The Role of Attention in Eliciting a Musically Induced Visual Motion Aftereffect
Bibliographic record
Abstract
Previous studies have reported visual motion aftereffects (MAEs) following prolonged exposure auditory stimuli depicting motion, such as ascending or descending musical scales. The role of attention in modulating these cross-modal MAEs, however, remains unclear. The present study manipulated the level of attention directed to musical scales depicting motion and assessed subsequent changes in MAE strength. In Experiment 1, participants either responded to an occasional secondary auditory stimulus presented concurrently with the musical scales (diverted-attention condition) or focused on the scales (control condition). In Experiment 2, all participants engaged in a continuous secondary auditory task presented concurrently and dichotically with the musical scales. Visual motion perception in both experiments was assessed via random dot kinematograms (RDKs) varying in motion coherence. Results from Experiment 1 replicated prior work, in that extended listening to ascending scales resulted in a greater likelihood of judging RDK motion as descending, in line with the MAE. In contrast, the MAE was eliminated in Experiment 2. Results from Experiment 2 could not be explained by a lack of explicit awareness of the musical scales. These results suggest that attention is necessary in eliciting an auditory-induced visual MAE.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".