Bibliographic record
Abstract
Humans and other animals are surprisingly adept at estimating the duration of temporal intervals, even without the use of watches and clocks. This ability is typically studied in the lab by asking observers to indicate their estimate of the time between two external sensory events. The results of such studies confirm that humans can accurately estimate durations on a variety of time scales. Although many brain areas are thought to contribute to the representation of elapsed time, recent neurophysiological studies have linked the parietal cortex in particular to the perception of sub-second time intervals. In this Primer, we describe previous work on parietal cortex and time perception, and we highlight the findings of a study published in this issue of PLOS Biology, in which Schneider and Ghose characterize single-neuron responses during performance of a novel "Temporal Production" task. During temporal production, the observer must track the passage of time without anticipating any external sensory event, and it appears that the parietal cortex may use a unique strategy to support this type of measurement.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".