Sight trumps sound: The relationship between visual and auditory distraction
Bibliographic record
Abstract
Previous research has revealed that the presentation of non-target information in an alternate modality from the target can influence performance on perceptual localization tasks. This influence is enhanced as the potential for information gain from the second stimulus increases. If this enhanced influence can also be observed in goal-directed aiming movements, the influence of a visual distractor on an auditory target should be greater than vice versa because a visual stimulus provides more accurate spatial information than an auditory stimulus. The present study tested this prediction by examining the kinematics of aiming movements towards visual or auditory targets with or without a distracting stimulus in the other modality. When present, the distracting stimulus was delivered simultaneous to the target modality at: a spatially coincident location, to the left, or to the right of the target. The results revealed that movement trajectories and endpoints were biased towards the distracting stimulus only when aiming to auditory target locations (the distractor was visual). No biases were observed when the targets were visual (the distractor was auditory). Thus, the distractor influenced participants' movements only when the secondary modality provided more accurate spatial information than the target modality. The unidirectional nature of this effect supports the notion that the influence of a secondary stimulus depends on the potential for information transmission.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".