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.

No abstract

Function allocation between human and automation can be represented in terms of the stages & levels taxonomy proposed by Parasuraman, Sheridan & Wickens (2000). Higher degrees of automation (DOA) are achieved both by later stages (e.g., automation decision aiding rather than diagnostic aiding) and higher levels within stages (e.g. executing a choice unless vetoed, versus offering the human several choices). A meta analysis based on data of 14 experiments examines the mediating effects of DOA on routine system performance, performance when the automation fails, workload and situation awareness. The effects of DOA on these four measures are summarized by level of statistical significance. We found: (1) an inverse relationship between routine performance and workload as automation is introduced and DOA increases. (2) a weak positive relationship between routine performance and failure performance, as mediated by DOA. (3) A strong mediating role of situation awareness in improving both routine and failure performance.

We describe a computational model that predicts the decision aspect of sequential multitasking. We investigate how people choose to switch tasks or continue performing an ongoing task when they are in overload conditions where concurrent performance of tasks is impossible. The model is based on a metaanalytic integration of 46 experiments from two literatures: interruption management and applied task switching. Consistent trends from the meta-analysis are used to set parameters in the mathematical model, which is then implemented in a task network model.