Benefits and Limits of Explicit Counting for Discriminating Temporal Intervals.
Bibliographic record
Abstract
Segmenting information into smaller parts helps to process it, and this is also true for temporal information. The aim of the present article is to compare the benefits of using explicit counting in a temporal discrimination task under various marker-type conditions and to show the limits of this strategy. In Experiment 1, conditions with and without counting were compared for two implicit standard durations, .8 and 1.6 s, in connection with three marker-type conditions, which were intervals marked by: 1) two brief auditory signals (Auditory-Auditory); 2) two brief visual signals (Visual-Visual); and 3) one auditory signal followed by a visual signal (Auditory-Visual). At .8 s, marker-type differences are significant (best in audition, worse with a bimodal sequence), and remain present with an explicit counting strategy. At 1.6 s, explicit counting provides clear improvements of performance in all marker-type conditions and annihilates marker-related differences. Experiment 1 also suggests that standard deviation remains constant from .8 to 1.6 s in the counting condition, while Experiment 2 shows that when standard intervals are extended up to 4 s, explicit counting does not totally prevent variance from increasing as base duration becomes progressively longer. The benefits derived from using explicit counting in duration discrimination are argued to depend (1) on a reduction of variance in the memory process involved in the timing mechanism, and (2) on a change in the decisional process.
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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 | 0.004 | 0.020 |
| 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 | 0.001 | 0.002 |
| 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".