The Perception of Time in Humans, Brains, and Machines
Bibliographic record
Abstract
The experience of time passing is fundamental to human experience. Considerable experimental data has found that perceptual judgments of duration show systematic distortions away from physical ‘clock’ time. The majority of neurocognitive explanations of duration perception invoke some form of ‘inner clock’, or pacemaker. Systematic distortions can then be accounted for by alterations in this pacemaker mechanism. Here, we summarise recent work exploring a different approach, according to which experiences of duration are based on activity within perceptual classification networks. Specifically, we propose that subjective time is constructed from accumulated salient changes within hierarchical perceptual networks and substantiate this proposal by (i) building an artificial neural network based model which is able to predict human subjective time judgements, including a number of its biases; (ii) using model-based neuroimaging to show that human subjective time can be predicted from activity in human perceptual cortex, as suggested by our model, and (iii) locating the model within a larger ‘predictive processing’ framework which enables connections between time perception and episodic memory to be elaborated. Altogether, we provide a new mechanistic framework for understanding human time perception in terms of inference about information arising during perceptual processing.
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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 | 0.000 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".