Measuring cognitive load in multitasking using mobile fNIRS
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it