Lost in time: rats are unable to return to a start location that varies
Bibliographic record
Abstract
Path integration provides guidance based on cues generated by a point of reference (usually start location) and subsequent self-movement. This well-established mechanism suggests start location may have special significance and might provide a useful window into memory in rats. In an earlier study rats did not learn to return to a start location in a four-arm radial water maze when the start location varied across trials. Here we examine return to start location in appetitive tasks. Initially, rats were released from one of three arms with the food located in the fourth arm. Once a rat found the food, a second arm was baited; either the start arm, for one group, or another fixed location, for a control group. Rats had difficulty finding the second food reward in the start arm, but not in another fixed location. Performance was similar when rats were trained with a three-arm maze. It was also observed that rats learned the initial fixed location more slowly if they were required to learn a variable second location. This suggested the nature of the journey affected the rate of problem solution. One explanation for the failure of rats to return to the start location is that the path integrator is reset upon reaching the first correct arm. In a final experiment, a foraging task was used where resetting of the path integrator should not occur. Again, rats failed to return to the start location when it varied across trials. These findings suggest rats did not time tag start locations, which indicates that there may be constraints on the occurrence of episodic-like memory in rats.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".