The Structure of Sensory Events and the Accuracy of Time Judgments
Bibliographic record
Abstract
We investigated how does the structure of empty time intervals influence temporal processing. In experiment 1, the intervals to be discriminated were the silent durations marked by two sensory signals, both lasting 10 or 500 ms; these signals were two identical flashes (intramodal: VV), or one visual flash (V) followed by an auditory tone (A) (intermodal: VA). For the range of duration under investigation (standards = 0.2, 0.6, 1, or 1.4 s), the results indicated that both the marker length and sensory mode influenced discrimination, but no interaction between these variables or between one of these variables and standard duration was significant. In experiment 2, we compared, for each of four marker-type conditions (VV, AA, VA, AV; and standard = 1 s), intervals marked by two 10 ms signals with intervals marked by unequal signal length (markers 1 and 2 lasting 10 and 500 ms, or 500 and 10 ms). As in experiment 1, the results revealed significant marker-mode and marker-length effects, but no significant interaction between these variables. Experiment 3 showed that, for the same conditions as in experiment 2, perceived duration is not influenced by marker length and that the variability of interval reproductions does not depend on the perceived duration of intervals. The results are discussed in the light of a single-clock hypothesis: marker-length and marker-mode effects are presented as being non-temporal sources of variability associated mainly with sensory and memory processes.
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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 | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".