The Perception of Empty and Filled Time Intervals by Pigeons
Bibliographic record
Abstract
Pigeons were trained in a within-subjects design to discriminate durations of a filled interval (2 s and 8 s of light) and durations of an empty interval (2 s and 8 s bound by two 500-ms light markers). Filled intervals required a response to one set of comparisons (e.g., blue vs. yellow), whereas empty intervals required a response to a different set of comparisons (e.g., red vs. green). Psychophysical testing indicated that empty intervals were judged to be longer than equivalent durations of a filled interval. This finding was replicated when the anchor durations used during training were changed to 1 s and 4s, or 4s and 16s. The difference between the point of subjective equality (PSE) for the empty intervals and the PSE for filled intervals increased as the magnitude of the anchor duration pairs increased. In addition, the difference limens (DL) for empty intervals were smaller than those for filled intervals, and they also increased as the magnitude of anchor duration pairs increased. An analysis of the Weber fractions (WF; i.e., DL/PSE) provided evidence for superimposition of the empty and filled timing functions across the different sets of anchor durations. These results suggest that the accumulation of subjective time was greater for empty intervals than for filled intervals. Within the framework of scalar timing theory, this difference in timing appeared to be the result of a clock rate difference rather than a switch latency difference.
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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 | 0.002 |
| 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 | 0.000 |
| 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".