Temporal rate is not a distinct perceptual metric
Bibliographic record
Abstract
Sensory adaptation experiments have revealed the existence of 'rate after-effects' - adapting to a relatively fast rate makes an intermediate test rate feel slow, and adapting to a slow rate makes the same moderate test rate feel fast. The present work aims to deconstruct the concept of rate and clarify how exactly the brain processes a regular sequence of sensory signals. We ask whether rate forms a distinct perceptual metric, or whether it is simply the perceptual aggregate of the intervals between its component signals. Subjects were exposed to auditory or visual temporal rates (a 'slow' rate of 1.5 Hz and a 'fast' rate of 6 Hz), before being tested with single unfilled intervals of varying durations. Results show adapting to a given rate strongly influences the perceived duration of a single empty interval. This effect is robust across both interval reproduction and duration discrimination judgments. These findings challenge our understanding of rate perception. Specifically, they suggest that contrary to some previous assertions, the perception of sequence rate is strongly influenced by the perception of the sequence's component duration intervals.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".