Bibliographic record
Abstract
Our perceptual system continuously processes information from multiple modalities. Some of these derive from common sources – such hearing a speaker’s voice while watching their lip movements. Others arrive independently – for example watching the same speaker’s lips when a passing train masks their voice. Deciding which sights and sounds “go together” is essential in reconstructing an accurate internal representation of our environment. The challenges of this process are illustrated vividly whenever clever ventriloquists trick audiences into “hearing” puppets speak. This “ventriloquist effect” is explored in laboratories through tasks involving localization of disembodied tone beeps and light flashes. Divorced from real world referents, these stimuli provide context-free tools thought to illuminate generalized principals of perceptual processing. A large body of research suggests a pattern of “optimal integration” in which the highest quality information is weighted most strongly, accurately describing both audio-visual (Alais & Burr, 2004) and visual-haptic tasks (Ernst & Banks 2002). However, the degree to which these models generalize beyond artificial stimuli is a matter of some debate. Here I will discuss new findings from my group arguing for a broader understanding of audio-visual integration using naturalistic stimuli. Broadly construed, this approach explores trade-offs between three parameters. The first two, coincidence in space and time, are well understood by virtue of previous work. However the third– event congruity – also plays a crucial albeit overlooked role. Although more difficult to quantify than spatial agreement or temporal alignment, event congruity plays an important role in the audio-visual binding of speech (Vatakis & Spence, 2007) impact events (Vatakis & Spence, 2008), and musical notes (Chuen & Schutz, in press; Schutz & Kubovy, 2009). Together this research raises intriguing questions about the best practices for exploring the perceptual system’s ability to integrate realistic, time-varying stimuli more complex than flashes and beeps.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".