The contribution of perceptual factors and training on varying audiovisual integration capacity.
Bibliographic record
Abstract
The suggestion that the capacity of audiovisual integration has an upper limit of 1 was challenged in 4 experiments using perceptual factors and training to enhance the binding of auditory and visual information. Participants were required to note a number of specific visual dot locations that changed in polarity when a critical auditory stimulus was presented, under relatively fast (200-ms stimulus onset asynchrony [SOA]) and slow (700-ms SOA) rates of presentation. In Experiment 1, transient cross-modal congruency between the brightness of polarity change and pitch of the auditory tone was manipulated. In Experiment 2, sustained chunking was enabled on certain trials by connecting varying dot locations with vertices. In Experiment 3, training was employed to determine if capacity would increase through repeated experience with an intermediate presentation rate (450 ms). Estimates of audiovisual integration capacity (K) were larger than 1 during cross-modal congruency at slow presentation rates (Experiment 1), during perceptual chunking at slow and fast presentation rates (Experiment 2), and, during an intermediate presentation rate posttraining (Experiment 3). Finally, Experiment 4 showed a linear increase in K using SOAs ranging from 100 to 600 ms, suggestive of quantitative rather than qualitative changes in the mechanisms in audiovisual integration as a function of presentation rate. The data compromise the suggestion that the capacity of audiovisual integration is limited to 1 and suggest that the ability to bind sounds to sights is contingent on individual and environmental factors. (PsycINFO Database Record
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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 | 0.000 | 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".