Institute of Behavioral Sciences

Decision making occurs in dynamic contexts in which the individual and reward attributes change, requiring that predicted reward values of options be updated continually and applied to future choices to maximize reward. Value processing relies on corticostriatal dopamine, which is dysregulated in people with cocaine use disorder (pwCocUD), but whether these individuals differ in value-based decision making has only recently been considered. A probabilistic concurrent monetary choice task with reversals, reinforcement learning modeling, and functional magnetic resonance imaging were used to assess value-based decision making in nontreatment-seeking pwCocUD and matched controls (n = 17 [8F and 9M] per group; n = 34 total). Both groups were sensitive to reinforcement probabilities, but pwCocUD made significantly fewer choices for the high probability reward option. Reinforcement learning modeling revealed that pwCocUD had lower β parameter estimates, indicating reduced consistency in using relative option values to make choices. Significant correlations between the application of value (i.e., β parameter) and value-modulated activity during choice deliberation were identified in brain regions previously linked to reinforcement learning and exploiting versus exploring choice options in both groups. A significant group difference in the strength of this relationship was found in medial frontal control regions, including the dorsal anterior cingulate cortex, which has been associated with the explore-exploit trade-off, suggesting that the differential engagement of these areas contributed to group differences in choice behavior. This research contributes to our understanding of suboptimal decisions made by pwCocUD in uncertain, low-reward contexts and support targeting networks involved in value-based decision making for neuromodulation intervention development. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

ABSTRACT Background While numerous models propose principles for designing digital learning environments (DLEs), most remain fragmented, unifaceted, or insufficiently grounded in pedagogical theory and empirical evidence. This fragmentation has left a clear gap—a lack of an integrated, empirically based framework that unites diverse pedagogical insights into a coherent model for effective DLE design. Objectives To synthesize the findings from previous studies and offer evidence‐based pedagogical recommendations for any future course of action, this study presents a systematic review of pedagogical frameworks, models, and sets of principles for designing DLEs, culminating in the proposal of a comprehensive model. Methods The review spanned from 2010 to 2024, employing the PRISMA method to distil key insights from 42 selected articles; the quantitative phase classified the records in terms of participants, methodology, validation, scope, and comprehensiveness. The qualitative phase employed thematic analysis to explore the included studies in the first phase and discover the needed components to propose a comprehensive model. Results and Conclusions The quantitative results highlight a focus on higher education, methodological diversity, varied levels of coverage and comprehensiveness, and a significant proportion not employing robust validation methods. The qualitative synthesis resulted in the SAFE Model—comprising Situated Engagement, Aligned Curriculum, Facilitated Learning, and Empowered Learners—which integrates theoretical and empirical insights into a unified pedagogical model. The model advances theory by reconciling key theoretical tensions and supports practice by guiding educators, instructional designers, and policymakers in creating coherent, evidence‐based digital learning strategies.

