The effect of spatial–temporal audiovisual disparities on saccades in a complex scene
Bibliographic record
Abstract
In a previous study we quantified the effect of multisensory integration on the latency and accuracy of saccadic eye movements toward spatially aligned audiovisual (AV) stimuli within a rich AV-background (Corneil et al. in J Neurophysiol 88:438-454, 2002). In those experiments both stimulus modalities belonged to the same object, and subjects were instructed to foveate that source, irrespective of modality. Under natural conditions, however, subjects have no prior knowledge as to whether visual and auditory events originated from the same, or from different objects in space and time. In the present experiments we included these possibilities by introducing various spatial and temporal disparities between the visual and auditory events within the AV-background. Subjects had to orient fast and accurately to the visual target, thereby ignoring the auditory distractor. We show that this task belies a dichotomy, as it was quite difficult to produce fast responses (<250 ms) that were not aurally driven. Subjects therefore made many erroneous saccades. Interestingly, for the spatially aligned events the inability to ignore auditory stimuli produced shorter reaction times, but also more accurate responses than for the unisensory target conditions. These findings, which demonstrate effective multisensory integration, are similar to the previous study, and the same multisensory integration rules are applied (Corneil et al. in J Neurophysiol 88:438-454, 2002). In contrast, with increasing spatial disparity, integration gradually broke down, as the subjects' responses became bistable: saccades were directed either to the auditory (fast responses), or to the visual stimulus (late responses). Interestingly, also in this case responses were faster and more accurate than to the respective unisensory 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".