The current work examines the effect of trial-by-trial feedback about correct and error responding on performance in two basic cognitive tasks: a classic Stroop task (n = 40) and a color-word matching task (n = 30). Standard measures of both RT and accuracy were examined in addition to measures obtained from fitting the ex-Gaussian distributional model to the correct RTs. For both tasks, RTs were faster in blocks of trials with feedback than in blocks without feedback, but this difference was not significant. On the other hand, with respect to the distributional analyses, providing feedback served to significantly reduce the size of the tails of the RT distributions. Such results suggest that, for conditions in which accuracy is fairly high, the effect of corrective feedback might either be to reduce the tendency to double-check before responding or to decrease the amount of attentional lapsing.

Competition elicits different psychological and cardiovascular responses depending on a person’s skills. Decision-making has been considered a distal factor that influences competition, but there are no studies analyzing this relationship. Our objective was to analyze whether decision-making affects the response to competition. Specifically, we aimed to test whether good performers on a decision-making test, the Iowa Gambling Task (IGT), showed an adaptive cardiovascular response to competition. In all, 116 participants (44 women) performed the IGT and were classified into Good or Poor decision-makers. Subsequently, they were exposed to a stress task in two different conditions: a face-to-face competition (winners/losers) or a control condition, while an electrocardiogram was recorded. In the competition group, good decision-makers increased their high-frequency respect to the total heart rate variability (HF/HRV) levels during the task, compared to Poor decision-makers. Again, competition group good decision-makers, showed lower LF and higher HF/HRV reactivity than the control group, which represents lower HRV stress pattern. Moreover, in the group of losers, good decision-makers had a decline in low frequency (LF) during the task and faster recovery than poor decision-makers. In conclusion, good decision-makers have a more adaptive stress response and higher levels of mental effort, based on total HRV interpretation. Decision-making skills could be a factor in a more adaptive cardiovascular response to competition.

Background: New technologies are changing access to medical records and the relationship between physicians and patients. Professionals can now use e-mental health tools to provide prompt and personalized responses to patients with mental illness. However, there is a lack of knowledge about the digital phenotypes of patients who use e-mental health apps. Objective: This study aimed to reveal the profiles of users of a mental health app through machine learning techniques. Methods: We applied a nonparametric model, the Sparse Poisson Factorization Model, to discover latent features in the response patterns of 2254 psychiatric outpatients to a short self-assessment on general health. The assessment was completed through a mental health app after the first login. Results: The results showed the following four different profiles of patients: (1) all patients had feelings of worthlessness, aggressiveness, and suicidal ideas; (2) one in four reported low energy and difficulties to cope with problems; (3) less than a quarter described depressive symptoms with extremely high scores in suicidal thoughts and aggressiveness; and (4) a small number, possibly with the most severe conditions, reported a combination of all these features. Conclusions: User profiles did not overlap with clinician-made diagnoses. Since each profile seems to be associated with a different level of severity, the profiles could be useful for the prediction of behavioral risks among users of e-mental health apps.