Editorial: Psychological and neurobiological mechanisms of time perception and temporal information processing: insight from novel technical approaches
Bibliographic record
Abstract
Subjective time is an integral part of our experience and cognition.Decades-long research has suggested that animals, including human has a mechanism of the internal clock/stopwatch (Gibbon et al., 1984;Buhusi and Meck, 2005;Merchant et al., 2013;Paton and Buonomano, 2018).The similarities between the psychophysical properties of timing behavior observed in human and non-human animals suggest that the mechanism of timing and time perception is an evolutionarily well-preserved neurobiological mechanism.Based on this premise, researchers have made a leap in understanding the internal clock/stopwatch based on the comparative study of interval timing.However, the knowledge of the psychological and neurobiological mechanisms of timing is still far from complete.This Research Topic focused on recent technical and theoretical developments in the study of interval timing.De Corte et al. has made an excellent conceptual analysis of interval timing models and their implications for neural computing.They classified models of interval timing as ramping vs. population code models and discussed the computational affordances of these theoretical approaches.They showed the importance of understanding the neural mechanisms of temporal scaling and suggested understanding the mechanisms of speed regulation, which enable the model to show temporal scaling as the next research direction.An empirical gap in the study of interval timing, particularly in non-human animals, is that the related behaviors are studied in a fashion that is isolated from the ecologically relevant setting.In this issue, Henke et al. introduced a new behavioral task in virtual reality that required animals to reproduce the target duration walking along a corridor.Importantly, subjects (i.e., gerbils) could not use spatial cues as a proxy for time and exhibited timing performance that highly resembles human data (i.e., scalar variability, regression to
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".