A unified neurocomputational model of prospective and retrospective timing.
Bibliographic record
Abstract
Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.e., in passing) is qualitatively different from estimating time in retrospect (i.e., after the event is over). Indeed, computational models that attempt to explain both prospective and retrospective timing assume a fundamental separation of their underlying processes. We, in contrast, propose a new neurocomputational model of timing, the unified temporal coding (UTC) model that unifies prospective and retrospective timing through common principles. The UTC model assumes that both stimulus and timing information are represented inside the same rolling window of input history. As a consequence, the UTC model explains a wide range of phenomena typically covered by specialized models, such as conformity to and violations of the scalar property, one-shot learning of intervals, neural responses underlying timing, timing behavior under normal and distracting conditions, common capacity limits in timing and working memory, and how timing depends on attention. Strikingly, by assuming that prospective and retrospective timing rely on the same principles and are implemented in the same neural network, a simple attentional gain mechanism can resolve the apparently paradoxical effect of cognitive load on prospective and retrospective timing. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".