Browsing by Author "Ahn, Woo-Young"

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    Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm
    (Nature Publishing group, 2020-07-21) Ahn, Woo-Young; Gu, Hairong; Shen, Yitong; Haines, Nathaniel; Hahn, Hunter A.; Teater, Julie E.; Myung, Jay I.; Pitt, Mark A.; Psychiatry, School of Medicine
    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.
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    Resting-state EEG, impulsiveness, and personality in daily and nondaily smokers
    (Elsevier, 2016-01) Rass, Olga; Ahn, Woo-Young; O'Donnell, Brian F.; Department of Psychiatry, IU School of Medicine
    OBJECTIVES: Resting EEG is sensitive to transient, acute effects of nicotine administration and abstinence, but the chronic effects of smoking on EEG are poorly characterized. This study measures the resting EEG profile of chronic smokers in a non-deprived, non-peak state to test whether differences in smoking behavior and personality traits affect pharmaco-EEG response. METHODS: Resting EEG, impulsiveness, and personality measures were collected from daily smokers (n=22), nondaily smokers (n=31), and non-smokers (n=30). RESULTS: Daily smokers had reduced resting delta and alpha EEG power and higher impulsiveness (Barratt Impulsiveness Scale) compared to nondaily smokers and non-smokers. Both daily and nondaily smokers discounted delayed rewards more steeply, reported lower conscientiousness (NEO-FFI), and reported greater disinhibition and experience seeking (Sensation Seeking Scale) than non-smokers. Nondaily smokers reported greater sensory hedonia than nonsmokers. CONCLUSIONS: Altered resting EEG power in daily smokers demonstrates differences in neural signaling that correlated with greater smoking behavior and dependence. Although nondaily smokers share some characteristics with daily smokers that may predict smoking initiation and maintenance, they differ on measures of impulsiveness and resting EEG power. SIGNIFICANCE: Resting EEG in non-deprived chronic smokers provides a standard for comparison to peak and trough nicotine states and may serve as a biomarker for nicotine dependence, relapse risk, and recovery.