Woo‐Young Ahn

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM

It is a hallmark of a good model to make accurate a priori predictions to new conditions (Busemeyer & Wang, 2000). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models

Substance dependent individuals (SDI) often exhibit decision-making deficits; however, it remains unclear whether the nature of the underlying decision-making processes is the same in users of different classes of drugs and whether these deficits persist after discontinuation of drug use. We used computational modeling to address these questions in a unique sample of relatively "pure" amphetamine-dependent (N = 38) and heroin-dependent individuals (N = 43) who were currently in protracted abstin

A recent trend in decision neuroscience is the use of model-based fMRI using mathematical models of cognitive processes. However, most previous model-based fMRI studies have ignored individual differences due to the challenge of obtaining reliable parameter estimates for individual participants. Meanwhile, previous cognitive science studies have demonstrated that hierarchical Bayesian analysis is useful for obtaining reliable parameter estimates in cognitive models while allowing for individual

Political ideologies summarize dimensions of life that define how a person organizes their public and private behavior, including their attitudes associated with sex, family, education, and personal autonomy. Despite the abstract nature of such sensibilities, fundamental features of political ideology have been found to be deeply connected to basic biological mechanisms that may serve to defend against environmental challenges like contamination and physical threat. These results invite the prov

Background: Identifying objective and accurate markers of cocaine dependence (CD) can innovate its prevention and treatment. Existing evidence suggests that CD is characterized by a wide range of cognitive deficits, most notably by increased impulsivity. Impulsivity is multidimensional and it is unclear which of its various dimensions would have the highest predictive utility for CD. The machine learning approach is highly promising for discovering predictive markers of disease. Here we used mac

The Iowa Gambling Task (IGT) is widely used to study decision-making within healthy and psychiatric populations. However, the complexity of the IGT makes it difficult to attribute variation in performance to specific cognitive processes. Several cognitive models have been proposed for the IGT in an effort to address this problem, but currently no single model shows optimal performance for both short- and long-term prediction accuracy and parameter recovery. Here, we propose the Outcome-Represent

Abstract Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories, with which we can disentangle psychiatric conditions into basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assess and potentially diagnose psychiatric patients, and there is growing enthusiasm on RLDM and Computational Psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM proc

Lesbian, gay, and bisexual (LGB) individuals report higher levels of problematic alcohol and substance use than their heterosexual peers. This disparity is linked to the experience of LGB-specific stressors, termed minority stress. Additionally, bisexual individuals show increased rates of psychopathology, including problematic alcohol and substance use, above and beyond lesbian and gay individuals. However, not everyone experiencing minority stress reports increased rates of alcohol and substan

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

Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression research has followed from discrete emotion theories, which posit a limited number of distinct affective states that are represented with specific patterns of facial action. Much less work has focused on dimensional features of emotion, particularly positiv

Trait impulsivity—defined by strong preference for immediate over delayed rewards and difficulties inhibiting prepotent behaviors—is observed in all externalizing disorders, including substance-use disorders. Many laboratory tasks have been developed to identify decision-making mechanisms and correlates of impulsive behavior, but convergence between task measures and self-reports of impulsivity are consistently low. Long-standing theories of personality and decision-making predict that neurally