2016 Bulletin

Clinical Take Home Points: 1. Depending on mean RT or mean accuracy alone can easily lead to erroneous interpretations of performance. 2. The diffusion model represents a computational approach that utilizes the shape of the distribution for error and correct responses, to derive a set of parameters that can be used in a process-based approach to ask why a certain pattern of performance was obtained. 3. Standardized normative values for diffusion modeling (DDM) should be developed to effectively measure each component of the flow of information processing. 20 | Bulletin vol. 30 no. 1 that slow RTs in that population are not due to a slowing of drift, as might be assumed, but are instead because healthy aging is associated with slower non-decision times (i.e. motor preparation) and to wider boundaries (i.e. valuing accuracy over speed) (Ratcliff, Thapar, & McKoon, 2001). Studies of children with ADHD have consistently found slower drift rates relative to typically developing controls (Karalunas, Geurts, Konrad, Bender, & Nigg, 2014), and that slow drift rate is not only responsible for the slower and more variable RTs observed among children with ADHD, but can also account for impairments in working memory, motor disinhibition, sustained attention, and error monitoring (Huang-Pollock, Karalunas, Tam, & Moore, 2012; Huang-Pollock et al., in press; Karalunas & Huang-Pollock, 2013; Weigard & Huang-Pollock, in press). The diffusion model allows us the opportunity to become independent of the complex and homuncular EF construct. Rather than asking why children with ADHD have poor accuracy on tasks of WM capacity, make more failed inhibits on a go-no-go- task, and are slow and variable to detect targets on a continuous performance task, we may instead look more comprehensively across tasks and ask: why are they slower, more variable, and error prone? Diffusion modeling in clinical practice This comprehensive approach to task performance is consistent with the process approach used in clinical assessment, and has the potential to improve upon our current efforts to evaluate cognitive deficits and treatment response. However, translating this tool from research to clinical practice requires test developers to be aware of this problem, so that large representative normative values for the diffusion model parameters can be derived. Once developed, normative values for these parameters may prove to be an invaluable component of clinical neuropsychological assessments and research. For example, the implications of a medication that improves reaction times while increasing error rate is strikingly different than one that improves both reaction time and error rate. As another example, the utility of a rehabilitation program that reduces RTs through an improvement in drift rate, would be quite different than one that reduced RTs through speeding motor preparation. ADHD is the prototypical childhood psychiatric disorder associated with cognitive performance deficits. Because of this, new tools and insights into how cognitive deficits can be measured and monitored have broad application for any number of neuropsychiatric disorders, for initial assessment purposes, and for the evaluation of treatment outcomes, with implications well beyond ADHD itself. Figure 1. Reaction times (RT) are ultimately influenced by several interacting subprocesses. In most cases, the speed or efficiency with which a decision can be made is the construct that is of greatest interest (e.g. how rapidly can a person make the decision to "go" or "not go" on a test of motor inhibition?). But, RTs are also influenced by the time it takes to encode a stimulus, the time it takes to prepare and execute a motor response, and whether one tends to (or has been instructed to) emphasize speed over accuracy. Speed/Acc (boundary separation) Encoding (non-decision time) Decision (drift rate) Motor Preparation (non-decision time) RT

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