Bibliographic record
Abstract
Given that multiple senses are often stimulated at the same time, perceptual awareness is most likely to take place in multisensory situations. However, theories of awareness are based on studies and models established for a single sense (mostly vision). Here, we consider the methodological and theoretical challenges raised by taking a multisensory perspective on perceptual awareness. First, we consider how well tasks designed to study unisensory awareness perform when used in multisensory settings, stressing that studies using binocular rivalry, bistable figure perception, continuous flash suppression, the attentional blink, repetition blindness and backward masking can demonstrate multisensory influences on unisensory awareness, but fall short of tackling multisensory awareness directly. Studies interested in the latter phenomenon rely on a method of subjective contrast and can, at best, delineate conditions under which individuals report experiencing a multisensory object or two unisensory objects. As there is not a perfect match between these conditions and those in which multisensory integration and binding occur, the link between awareness and binding advocated for visual information processing needs to be revised for multisensory cases. These challenges point at the need to question the very idea of multisensory awareness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".