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. Delay discounting, one dimension of impulsivity 1 , assesses how individuals make trade-offs between small but immediately available rewards versus large but delayed rewards. Delay discounting is broadly linked to normative cognitive and behavioral processes, such as financial decision making 2 , social decision making 3 , and personality 4 , among others. Also, individual differences in delay discounting are associated with several cognitive capacities, including working memory 5 , intelligence 6 , and top-down regulation of impulse control mediated by the prefrontal cortex 7,8. Delay discounting is a strong candidate endophenotype for a wide range of maladaptive behaviors, including addictive disorders 9,10 and health risk behaviors for a review, see 11. Studies of test-retest reliability of delay discounting demonstrate reliability both for adolescents 12 and adults 13 , and genetics studies indicate that delay discounting may be a heritable trait 9. As such, 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 14-16. The construct validity of delay discounting has been demonstrated in numerous studies. For example, the delay discounting task is widely used to assess (altered) temporal impulsivity of various psychiatric disorders, including patients with substance use disorders e.g., 11 , schizophrenia 13,14 , and bipolar disorder 14. Therefore, improved assessment of delay discounting may be beneficial to many fields, including psychology, neuroscience, medicine 15 , and economics. To link decision making tasks to mental functioning is a formidable challenge that requires simultaneously achieving multiple measurement goals. We focus on three aspects of measurement: reliability, precision, and efficiency. Reliable measurement of latent neurocognitive constructs or biological processes, such as impulsivity, reward sensitivity, or learning rate, is difficult. Recent advancements in neuroscience and computational psychiatry 16...

Number of words in the abstract: 135Number of words in the main text (including references): 7,600 AbstractMachine 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 minutes 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.

Alcohol use disorder is a destructive compulsion characterized by chronic relapse and poor recovery outcomes. Heightened reactivity to alcohol-associated stimuli and compromised executive function are hallmarks of alcohol use disorder. Interventions targeting these two interacting domains are thought to ameliorate these altered states, but the mutual brain sites of action are yet unknown. Although interventions on alcohol cue reactivity affect reward area responses, how treatments alter brain responses when subjects exert executive effort to delay gratification is not as well-characterized. Focusing on interventions that could be developed into effective clinical treatments, we review and identify brain sites of action for these two categories of potential therapies. Using activation likelihood estimation (ALE) meta-analysis, we find that interventions on alcohol cue reactivity localize to ventral prefrontal cortex, dorsal anterior cingulate, and temporal, striatal, and thalamic regions. Interventions for increasing delayed reward preference elicit changes mostly in midline default mode network regions, including posterior cingulate, precuneus, and ventromedial prefrontal cortex-in addition to temporal and parietal regions. Anatomical co-localization of effects appears in the ventromedial prefrontal cortex, whereas effects specific to delay-of-gratification appear in the posterior cingulate and precuneus. Thus, the current available literature suggests that interventions in the domains of cue reactivity and delay discounting alter brain activity along midline default mode regions, specifically in the ventromedial prefrontal cortex for both domains, and the posterior cingulate/precuneus for delay-of-gratification. We believe that these findings could facilitate targeting and development of new interventions, and ultimately treatments of this challenging disorder.

Our goal was to develop a behavioral measure of sensation seeking (SS). The Aroma Choice Task (ACT) assesses preference for an intense, novel, varied, and risky (exciting) option versus a mild, safe (boring) option using real-time odorant delivery. A total of 147 healthy young adults completed 40 binary choice trials. We examined (1) intensity and pleasantness of odorants, (2) stability of responding, (3) association with SS self-report, and (4) association with self-reported illicit drug use. Participants’ preference for the “exciting” option versus the safe option was significantly associated with self-reported SS ( p < .001) and illicit drug use ( p = .041). Odorant ratings comported with their intended intensity. The ACT showed good internal, convergent, and criterion validity. We propose that the ACT might permit more objective SS assessment for investigating the biological bases of psychiatric conditions marked by high SS, particularly addiction. The ACT measures SS behaviorally, mitigating some self-report challenges and enabling real-time assessment, for example, for functional magnetic resonance imaging (fMRI).

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