Optimization of apparatus design and behavioral measures for the assessment of visuo-spatial learning and memory of mice on the Barnes maze
Bibliographic record
Abstract
We have previously shown that apparatus design can affect visual-spatial cue use and memory performance of mice on the Barnes maze. The present experiment extends these findings by determining the optimal behavioral measures and test procedure for analyzing visuo-spatial learning and memory in three different Barnes maze designs. Male and female C57BL/6J mice were trained with a stable or random escape hole location and the sensitivities (statistical power) of four commonly used measures of learning and three measures of memory to detect differences between these training procedures were compared on each maze design. A maze design with a large diameter and no wall was optimal, because mice showed a reliable use of extra-maze visual cues, visuo-spatial search strategies, and spatial memory. A maze design with a small diameter, surrounding wall, and intra-maze visual cues was the least sensitive for determining visuo-spatial learning and memory, because mice showed little evidence of extra-maze cue use. Errors, distance traveled, and hole deviation scores were more sensitive measures of learning than latency to find the escape hole. Measures based on locating the escape hole (primary measures) were more sensitive than measures based on entering the escape hole (total measures). Measures of memory had similar levels of sensitivity on each maze. This experiment demonstrates that both apparatus design and the behavioral measures used as indicators of learning and memory can influence the ability of the Barnes maze to detect visuo-spatial learning and memory impairments in mice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".