Studying the Processing of Multimodal Brief Temporal Intervals with an Equisection (Bisection) Task
Bibliographic record
Abstract
Several studies have investigated the influence of auditory and visual sensory modalities on the variability and perceived duration of brief time intervals. However, few studies have investigated this influence when the two intervals to be discriminated share the same stimulus, and none of these have included the tactile modality. The aim of the present study was to investigate, in multimodal conditions, the capability to discriminate two adjacent intervals, using an equisection and adjustment method. Participants had to adjust the second of three brief successive signals marking two empty intervals until they were subjectively perceived as equal. The experiment included nine modality conditions and intervals between Markers 1 and 3 lasted 0.5, 1, 1.5, or 2 s (four standard conditions). The results show that the adjustment is better (lower variability) with three auditory (A) than with three visual (V) or tactile (T) markers, and these three conditions are better than when Marker 2 differs from Markers 1 and 3 (all intermodal conditions). Differences also emerged in the perceived duration of intermodal conditions. In TVT and VTV conditions, intervals marked by a tactile-visual (TV) sequence are perceived as longer than VT intervals, and in AVA and VAV conditions AV intervals are perceived as longer than VA intervals. Finally, AT intervals are perceived as longer than TA intervals, but only in the short standard conditions. In addition to replicating the classical variability increase when short intermodal intervals are used, the study shows the influence on perceived duration of the speed of processing of a visual signal.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".