Using approach latency and anticipatory behaviour to assess whether voluntary playpen access is rewarding to laboratory mice
Bibliographic record
Abstract
Laboratory mice are typically housed in "shoebox" cages that limit the expression of natural behaviours. Temporary access to more complex environments (playpens) may improve their welfare. We aimed to assess if access to playpens is rewarding for conventionally-housed mice and to document mouse behaviour during playpen access. Female C57BL/6J, BALB/cJ, and DBA/2J mice were provided temporary access to a large enriched playpen three times per week; control mice remained in their home cages. We measured latency to enter playpens and anticipatory behaviour to determine if access was rewarding, and recorded mouse behaviour during playpen sessions. Over time, playpen mice entered the playpen more quickly; latency declined from 168 ± 22 to 13 ± 2 s over the 14-d trial. As expected, playpen mice showed an increase in anticipatory behaviour before playpen access (mean ± SE = 19.7 ± 2.6 behavioural transitions), while control mice showed no change in anticipatory behaviour relative to baseline values (2.4 ± 1.6 transitions). Mice in the playpen performed more ambulatory behaviours than control mice who remained in home cages (21.5 ± 0.7 vs 6.9 ± 1.1 observations of 25 total observations). We conclude that conventionally-housed mice find voluntary playpen access rewarding, and suggest this as a useful option for providing laboratory mice with access to more complex environments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".