Identifying the Neuropathology of Altered Reinforcement Sensitivity in ADHD: A Review and Research Agenda.

M. Luman, G. Tripp, A. Scheres

Research output: Contribution to JournalArticleAcademicpeer-review


ADHD is associated with altered reinforcement sensitivity, despite a number of inconsistent findings. This review focuses on the overlap and differences between seven neurobiologically valid models and lists 15 predictions assessing reinforcement sensitivity in ADHD. When comparing the models it becomes clear that there are great differences in the level of explanation. For example, some models try to explain a single core deficit in terms lower-level reinforcement systems, such as the dopamine transfer to reward back in time. Other models explain multiple deficits, by describing higher-level systems, such as impaired bottom-up prefrontal activation. When reviewing the available experimental evidence in support of the predictions, most experimental studies have been focusing on behavioral changes in the face of reward and response cost over no-reward, and on delay discounting. There is currently a lack in studies that focus on explaining underlying cognitive or neural mechanisms of altered reinforcement sensitivity in ADHD. Additionally, there is a lack in studies that try to understand what subgroup of children with ADHD shows alterations in reinforcement sensitivity. The scarcity in studies testing the neurobiological predictions is explained partly by a lack in knowledge how to test some of these predictions in humans. Nevertheless, we believe that these predictions can serve as a useful guide to the systematic evaluation of altered reinforcement sensitivity in ADHD. © 2009 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)744-755
Number of pages10
JournalNeuroscience and Biobehavioral Reviews
Publication statusPublished - 2010


Dive into the research topics of 'Identifying the Neuropathology of Altered Reinforcement Sensitivity in ADHD: A Review and Research Agenda.'. Together they form a unique fingerprint.

Cite this