A behavioural assay for investigating cued conflict between allocentric and egocentric spatial memory with instinctive escape in mice
Bibliographic record
Abstract
Instinctive escape behavior is widely recognized as a reliable model for assessing spatial memory in mice. Here, we present a novel behavioral assay designed to evaluate how mice navigate toward a learned goal relying on a variable proximal LED landmark or internal egocentric cues to provide spatial orientation in the absence of distal cues. Escape to a learned shelter was triggered by an innately aversive auditory stimulus in the presence of a proximal LED landmark. To assess cue preference (egocentric vs. allocentric), we introduced a cue-conflict situation where the allocentric LED cue was deliberately shifted away from the previously visited shelter location. In a no-conflict scenario, the LED stayed directly above the shelter. Baseline tests in C57BL/6 J mice showed a decreased reliance on allocentric cues as the LED landmark deviated further from the actual shelter. When the disparity between the LED cue and the shelter location exceeded a threshold, the mice began to favor egocentric strategies over allocentric ones. To demonstrate its utility, we applied the cue-conflict assay in a cross-sectional study of the 5xFAD Alzheimer's disease (AD) mouse model at early pathological stages (2-3 months of age). Escape profiles in AD and wild-type (WT) mice revealed a complex, genotype-dependent pattern of behavior across LED shift conditions, independent of disease progression. Although both AD and WT mice exhibited a higher incidence of allocentric (LED-directed) escapes when the LED shift was small, our analysis did not reveal significant changes in escape strategy that were associated with condition, genotype or age. Future studies using neural recording and manipulation techniques can further elucidate the circuit-level mechanisms underlying the cue conflict resolution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".