Audiovisual short-term influences and aftereffects in motion: Examination across three sets of directional pairings
Bibliographic record
Abstract
The study of cross-modal influences in perception, particularly between the auditory and visual modalities, has been intensified recently. This paper reports on a comprehensive study of auditory-visual cross-modal influences in motion, including motion aftereffects (MAE). We examined both auditory influences on visual perception and vice versa. Visual motion interactions were examined using three directional pairings or configurations: along the horizontal, vertical, and depth axes. In Experiment 1 we assessed how the simultaneous presence of a strong motion signal in one modality affected the perception of motion in the other modality. To investigate further whether such influences have long-term effects, we tested whether adaptation in one modality alone could produce cross-modal MAEs in Experiment 2. Overall, the pattern of results was similar across all directional pairings, with the strongest cross-modal influences observed in motion along the horizontal axis; this is likely due to the greater co-localization of the two stimuli in this configuration. Although both auditory and visual stimuli affected the other modality when presented simultaneously, significant cross-modally induced aftereffects could only be produced using visual stimuli. However, we did observe vertical visual MAE following adaptation to auditory spectral motion. These results are discussed in terms of current psychophysical and neurophysiological findings concerning the way in which auditory-visual signals are processed.
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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) | 0.001 | 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".