Spatiotemporal task parameters modulate multisensory response enhancement in saccadic latency
Bibliographic record
Abstract
Saccadic latencies are typically faster in response to multisensory as compared to unisensory stimuli. However, studies using different saccadic protocols result in varying degrees of multisensory response enhancement. This study investigates which spatial and temporal task parameter configuration yields the greatest multisensory response enhancement for saccadic latency. Human observers (n=9) made saccades to either visual targets (small black dot presented 10 degrees to the left or right from the screen center), auditory targets (burst of white noise played to the left or right ear through headphones), or combined, congruent audiovisual targets. We measured saccadic latency with the Eyelink1000 eye-tracker as indicators of sensory processing speed and to determine the efficacy of multisensory integration across three task configurations: (1) gap & placeholders, (2) no-gap & no-placeholders, and (3) no-gap & placeholders. As expected, a gap reduced saccadic latency to visual targets, whereas placeholders sped up saccades to auditory targets. Across all task configurations, we observed faster latencies to audiovisual targets compared to visual or auditory targets, indicating multisensory response enhancement (all p<0.001). To assess whether this enhancement was explained by statistical facilitation of the redundant audiovisual signal, we compared observers’ audiovisual latencies to the upper bound of a race model based on the unisensory target conditions. Task configurations strongly modulated the degree to which audiovisual latencies outperformed the race model (F(2,16)=6.42, p<0.01) and showed that only the no-gap & placeholder-condition resulted in reliable and strong multisensory integration. Our findings demonstrate that the spatiotemporal parameters of a simple saccade task modulate the magnitude of multisensory enhancement on saccadic latency. These findings provide a reliable foundation to allow exploration of the underlying mechanisms of multisensory perception and orienting behavior.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".