Effects of within-modal congruency, cross-modal congruency and temporal asynchrony on the perception of perceived audio–visual distance
Bibliographic record
Abstract
The factors we use to determine whether information from separate modalities should be assigned to the same source include task demands, the spatial and temporal coincidence of the composite signals, and, whether the signals are congruent with one another. In a series of experiments, we examined how temporal asynchrony and congruency interact in a competitive binding situation. Across a series of experiments, participants assigned a temporally roving auditory stimulus to competing primary or secondary visual anchors (VAV), or, a temporally roving visual stimulus to competing primary or secondary auditory anchors (AVA), based on causality. Congruency was defined in terms of simulated distance both within- and between-modalities (visual: small, auditory: quiet = far; visual: large, auditory: loud = near). Strong temporal effects were revealed, with differences between VAV and AVA conditions reflecting natural auditory lag tolerance for binding. During VAV conditions, binding was influenced only by visual congruency. During AVA conditions, binding was influenced by audio–visual congruency. These differences did not seem to be due to the relative discriminability between visual and auditory magnitude. The data reiterate the dominance of audition in the time domain (showing stronger temporal effects), the dominance of vision in the spatial domain (showing stronger congruency effects), and, the assistance of domain-inappropriate modalities by domain-appropriate modalities. A special case of congruency in terms of visual looming will also be discussed, along with the potential alerting properties of high magnitude stimuli.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".