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Beyond Cognitive Load: AI-Based Estimation of Cognitive Effort Using Brain Signals During Digital Tasks.

Shayla Sharmin¹, Mohammad Fahim Abrar¹, Gael Lucero-Palacios¹

¹Computer and Information Sciences, University of Delaware.

Delaware Journal of Public Health

|May 21, 2026

Summary

Researchers can now estimate cognitive effort using brain signals and machine learning. This method accurately predicts individual performance, offering insights into cognitive workload and mental fatigue in digital environments.

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Area of Science:

Neuroscience
Cognitive Science
Artificial Intelligence

Background:

Cognitive effort, the balance between cognitive load and task performance, is vital for understanding mental resource allocation.
Unmanaged cognitive overload in public health and clinical training can lead to medical errors and burnout.

Purpose of the Study:

To investigate if cognitive effort varies across different task segments.
To determine if cognitive effort can be estimated at an individual level using brain signal data and machine learning.

Main Methods:

Functional near-infrared spectroscopy (fNIRS) data were collected from 16 participants during a digital cognitive task.
Cognitive effort was defined using relative neural efficiency and involvement, combining prefrontal hemodynamic activity with task performance.

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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

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Published on: August 9, 2016

A two-stage analysis involved group-level assessment of cognitive effort across segments and participant-independent machine learning to predict performance.

Main Results:

Significant differences in cognitive effort were observed across the four task segments, indicating task structure impacts cognitive efficiency.
Machine learning models using fNIRS data accurately predicted individual task performance.
Cognitive effort estimated from predicted performance scores closely matched that from actual scores, validating the brain signal basis of the metric.

Conclusions:

The study demonstrates the feasibility of estimating cognitive effort from brain signals using artificial intelligence at both group and individual levels.
This approach offers a scalable method for monitoring cognitive workload and mental fatigue in technology-mediated settings.