Intelligent Sensory Modality Selection for Electronic Supportive Devices
Bibliographic record
Abstract
Humans operating in stressful environments, such as in military or emergency first-responder roles, are subject to high sensory input loads and must often switch their attention between different modalities. Conventional supportive devices that assist users in such situations typically provide information using a single, static sensory modality; however, this carries the risk of overload when the modalities for the primary task and the supportive device overlap. Effective feedback modality selection is essential in order to avoid such a risk. One potential method for accomplishing this is to intelligently select the supportive device's feedback modality based on the user's environment and given task; however, this may result in delayed or lost information due to the performance cost resulting from switching attention from one modality to another. This paper describes the design and results of a human-participant study designed to evaluate the benefits and risks of various intelligent modality-selection strategies. Our findings suggest complex interactions between strategies, sensory input load levels and feedback modalities, with numerous significant effects across many different performance metrics.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".