Exploiting the Auditory Modality in Decision Support
Bibliographic record
Abstract
The rate at which technology continues to develop and permeate our lives is such that it has become increasingly easier, and thus more likely, for information to be presented to us via different modalities simultaneously. But to what extent does this confluence of information affect our subsequent judgment and performance? Furthermore, what are the implications for system design when this information is critical to saving our lives and others? This study uses a visual ‘microworld’ simulation of a naval anti-air warfare to investigate whether the content and priority of audio messages that accompany changes in the visual modality assist or hinder performance in the task (identification of change and evaluation of threats). Results indicate that although helping critical change detection, a critical warning in the auditory modality is not as efficient as its visual counterpart. Moreover, audio messages tended to bias threat evaluation towards perceiving objects as more hostile than they were in reality. Such findings have clear implications in regard to the costs and benefits of further exploiting the auditory modality in dynamic visual environments.
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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 | 0.001 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".