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Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

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.

SUBMITTER: Ahn WY

PROVIDER: S-EPMC7374100 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

Ahn Woo-Young WY Gu Hairong H Shen Yitong Y Haines Nathaniel N Hahn Hunter A HA Teater Julie E JE Myung Jay I JI Pitt Mark A MA

Scientific reports 20200721 1

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 al ...[more]

PMID: 32694654

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Project description:Current literature is divided over whether and how processes such as perspective taking and reward sensitivity differ between individuals with autism spectrum disorder (ASD) versus neurotypical individuals. Discounting tasks may provide novel insight into how these processes operate. In delay discounting tasks, participants choose between smaller immediate rewards and larger delayed rewards, and in social discounting tasks, participants choose between a smaller monetary rewards for themselves versus a larger reward for partners of varied social distance (e.g., a close friend vs. an acquaintance). Delay and social discounting tasks thus implicitly measure the subjective value of rewards given to one's future self and to others, capturing constructs such as perspective taking, reward processing, and social closeness, all of which have been discussed as core cognitive mechanisms underlying ASD. Despite extensive research on discounting in other clinical populations, few studies have examined delay discounting in ASD and no research has examined social discounting in ASD. The goal of the current study was to assess delay and social discounting for monetary rewards in a single sample of adolescents and adults with ASD compared to a matched neurotypical sample. Overall, adults and adolescents with ASD valued both future rewards and rewards given to others less than their typical counterparts did, but rates of discounting were not significantly correlated across temporal and social domains. These results extend an important behavioral paradigm for understanding both perspective taking and reward processing to autism. Autism Res 2019, 12: 870-877. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Discounting tasks-which experimentally measure the subjective value of different rewards-have been used with a variety of clinical populations, but are underexplored in ASD. We found that compared to neurotypical individuals, individuals with ASD showed diminished subjective value for future rewards (compared to immediate rewards) and rewards for others (compared to rewards for self). This finding has implications for understanding perspective taking, reward processing, and social closeness in ASD.

Dataset Information

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

Publications

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

Similar Datasets

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