Assessing cognitive flexibility in mice using a custom-built touchscreen chamber
Bibliographic record
Abstract
Automated touchscreen systems have become increasingly prevalent in rodent model screening. This technology has significantly enhanced cognitive and behavioral assessments in mice and has bridged the translational gap between basic research using rodent models and human clinical research. Our study introduces a custom-built touchscreen operant conditioning chamber powered by a Raspberry Pi and a commercially available computer tablet, which effectively addresses the significant cost barriers traditionally associated with this technology. In order to test our prototype, we decided to train C57BL/6 mice on a visual discrimination serial-reversal task, and both C57BL/6 and App NL−G−F strain - an Alzheimer’s Disease (AD) mouse model - on a new location discrimination serial-reversal task. The results demonstrated a clear progression toward asymptotic performance, particularly in the location discrimination task, which also revealed potential genotype-specific deficits, with App NL−G−F mice displaying an increase in the average number of errors in the first reversal as well as in perseverative errors, compared to wild-type mice. These results validate the practical utility of our touchscreen apparatus and underline its potential to provide insights into the behavioral and cognitive markers of neurobiological disorders.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".