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External sources of information influence human actions. However, psychological traits (PTs), considered internal variables, also play a crucial role in decision making. PTs are stable across time and contexts and define the set of behavioral repertoires that individuals express. Here, we explored how multiple metrics of adaptive behavior under uncertainty related to several PTs. Participants solved a reversal-learning task with volatile contingencies, from which we characterized a detailed behavioral profile based on their response sequences. We then tested the relationship between this multimetric behavioral profile and scores obtained from self-report psychological questionnaires. The PT measurements were based on the Hierarchical Taxonomy Of Psychopathology (HiTOP) model. By using multiple linear regression models (MLRMs), we found that the learning curves predicted important differences in the PTs and task response times. We confirmed the significance of these relationships by using random permutations of the predictors of the MLRM. Therefore, the behavioral profile configurations predicted the PTs and served as a "fingerprint" to identify participants with a high certainty level. We discuss briefly how this characterization and approach could contribute to better nosological classifications.

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The evaluation of external and internal stimuli permits the ongoing actualization of choice-related information and, thus, the association between stimuli and outcomes. This process is essential to decision-making as it allows constant adaptation to environmental changes in order to maximize gains and minimize losses. Reversal learning paradigms are used to study this process, which has been associated with prefrontal cortex activity (frontopolar, dorsolateral) in conjunction with posterior areas (parietal, temporal), due to their participation in integrating and processing the stimuli-reward relation. The aim of this study was to assess the cortical functionality associated with reversal learning during the decision-making process. The EEG activity of 22 young men was recorded while performing a decision-making task in a reversal learning condition compared to an initial learning condition. EEG data were analyzed during evaluation of the stimuli, before motor execution (formation of preferences), and during task feedback (outcome evaluation). The formation of preferences stage was characterized by a higher correlation of the alpha2 band between the parietal cortices. In the feedback stage of the reversal learning condition, a higher absolute power of the theta band in the left dorsolateral (F3), and a lower correlation of the alpha1 band between the right frontopolar and dorsolateral (Fp2-F4), as well as between the right frontopolar and temporal (Fp2-T4), were observed. The data obtained show that the EEG activity of the areas recorded changed in the evaluation of the stimuli information in the reversal learning condition during a decision-making task.

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BACKGROUND: Decisions made by individuals with disordered gambling are markedly inflexible. However, whether anomalies in learning from feedback are gambling-specific, or extend beyond gambling contexts, remains an open question. More generally, addictive disorders-including gambling disorder-have been proposed to be facilitated by individual differences in feedback-driven decision-making inflexibility, which has been studied in the lab with the Probabilistic Reversal Learning Task (PRLT). In this task, participants are first asked to learn which of two choice options is more advantageous, on the basis of trial-by-trial feedback, but, once preferences are established, reward contingencies are reversed, so that the advantageous option becomes disadvantageous and vice versa. Inflexibility is revealed by a less effective reacquisition of preferences after reversal, which can be distinguished from more generalized learning deficits. METHODS: In the present study, we compared PRLT performance across two groups of 25 treatment-seeking patients diagnosed with an addictive disorder and who reported gambling problems, and 25 matched controls [18 Males/7 Females in both groups, Mage(SDage) = 25.24 (8.42) and 24.96 (7.90), for patients and controls, respectively]. Beyond testing for differences in the shape of PRLT learning curves across groups, the specific effect of problematic gambling symptoms' severity was also assessed independently of group assignment. In order to surpass previous methodological problems, full acquisition and reacquisition curves were fitted using generalized mixed-effect models. RESULTS: Results showed that (1) controls did not significantly differ from patients in global PRLT performance nor showed specific signs of decision-making inflexibility; and (2) regardless of whether group affiliation was controlled for or not, gambling severity was specifically associated with more inefficient learning in phases with reversed contingencies. CONCLUSION: Decision-making inflexibility, as revealed by difficulty to reacquire decisional preferences based on feedback after contingency reversals, seems to be associated with gambling problems, but not necessarily with a substance-use disorder diagnosis. This result aligns with gambling disorder models in which domain-general compulsivity is linked to vulnerability to develop gambling-specific problems with exposure to gambling opportunities.

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Objective: To evaluate the effect of the application of a flipped learning model for teaching biostatistics to dental students in a Peruvian public university. Methodology: A quasi-experimental, crossover, longitudinal and prospective design was used. A non-probability sampling technique was employed. The sample consisted of 63 students that enrolled in the Biostatistics course at the School of Dentistry at Universidad Nacional Mayor de San Marcos. Students were divided into two groups according to their designated training schedule. The contents of two units were assessed. For the first unit (descriptive statistics), the first group was taught using the flipped learning model and the second group with the master class model. For the second unit (inferential statistics), groups were crossed over. At both periods of the study, cognitive, procedural and attitudinal skills were assessed through previously validated questionnaires. Mann-Whitney U test, Cohen is d and multiple linear regression analysis were performed. Results: the mean total score for the second unit was higher (p<0.001) in the flipped learning group (32.58) compared to the master class guided training group (27.94), presenting a Cohen's d=0.97. Procedural (9.23 versus 7.80) and attitudinal (15.63 versus 12.90) skills were on average higher in the flipped learning group. Regression analysis resulted in R2=0.245, p=0.003. Conclusion: the flipped learning method achieved a higher content learning in the second unit, compared to the master class model.

Objetivo: Evaluar el efecto de la experiencia de aplicación del modelo flipped learning para el aprendizaje de bioestadística en estudiantes de odontología de una universidad pública peruana. Metodología: Se realizó un diseño cuasi experimental de secuencia cruzada, longitudinal, prospectivo. La muestra se obtuvo de manera no probabilística y estuvo conformada por 63 estudiantes que cursaron la asignatura de Bioestadística en la Facultad de Odontología de la Universidad Nacional Mayor de San Marcos. Se conformaron dos grupos de acuerdo al horario de práctica designado y se evaluaron los contenidos de dos unidades de la asignatura. En la primera unidad (estadística descriptiva) el primer grupo fue sometido al modelo flipped learning y el segundo sometido al modelo presencial clase magistral. En la segunda unidad del curso (estadística inferencial) los grupos se cruzaron. En ambos momentos se evaluaron las capacidades cognitivas, procedimentales y actitudinales a través de cuestionarios previamente validados para el estudio. Se aplicó la prueba U de Mann-Whitney, la d de Cohen y la regresión lineal múltiple. Resultados: En la segunda unidad la puntuación media total fue mayor (p<0,001) en el grupo flipped learning (32,58) en comparación al de clase magistral-práctica dirigida (27,94) con un efecto alto d Cohen= 0,97. Las capacidades procedimentales (9,23 versus7,80) y actitudinales (15,63 versus 12,90) fueron en promedios mayores en el grupo flipped learning. Al aplicarse la regresión se halló un R2= 0,245 p=0,003. Conclusión: La aplicación del modelo flipped learning logró un mayor aprendizaje en contenidos de la segunda unidad, en comparación con el modelo presencial clase magistral en los estudiantes.

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