Striking A Balance Between Psychometric Integrity and Efficiency for Assessing Reinforcement Learning and Working Memory in Psychosis-Spectrum Disorders
2021-06
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Striking A Balance Between Psychometric Integrity and Efficiency for Assessing Reinforcement Learning and Working Memory in Psychosis-Spectrum Disorders
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2021-06
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Cognitive deficits are well-established in psychosis-spectrum disorders and are highly related to functional outcomes for those individuals. Therefore, it is imperative to measure cognition in reliable and replicable ways, particularly when assessing for change over time. Notably, despite revolutionizing our measurement of specific cognitive abilities, parameters from computational models are rarely psychometrically assessed. Cognitive tests often include vast numbers of trials in order to increase psychometric properties, however long tests cause undue stress on the participant, limit the amount of data that can be collected in a study, and may even result in a less accurate measurement of the domain of interest. Thus, balancing psychometrics with efficiency can lead to better assessments of cognition in psychosis. The goal of this dissertation is to establish the psychometric properties and replicability of reinforcement learning and working memory tasks and determine the extent to which they could be made more efficient without sacrificing the psychometric integrity. The results provide support that these tests of reinforcement learning are appropriate for use in studies with only one time point but may not currently be appropriate for retest studies due to the inherent learning that occurs during the first time performing the task. The working memory tasks are ready for use in intervention studies, with the computational parameters of working memory appearing slightly less reliable than observed measures, but potentially more sensitive to detecting group differences. Lastly, these reinforcement learning and working memory tasks can be made 25%-50% more efficient without sacrificing reliability and optimized by focusing on items yielding the most information. Altogether, this dissertation provides guidance for using reinforcement learning and working memory tests in studies of cognition in psychosis in the most appropriate, efficient, and effective ways.
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University of Minnesota Ph.D. dissertation. June 2021. Major: Psychology. Advisor: Angus MacDonald. 1 computer file (PDF); vi, 170 pages.
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Pratt, Danielle. (2021). Striking A Balance Between Psychometric Integrity and Efficiency for Assessing Reinforcement Learning and Working Memory in Psychosis-Spectrum Disorders. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/224656.
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