This study examines associations between patient demographics, clinical status, and linguistic features of text messages with engagement in a message-based intervention for serious mental illness. Data from a randomized controlled trial of a message-based mental health intervention were analyzed. Engagement was operationalized as total texts sent per day and total number of disengaged days. Linguistic Inquiry and Word Count identified expressions of affect, social processes, thinking styles, health, and time orientation. Generalized estimating equations assessed associations between demographic, clinical, and Linguistic Inquiry and Word Count variables with engagement across three different time intervals. Among 39 participants, most were male (n = 23, 59%), with diagnoses of schizophrenia (n = 16, 41%), schizoaffective disorder (n = 9, 23%), bipolar disorder (n = 9, 23%), and major depressive disorder (n = 5, 13%). Participants sent approximately two messages per day, with 48% of days disengaged. Race, education, and diagnosis were associated with engagement. Black participants and those with at least some college education sent more texts while individuals with schizophrenia had more disengaged days. Messages containing language about anxiety, friendship, cognitive processes, and common verbs were associated with engagement. Significant relationships between message content and future engagement were observed, particularly in the first 2 weeks, as well as in messages sent the day and week before a disengaged day. Demographic, clinical, and linguistic features are related to engagement in message-based interventions for serious mental illness. Identifying these characteristics can help tailor interventions, enhancing engagement, and reducing dropout rates in digital mental health interventions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Introduction: The interplay between coronary disease and neurocognitive dysfunction remains unclear with several underlying factors likely contributing to this complex relationship. This study develops a predictive model using a machine learning approach to determine a predictive model of neurocognitive functioning in patients with acute coronary syndrome (ACS).Methods: Sixty-three patients, enrolled in the phase III cardiac rehabilitation program, underwent a neurocognitive assessment. To predict neurocognitive functioning a cross validated random forest model was used (RF_cv) due to its robustness to non-linear relationships and overfitting, and its successful application in prior disease prediction studies.Results: The RF_cv model showed an r-squared of 0.978, an RMSE of 0.6309 and a MAE value of 0.479. The top-ten predictors in the model were: HDL, Depression, Glucose, Glycated Hemoglobin, B-Type Natriuretic Peptide, BMI (Kg/m2), Waist-to-Hip Ratio, Cholesterol, Anxiety and Age.Conclusion: The variance in neurocognitive functioning is explained by a combination of biochemical indicators and body composition, reflecting classical cardiovascular risk factors and depression. The obtained RF-cv predictive model supports early identification of patients for tailored interventions.

The potential role of autoantibodies in the etiology of schizophrenia is a key research focus because growing evidence suggests an association between immune dysfunction and psychotic disorders. This hypothesis is supported by findings indicating immune‐related abnormalities in patients with schizophrenia, including chronic inflammation and genetic alterations associated with immune system dysregulation. Epidemiological studies have reinforced this perspective by demonstrating a significant correlation between autoimmune diseases and schizophrenia, suggesting shared pathological mechanisms including autoantibodies. A particularly compelling line of evidence comes from the identification of autoantibodies targeting synaptic molecules in patients with schizophrenia. Autoantibodies against N‐methyl‐D‐aspartate (NMDA) receptors, gamma‐aminobutyric acid (GABA) receptors, neural cell adhesion molecule 1 (NCAM1), and neurexin 1 (NRXN1) have been detected in patients, raising the possibility that immune‐mediated synaptic dysfunction contributes to the disorder's pathophysiology. Experimental studies support this notion because the administration of these autoantibodies in mice induces molecular, cellular, and behavioral abnormalities that mirror aspects of schizophrenia. This review summarizes the relationships among schizophrenia, inflammation, immune‐related genetic factors, autoimmune diseases, and autoantibodies. Furthermore, this review discusses future research directions for further elucidating the role of autoantibodies in schizophrenia.

Itch expectancies play a key role in itch perception and may elicit avoidance behaviors to prevent itch, even when it is costly. Despite theoretical evidence that expectancies can influence avoidance behaviors, no studies have empirically investigated their association in the context of itch. The aim of this study was to investigate whether negative expectancy manipulation led to more costly itch-related avoidance behavior. This study was conducted using a within-subjects repeated measures experimental design. Thirty-four participants underwent an instructional learning and conditioning procedure in which a sham experimental solution paired with a "high" quantity of cowhage spicules was used to induce high itch-expectations. A control solution paired with a medium quantity of cowhage spicules was used to induce medium itch-expectations. Subsequently, participants learned that by effortfully gripping a dynamometer above a certain level, they could avoid strong itching. In anticipation of two other itch stimuli after reapplication of the experimental solution and the control solution, average grip strength (reflecting costly itch-avoidance behavior) was measured. Results indicated that negative itch expectations were successfully induced (p < 0.001, d = 1.16). However, while participants engaged in avoidance behavior in both experimental and control trials, negative expectancy learning did not lead to more costly avoidance behavior (p = 0.74, ηp2 = 0.003). Results suggested that acute itch induced avoidance behavior regardless of expectations toward itch. Extending the research on the role of avoidance and its impact on itch may shed light on new approaches for itch management.