Abstract
Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test–retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test–retest reliability of the discounting rate within 10–20 trials (under 1–2 min of testing), captured approximately 10% more variance in test–retest reliability, was 3–5 times more precise, and was 3–8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.
Highlights
Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning
We have demonstrated that adaptive design optimization (ADO) led to highly reliable, precise, and rapid measures of discounting rate
The results of this study are consistent with previous studies employing A DO29,46, showing improved precision and efficiency
Summary
Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting. Delay discounting has received attention in the developing field of precision medicine in mental health as a potentially rapid and reliable (bio)marker of individual differences relevant for treatment outcomes[14,15,16]. Recent advancements in neuroscience and computational psychiatry[16,17] provide novel frameworks, cognitive tasks, and latent constructs that allow us to investigate the neurocognitive mechanisms underlying psychiatric conditions; their reliabilities have not been rigorously tested or are not yet acceptable[18]. A by-product of low task efficiency is that the amount of data (e.g., number of participants) typically available for big data approaches to studying psychiatry is smaller than in other fields
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