Background/Objectives: The early detection of individuals at risk of cognitive impairment is a clinical imperative. With the recent advancement of digital devices, smartphone application-based cognitive assessment is considered a promising tool for cognitive screening and monitoring inside and outside the clinic. This study examined whether a smartphone-based cognitive assessment, Brain OK, was valid for evaluating cognitive performance and identifying people at risk of cognitive impairment. Methods: We recruited 88 study participants aged over 60. They completed two cognitive tests with the Montreal Cognitive Assessment (MoCA), a validated paper-and-pencil cognitive screening tool, and Brain OK, a smartphone-based cognitive testing application. To examine convergent validity, we conducted analyses of Spearman correlations between MoCA and BrainOK, a Bland–Atman plot with regression analysis, and the area under the curve (AUC). Results: There was a significant positive association between Brain OK and the MoCA total score, with a coefficient of 0.9044 (SE = 0.057, t = 15.750, p < 0.001). The Bland–Altman plot represented a reasonable level of agreement between the two tests. We conducted the AUC analysis of Brain OK to compare the cognitively normal and impaired groups. The AUC value for the Brain OK score of 13.5 was the highest at 0.941. The sensitivity and specificity were 0.958 and 0.925, respectively. Conclusions: The smartphone app-based Brain OK test was feasible for assessing cognitive function and acceptable for identifying subjects with cognitive impairment. The results suggest Brain OK complements traditional in-person cognitive assessments and may help enhance cognitive health dialogue between doctors and patients.

Recognition of sadness from facial expressions is associated with empathic responses. In this study, we devised an emotional probabilistic reward task (PRT) to facilitate sadness recognition and tested its effects on attentional and empathic responses to others in distress. During the emotional PRT, healthy participants were asked to discriminate between facial expressions subtly expressing sadness or anger. Reward feedback for correct sadness and anger recognition was provided, with different probabilities between the training (70% vs. 30%) and control groups (50% vs. 50%). Subsequently, participants performed a visual dot-probe task involving facial expressions of sadness, anger, fear, and happiness. They also completed an empathy rating task while viewing short video clips depicting people experiencing distressing or neutral events. The results showed that the training group developed greater recognition bias for sadness than the control group. Within the training group, sad recognition bias was positively associated with subsequent attentional orienting to sad faces and empathic concern towards distressed others. These findings suggest that the emotional PRT holds promise for modifying cognitive and emotional processes that are associated with empathy for others.

Background/Objectives: Population aging requires scalable approaches for early identification of cognitive decline, particularly mild cognitive impairment (MCI). Although the full 11-task BrainOK smartphone assessment showed excellent discrimination (AUC = 0.941), its administration time constrains large-scale use. To develop and validate a brief Short-Form BrainOK (SF-BrainOK) that preserves diagnostic performance while substantially reducing testing time. Methods: We enrolled 168 community-dwelling older adults (≥60 years). MCI was defined using the Montreal Cognitive Assessment (MoCA; score ≤ 23) as the reference standard. Candidate tasks were selected from the original BrainOK via LASSO-based preselection. To maximize data utilization given the limited sample size, model performance was evaluated using Leave-One-Out Cross-Validation (LOOCV). The cut-off value was determined by maximizing Youden’s J. Results: The final two-task model combined executive function task (Rule-based Drumming I) and memory task (Password Memory I). On the independent test set, discrimination was robust (AUC = 0.783), with sensitivity = 0.75 (95% CI: 0.63–0.85, specificity = 0.71 (95% CI: 0.62–0.80, and accuracy = 0.765 (95% CI: 0.65–0.79) at the optimal cutoff. Conclusions: SF-BrainOK provides a brief, two-task digital screen that markedly reduces administration time while maintaining effective diagnostic performance. By targeting executive function and memory—domains repeatedly shown to be sensitive to early MCI-related change—SF-BrainOK supports scalable, opportunistic screening and the timely identification of at-risk individuals in resource-constrained settings.