Relieving the Burden: Identifying Diminishing Returns to Create More Efficient Tasks (original) (raw)

2023

Abstract

Background: If well-constructed, efficient measures of cognition have the potential to increase validity, while decreasing research burden and costs for participants and assessors with little loss of reliability. There is the possibility that long and difficult measures create even noisier data for people with serious mental illness compared to the general population. In this study, we aim to assess the extent to which working memory and reinforcement learning tasks can be made more efficient.Methods: Participants included 185 people with serious mental illness and 75 controls. Internal consistency (Cronbach’s , Item-Total Correlations, and/or Spearman-Brown Prophecy Correlations) and test-retest reliability (Intraclass Correlations) were calculated for increasing subsets of trials on each task to assess the point at which reliability reached acceptable and good levels or reached diminishing returns.Results: Generally, the tasks had met acceptable internal consistency values by the time only half of the values had been included and either good reliability or diminishing returns by the time two-thirds of the task trials were considered. This was largely similar for test-retest on the working memory tasks, but acceptable test-retest reliability was mostly never achieved on the reinforcement learning tasks.Conclusions: Overall, each of the working memory and reinforcement learning tasks can be made 25-50% more efficient without significant loss of psychometric integrity. However, there may be limitations on the utility of some of the tasks due to acceptable test-retest reliability never being achieved.

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