Yisak Debele

Yisak Debele, Israel Goytom, Anwar Misbah

Artificial intelligence systems that model and support human cognition require reliable measures of cognitive state. We present the Focus Performance Score (FPS) from the Pulse Focus mobile Stroop application and evaluate whether it measures attentional control during color-word conflict resolution. We conduct behavioral, neural, and formula validation analyses. Behavioral results (N=466, 111,133 trials) show that FPS captures the Stroop interference effect, tracks individual differences in attentional control, and demonstrates strong test-retest reliability. Neural validation using the DMCC55B fMRI dataset (N=55) shows that the primary FPS component, mean incongruent reaction time, is significantly associated with anterior cingulate cortex activation, a key neural substrate of conflict monitoring. Formula validation identifies and resolves structural redundancy within the scoring framework and provides convergent support for the weighting design. Together, these findings establish FPS as a behaviorally valid, reliable, and neurally grounded measure of attentional control. FPS provides a defensible behavioral signal for evaluating human attentional state and supports future work on attention-aware human-AI interaction and physiological state modeling.

Yisak Debele, Henok Ademtew, Israel Goytom

Human cognitive performance is constrained by limited mental resources, yet continuous computational estimation of cognitive capacity dynamics remains an open challenge. We propose a theory-driven multimodal learning framework that models capacity-related cognitive state as a two-dimensional physiological representation defined by voluntary resource allocation (mental effort) and overload-related strain (stress). The proposed architecture combines dual-stream encoding of cardiac (IBI/HRV) and electrodermal (EDA) signals with late fusion and task-specific output heads that independently estimate probabilistic effort and stress states. Evaluation on the SWELL-KW dataset using strict leave-one-subject-out cross-validation demonstrates cross-individual generalization (stress: 70.0\% balanced accuracy; effort: 72.2\%), with significant gains from multimodal integration and theory-guided supervision. Rather than collapsing physiological dynamics into a single workload label, the proposed effort--stress state-space enables structured differentiation between distinct cognitive regimes, including productive engagement and overload-related strain. Predicted state trajectories exhibit significant demand-sensitive shifts under controlled workload manipulations, with effort and stress responding differentially across interruption and time-pressure conditions. These results suggest that physiologically grounded multidimensional state representations may provide a foundation for adaptive systems capable of continuous capacity-aware monitoring and human-centered interaction.