Assessing Situation Awareness (SA) Using Single- or Dual-Location functional Near Infrared Spectroscopy (fNIRS)
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Summary
This study addresses the challenge of objectively assessing Situation Awareness (SA) in high-stress operational environments, such as aviation, emergency response, and military operations. Effective performance in these fields requires personnel to maintain SA—specifically Level 3 SA, which involves predicting future events—without experiencing cognitive overload. Traditional assessment methods rely on trainer observation, which is subjective and often fails to distinguish between automatic skill execution and high-effort cognitive processing. To address this, the authors extended their existing cognitive workload assessment software, Sherlock™, to include SA metrics using functional near-infrared spectroscopy (fNIRS), a non-invasive neurophysiological monitoring technique. The researchers conducted experiments with two distinct participant groups: 22 Brown University students (average age 22.2) and 22 U.S. Army personnel (average age 49). Participants performed a validated driving hazard awareness task, viewing first-person driving videos and answering Level 3 SA questions predicting upcoming hazards. The task included three difficulty levels: single events, co-located dual events, and separated dual events. Physiological data were collected using single-channel (fNIRS Pioneer) and dual-channel (fNIRS Explorer) sensors placed on the prefrontal cortex. Data analysis involved normalizing hemoglobin concentration changes and calculating features such as signed/unsigned amplitude, direction, variance, and derivative. Logistic regression models were trained to predict correct versus incorrect SA responses using five-fold cross-validation. Behavioral results confirmed that separated dual events were the most difficult condition, yielding the lowest accuracy (60%) and longest response times (7332 ms). The fNIRS-based predictive models demonstrated significant efficacy. At the group level, the model predicted SA performance with a mean accuracy of 65% for Brown students, 71% for Army personnel, and 65% for the combined dataset. Notably, the model performed better on individual participants when trained specifically on their data (leave-one-out validation), achieving a mean individual prediction accuracy of 69% (range 45–88%). The authors hypothesized that higher accuracy in Army personnel may stem from greater driving experience. The findings indicate that neurophysiological data from single- or dual-location fNIRS sensors can reliably predict individual Level 3 SA performance. This supports the development of objective, real-time tools for monitoring trainee competence during simulations. By identifying when personnel are struggling to maintain SA, trainers can intervene more effectively, potentially improving training efficiency and safety in critical domains. The study validates the extension of physiological monitoring from cognitive workload to SA, paving the way for integration into military and medical training environments.
Key finding
Logistic regression models using fNIRS data successfully predicted individual situation awareness performance in a driving hazard task with a mean accuracy of 69%.
Methodology
lab_experiment
Sample size: 44
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | canonical_url | — | — | 11 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| enrich | failed | — | — | — | 4 | 2026-07-02 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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Information type
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- Empirical Findings: physiological data
- Theoretical Contribution: theory or model