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Computational models, in conjunction with (neuro)cognitive tests, are increasingly used to understand the cognitive characteristics of participants with attention-deficit/hyperactivity disorder (ADHD). We reviewed 50 studies from a broad range of cognitive tests for ADHD to synthesize findings and to summarize the new insights provided by three commonly applied computational models (i.e., diffusion decision models, absolute accumulator models, ex-Gaussian distribution models). Four areas are discussed to improve the utility of (neuro)cognitive testing for ADHD (a) the requirements for appropriate application of the computational models; (b) the consideration of sample characteristics and neurophysiological measures; (c) the integration of findings from cognitive psychology into the literature of cognitive testing to reconcile mixed evidence; and (d) future directions for the study of ADHD endophenotypes. We illustrate how computational models refine our understanding of cognitive concepts (slow processing speed, inhibition failures) presumed to characterize ADHD. We also show that considering sample characteristics and integrating findings from computational models and neurophysiological measures provide evidence for ADHD endophenotype-specific cognitive characteristics. However, studying the cognitive characteristics of ADHD endophenotypes often lies beyond the scope of existing research for three reasons some cognitive tests lack sensitivity to detect clinical characteristics; analysis methods do not allow the study of subtle cognitive differences; and the precategorization of participants restricts the study of symptom severity on a continuous spectrum. We provide recommendations for cognitive testing, computational modeling, and integrating electrophysiological measures to produce more valuable tools in research and clinical practice (above and beyond the research domain of ADHD). G Protein activator (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Among Black Americans, young adulthood marks an important developmental period for the initiation of problematic substance use and related health disparities. Unique cultural factors, such as discrimination and racial identity, influence substance use during this period, but it is unknown whether they influence more severe patterns such as polysubstance use. The current study identifies prototypical patterns of substance use among Black young adults and examines whether cultural factors are associated with those patterns.

The current study used latent class analysis to characterize prototypical substance use among a community-based sample of Black young adults (N = 147). Culturally relevant correlates of substance use classes (i.e., perceived discrimination and racial identity) were examined in univariate and multivariate models.

Five profiles of substance use emerged, including three characterized by polysubstance use. In univariate models only, racial identity was differentially associated with membership in classes characterized by co-use of alcohol and cannabis. In both models, discrimination was significantly associated with membership in a class characterized by polysubstance use.

Findings suggest a need to integrate cultural resilience factors into substance use interventions for Black young adults. Helping Black youth build coping skills for discrimination may also confer resilience against polysubstance use and associated health disparities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Findings suggest a need to integrate cultural resilience factors into substance use interventions for Black young adults. Helping Black youth build coping skills for discrimination may also confer resilience against polysubstance use and associated health disparities. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Alcohol use disorder (AUD) is associated with passive avoidance learning (PAL) deficits. This study investigated PAL deficits in AUD by using a novel growth model approach to quantify patterns of PAL as changes in false alarms over time, rather than the typical index of total false alarms in a PAL task.

Subjects, 112 (58 men; 54 women) with an AUD and 110 controls (44 men; 66 women), were administered a monetary incentive Go/No-Go task. Subjects could win $0.25 for a hit (response after a GO) or lose $0.25 for a false alarm.

PAL rate was quantified as the slope of initial learning phase (across the first 5 blocks) on the Go/No-Go task. The PAL curves indicated rapid learning in first 5 blocks followed by a later slower learning across blocks 6-9 (consolidation phase). A piecewise growth model with random intercepts indicated that AUD status was significantly associated with a slower initial PAL (i.e. learning phase), with B = -0.69, p < 0.001 for the control group and a PAL slope of 0.13 higher for the AUD group indicating a slower learning rate in the AUD group. This effect was not observed in the consolidation phase.

The results suggest that those with an AUD have greater difficulty learning to avoid negative consequences compared with controls. The results also suggest that measuring PAL rate by focusing on the rate of learning early in the task may be a better index of PAL learning than simply looking at overall false alarm rate. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The results suggest that those with an AUD have greater difficulty learning to avoid negative consequences compared with controls. The results also suggest that measuring PAL rate by focusing on the rate of learning early in the task may be a better index of PAL learning than simply looking at overall false alarm rate. (PsycInfo Database Record (c) 2020 APA, all rights reserved).We study the concentration and temperature dependence of the reorientation dynamics of water molecules in binary mixtures of water and 2,6-lutidine below the lower solution critical temperature (LSCT) with femtosecond mid-infrared pump-probe spectroscopy. The measurements reveal the presence of water molecules interacting with both the hydrophobic groups of lutidine and forming a hydrogen bond with the nitrogen atom of lutidine. Both types of molecules show a strongly decreased rotational mobility in comparison to bulk water. From the temperature dependence of the slow water fraction, we conclude that the lutidine molecules form clusters that decrease in size when the temperature is decreased further below the LSCT.