Movement path as an ethological lens into interval timing
Bibliographic record
Abstract
Abstract Interval timing behavior is traditionally investigated in operant chambers based on the very focal responses of the subjects (e.g., head entry to the magazine, lever press). These measures are blind to the movement trajectory of the animals and capture only a tiny segment and some-times an idiosyncratic component of the animal’s behavior. In other words, the state of the temporal expectancy is not observable at every time point in the trial. On the other hand, in nature, temporal expectancies guide actions in a much more complex fashion. For instance, an animal might approach a food patch at different degrees as a function of the expected time of food availability (e.g., nectar collection behavior). The current study aimed to investigate interval timing in a more naturalistic fashion by analyzing the movement trajectory of rats in fixed time ( FT-30s ) vs. variable time ( VT-30s ) schedules in modified open field equipment. We observed a temporally patterned movement in FT but not in a VT schedule. In the FT schedule, rats moved away from the reward grid after consuming the presented water and were farthest from it for around 15 seconds, after which they started to approach the reward grid again. There was no such temporal patterning of movement trajectory in the VT schedule. Temporal control in the FT schedule was stronger in the Wall compared to the Center condition. Our results show that movement trajectory may reflect instantaneous temporal expectancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".