The role of biological motion in audio-visual integration
Bibliographic record
Abstract
In multi-sensory integration, vision generally has little influence on auditory duration judgments (Walker & Scott, 1981); provided sufficient quality of the auditory signal (Alais & Burr, 2004; Wada et al., 2003). However much of this research uses visual stimuli that are either static (Soto-Faraco, Spence, & Kingstone, 2004) or exhibit apparent motion (Getzmann, 2007). Here we explore the role of continuous motion in explaining surprising previous findings that visible striking gestures can in fact influence the perception of tone duration (Schutz & Kubovy, 2009); an influence at odds with ‘optimal integration’ as it cannot be explained by auditory ambiguity (Schutz, 2009). Our findings suggest the illusion stems in part from differences in perceiving stimuli exhibiting biological motion vs. non-motion (i.e. unmoving dots). Our stimuli for the three experiments included two classes of dots: dynamic—based on long and short gestures used by a musician to strike a percussion instrument (used previously; Schutz, 2009) and static—based on single dots turning on for a ‘long’ or ‘short’ period of time. We asked participants to judge the durations of several sounds while ignoring concurrent visual stimuli. Overall, we found auditory duration ratings were strongly affected by visual duration when dots were dynamic, rather than static. However, we also found this effect was dependent upon our blocking structure. The illusion was strongest when participants experienced all of the dynamic visual stimuli before all of the static visual stimuli (Exp 1) or vice-versa (Exp 2); when intermingled the visual influence was minimal (Exp 3). We will discuss these results in light of currently theories of multi-sensory integration generally based heavily on experiments in which visual information is static, rather than dynamic as is experienced in our everyday perceiving. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".