Visual motion perception is influenced by sound pitch and location
Bibliographic record
Abstract
Prior research has shown that auditory pitch can influence spatial and motion estimates of visual stimuli. Cross-modal correspondence between pitch and space show high-pitched sounds are associated with upper space and low-pitched sounds with lower space (Evans & Treisman, 2010). Ambiguous motion stimuli are perceived as moving upward when paired with ascending pitch sounds and downward with descending pitch sounds (Maeda, Kanai, & Shimojo, 2004). However, the extent to which pitch sounds influence visual motion remains unclear. Also, how manipulating both a sound's pitch and location might influence visual motion perception has not been explored before. Therefore, we investigated how changes in pitch tones affect visual motion direction judgments while varying visual motion saliency and the pitch-spatial location relationship relative to the direction of a visual motion stimulus. Participants were presented with high or low pitch tones with a random dot kinematogram (RDK) stimulus displaying either upward or downward motion. Saliency of visual motion was manipulated by systematically varying the degree of coherent motion. In Experiment 1, the auditory stimuli were presented from two speakers placed left and right of the RDK display. In Experiment 2, the auditory stimuli were presented from either a speaker above or below the RDK display. Subjects had to judge the direction of the coherent motion dots by a 2AFC response. Experiment 1 showed subjects were more likely to judge the direction of visual motion as going up with high-pitched tones and down with low-pitched tones. In Experiment 2, we found an interaction of sound location and pitch (low-pitch tones from the bottom speaker yielded greater judgment bias than low-pitch tones from the top speaker). These findings show the extent to which pitch affects visual motion perception as the strength of the visual signal is varied and how this pitch effect is sensitive to the pitch-space correspondence. Meeting abstract presented at VSS 2018
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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 | 0.004 |
| 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 | 0.001 | 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".