases decision-making, even when unrelated to choice outcome, acting as if possessing intrinsic reward value to guide decisions toward uncertain options. Heightened novelty seeking is characteristic of attention deficit hyperactivity disorder, yet how this influences reward-related decision-making is computationally encoded, or is altered by stimulant medication, is currently uncertain. Here we used an established reinforcement-learning task to model effects of novelty on reward-related behaviour during functional MRI in 30 adults with attention deficit hyperactivity disorder and 30 age-, sex-

SUBMITTER: Sethi A 

PROVIDER: S-EPMC5917772 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature