Bibliographic record
Abstract
Four duration-discrimination experiments were carried out to compare crossmodal and unimodal timing conditions. For all experiments, participants were presented with two sequences, each consisting of 1 or 4 time intervals (marked by 2 or 5 signals), and asked to indicate whether the interval(s) of the second sequence was (were) shorter or longer than the interval(s) of the first. Markers in the first and second sequences were, respectively, tones and flashes (experiment 1), flashes and tones (experiment 2), both flashes (experiment 3), and both tones (experiment 4). In all modality conditions, except when using only tones (experiment 4), increasing the number of repetitions of the variable interval reduced duration-discrimination thresholds, independently of whether the fixed interval was presented first or second within the sequence pair. Moreover, judgments about sequence timing were best for tones-tones sequence pairs, worst for flashes-flashes sequence pairs, and intermediate for crossmodal (flashes-tones or tones-flashes) sequences. Finally, presenting a fixed interval in the first sequence resulted in better discrimination than presenting a variable interval in the first sequence. Implications for theories of timing are discussed.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".