Measuring cognitive load in multitasking using mobile fNIRS
Bibliographic record
Abstract
Cognitive load, or the mental effort required to process and retain information, is a critical factor in high-stakes environments where task demands often exceed working memory capacity, leading to performance declines and errors. However, most cognitive load research has relied on controlled, single-task paradigms, limiting its applicability to real-world multitasking situations. Addressing this gap, we used a mobile, two-channel functional near-infrared spectroscopy (fNIRS) device to measure cognitive load in a complex multitasking environment, simulating real-world cognitive demands. Thirty-one undergraduate participants engaged in single-task and multitask conditions to simulate real-world cognitive demands. Results showed that subjective cognitive load ratings were higher, performance scores were lower, and error rates increased in the multitask condition compared to the single-task condition. However, contrary to expectations, prefrontal cortex activation did not increase in the multitask condition, suggesting a "cognitive disengagement" effect, where the brain limits engagement to manage overload. This finding challenges the traditional one-to-one association between cognitive load and prefrontal activation, as seen in simpler validation studies. Our study highlights the value of mobile fNIRS for assessing cognitive load in ecologically valid settings and provides insights that could inform strategies for optimizing performance in high-stakes environments like aviation and healthcare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".