Visual orienting and multisensory perception: The role of cue frequency
Bibliographic record
Abstract
Attention can facilitate multisensory perception. The impact of visual attention on audio-visual perception was investigated using an established cueing paradigm which has revealed facilitatory effects of attention at short (100 ms) stimulus onset asynchronies (SOAs), and inhibitory effects at longer (e.g., 500 ms) SOAs. White disk(s) were presented within a cued (i.e., congruent) or uncued (i.e., incongruent) location, 100 or 500 ms after the cue. The congruent location was considered attended at the 100 ms SOA, but inhibited at the 500 ms SOA. The disk flashed (F) either once or twice. Auditory beeps (B) were presented that were consistent (i.e., 1F1B) or inconsistent with the number of flashes (i.e., 1F2B or 2F1B). Typically, the number of perceived flashes is biased towards the number of beeps (i.e., multisensory perception). If attention facilitates multisensory perception, the beeps should elicit the strongest bias 100 ms after the cue, and a reduced bias at the 500 ms SOA. At the 100 ms SOA, the 1F2B condition yielded a greater number of flashes at the congruent relative to the incongruent location (i.e., increased auditory bias). In the 2F1B condition, more flashes were reported in the congruent relative to the incongruent location (i.e., reduced auditory bias). At the 500 ms SOA, the beep's influence was comparable for congruent and incongruent trials. The results suggested that following attentional orienting, multisensory perception was biased towards stimuli that occurred more often within a trial, regardless of modality. In addition, inhibitory orienting mechanisms did not significantly influence audio-visual perception. influence audio-visual perception.Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC); Canada Foundation for Innovation (CFI); University of Toronto Graduate Student Bursary
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".