We test our neurocomputational model of fronto-striatal dopamine (DA) and noradrenaline (NA) function for understanding cognitive and motivational deficits in attention deficit/hyperactivity disorder (ADHD). Our model predicts that low striatal DA levels in ADHD should lead to deficits in 'Go' learning from positive reinforcement, which should be alleviated by stimulant medications, as observed with DA manipulations in other populations. Indeed, while nonmedicated adult ADHD participants were impaired at both positive (Go) and negative (NoGo) reinforcement learning, only the former deficits were ameliorated by medication. We also found evidence for our model's extension of the same striatal DA mechanisms to working memory, via interactions with prefrontal cortex. In a modified AX-continuous performance task, ADHD participants showed reduced sensitivity to working memory contextual information, despite no global performance deficits, and were more susceptible to the influence of distractor stimuli presented during the delay. These effects were reversed with stimulant medications. Moreover, the tendency for medications to improve Go relative to NoGo reinforcement learning was predictive of their improvement in working memory in distracting conditions, suggestive of common DA mechanisms and supporting a unified account of DA function in ADHD. However, other ADHD effects such as erratic trial-to-trial switching and reaction time variability are not accounted for by model DA mechanisms, and are instead consistent with cortical noradrenergic dysfunction and associated computational models. Accordingly, putative NA deficits were correlated with each other and independent of putative DA-related deficits. Taken together, our results demonstrate the usefulness of computational approaches for understanding cognitive deficits in ADHD.