Beyond Cognitive Load

Background.Cognitive effort, defined as the relationship between cognitive load and task performance, offers insight into how individuals efficiently allocate mental resources during cognitively demanding activities.This metric is crucial in high-stakes public health and clinical training, where unmanaged cognitive overload has been linked to medical errors and workforce burnout.This study aims to examine whether cognitive effort varies systematically across task segments and whether it can be estimated at the individual level using brain signal data and machine learning.Method.Functional near-infrared spectroscopy (fNIRS) data were collected from 16 participants during a structured digital cognitive task comprising four sequential segments separated by short and long rest intervals.Cognitive effort was defined through relative neural efficiency and relative neural involvement, which combined measures of prefrontal hemodynamic activity with task performance.The analysis followed a two-stage approach.First, a segment-level group analysis assessed whether cognitive effort differed significantly across predefined task segments, thereby confirming that the task structure produced meaningful changes in cognitive demand.Second, participant-independent machine learning models predicted task performance from brain signal features.These predicted performance scores were then combined with neural measures to estimate cognitive effort at the individual level.Results.First, statistical analysis showed significant differences in cognitive effort across the four task segments.This confirms that even small changes in the assessment structure impact on the collective cognitive efficiency of the trainees.Then, we used machine learning on fNIRS data to predict individual performance scores.We found that the effort calculated from these predicted scores was nearly identical to that calculated from the actual scores.This result suggests that our effort metric is strongly based on brain signals.Conclusion.The findings demonstrate the feasibility of estimating cognitive effort from brain signals using artificial intelligence at both group and individual levels.Public Health Implications.Estimation of cognitive effort from digital task data may support scalable monitoring of cognitive workload and mental fatigue in technology-mediated environments, which complements subjective assessment methods used in public and digital health research.Doi: 10.32481/djph.2026.03.08 digital tasks, yet they provide an incomplete and sometimes inconsistent view of internal cognitive state.Two individuals can reach the same score with very different mental effort, and low performance can reflect overload, lapses in attention, or strategy differences rather than disengagement, 4,5 which are critical risk factors in clinical practice.Cognitive Load Theory explains performance constraints through limited working memory capacity and distinguishes between productive and unproductive load. [6][7]7][8] However, load estimates based on behavior or self-report alone cannot reliably capture real-time effort investment during task execution. [9][10]0][11] Task performance and neural activity, when considered independently, provide an incomplete and sometimes ambiguous characterization of an individual's cognitive state during task execution.To address this limitation, cognitive effort can be operationalized by combining neural measures of cognitive load with task performance.Getchell and Shewokis proposed Relative Neural Efficiency (RNE) and Relative Neural Involvement (RNI) to represent cognitive effort to distinguish efficient engagement, overload, disengagement, and deep involvement. 5,12,13RNE reflects the efficiency with which an individual achieves task performance relative to cognitive load, whereas RNI reflects the degree of engagement and involvement during task execution. 3,14Together, these two metrics provide a comprehensive view of efficiency and involvement, which offers actionable indicators for optimizing learning strategies and improving cognitive performance.