Different Effects of Attentional Mechanisms between Visual and Auditory Cueing
Bibliographic record
Abstract
Audio-visual integration interacts with attentional mechanisms. Additionally, salient auditory stimuli automatically draw attention to an audio-visual event, while spatial attention can modulate audio-visual integration. Attention induced by auditory inputs (sound-driven attention) facilitates visual perception. Similarly, visual attention improves performance on a visual task. However, the difference between attention driven by auditory and visual cues is not clear. When visual attention facilitates visual perception, there is a trade-off between spatial and temporal resolution. In contrast, audition has superior temporal resolution to vision. In the present study, we investigated the difference between auditory and visual cue-driven attention with respect to this trade-off. The results indicated that visual cueing increased spatial resolution but decreased temporal resolution. On the other hand, auditory cueing affected the efficiency of visual processing (i.e., response time) for temporal gap detection. These findings suggest that auditory cueing capitalizes on resources available for visual processing. In contrast, visual cueing may increase activation of the spatial channel instead of inhibiting the temporal channel, as proposed in previous study. Overall, there appear to be clear differences between mechanisms involved in auditory and visual cues-driven attention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".