Cognitive Load and Situation Awareness for Soldiers: Effects of Message Presentation Rate and Sensory Modality
Bibliographic record
Abstract
OBJECTIVE: We sought to determine the influence of message presentation rate (MPR) and sensory modality on soldier cognitive load. BACKGROUND: Soldiers commonly communicate tactical information by radio. The Canadian Army is equipping soldiers with a battle management system (BMS), which also allows them to communicate by text. METHOD: We varied presentation modality (auditory vs. visual) and MPR (fast or slow) in an experiment involving a tactical scenario. Participants (soldiers) received messages and periodically provided situation reports to higher level command, and the scored reports were used to provide a measure of situation awareness (SA). The detection response task (DRT) and NASA-TLX were used to measure cognitive load. RESULTS: The fast MPR reduced DRT accuracy and increased response times relative to slow MPR. The NASA-TLX results also showed higher subjective workload ratings for several subscales with fast MPR. Messages presented visually produced greater cognitive load, with slower DRT response times for the visual than the auditory condition. SA scores were higher with slower MPR and auditory presentation. There was no statistical interaction of presentation modality and rate for any measure. CONCLUSION: Fast MPR and visual presentation increased cognitive load and degraded SA. APPLICATION: These findings show that the DRT can be used to measure workload effectively in a tactical military context and that the method of information presentation affects how soldiers process information in a BMS.
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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 | 0.002 | 0.015 |
| 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) | 0.005 | 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".