Experience with featural-cue reliability influences featural- and geometric-cue use by mice (Mus musculus).
Bibliographic record
Abstract
Orienting is a critical skill for all mobile animals. Two commonly studied visual components used to guide orientation in an environment are geometric (e.g., distance or direction) and featural cues (e.g., color or texture). Previous research has shown that visual-cue use and cue weighing can depend on the navigator's previous experience, the nature and reliability of the cues, and genetic factors. Accordingly, the domestic mouse (Mus musculus) is a species of increasing interest because of its potential as a model for human neurological disorders with associated spatial disorientation, as is seen in Alzheimer's disease. In the present study, adult C57BL/6 mice were trained to search for a hidden food reward in one corner of a rectangular environment with featural information displayed continuously along the walls. After training, one group of mice was given a block of testing in which the featural information was removed, followed by a second block of testing in which the featural information was put in conflict with the learned configuration of featural and geometric cues. A second group of mice was given the same set of tests, but in the reverse order. Our results show that the mice incidentally encoded the geometry of the environment if they had experience with featural cues being unreliable prior to tests, during which featural cues were completely removed (unstable). Furthermore, we found when featural and geometric cues provide conflicting spatial information, this unreliability of featural cues over the course of the study may influence cue weighing. (PsycINFO Database Record
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".