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...

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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.

The goal of the current study was to examine the relationship between early physical victimization and long-term mental health outcomes in a sample of first responder police and firefighter personnel involved in the relief efforts after Hurricane Katrina. Participants included 441 Biloxi and Gulfport Police and Firefighters. One fifth of participants reported having experienced physical victimization before age 18. After controlling for age, relationship status, and disaster exposure, early physical victimization was modestly associated with symptoms of PTSD, peritraumatic dissociation, depression, and sleep problems. The results suggest that early physical victimization might be a risk factor for mental health problems in police and fire personnel responding to mass disaster, pointing to the importance of developing interventions to mitigate risk related to a history of physical victimization in first-responders.

Unintentional drug overdose has become a grave and sustained public health burden in the US. 1 The US Centers for Disease Control and Prevention (CDC) defines unintentional drug overdose as occurring "…when no harm is intended." 2(p1) and inclusive of "…overdoses resulting from drug misuse, drug abuse, and taking too much of a drug for medical reasons." 2(p1) Adult decedents have been the focus of most overdose mortality reports, despite the fact that adolescents (aged 10-19 years) and young people (aged 10-24 years) are increasingly dying of unintentional drug overdose. 3 This troubling trend requires further study, given that adolescents and young people are deprived of many more years of work, community life, and family life than are older individuals dying of unintentional drug overdose.To our knowledge, no prior study has assessed unintentional drug overdose mortality among adolescents and young people in years of life lost (YLL). YLL is an epidemiologic descriptor that gives weight to deaths among the young. 4 YLL analysis has the potential to provide important context to the overdose crisis by better representing what is meant to society by the loss of adolescents and young people to unintentional drug overdose. The present work aimed to fill this important gap in the literature by calculating unintentional drug overdose YLL in this vulnerable population.