Critical mass: The rise of a touchscreen technology community for rodent cognitive testing
Bibliographic record
Abstract
The rise in the number of users and institutions utilizing the rodent touchscreen technology for cognitive testing over the past decade has prompted the need for knowledge mobilization and community building. To address the needs of the growing touchscreen community, the first international touchscreen symposium was hosted at Western University. Attendees from around the world attended talks from expert neuroscientists using touchscreens to examine a vast array of questions regarding cognition and the nervous system. In addition to the symposium, a subset of attendees was invited to partake in a hands-on training course where they received touchscreen training covering both hardware and software components. Beyond the two touchscreen events, virtual platforms have been developed to further support touchscreen users: (a) Mousebytes.ca, which includes a data repository of rodent touchscreen tasks, and (b) Touchscreencognition.org, an online community with numerous training and community resources, perhaps most notably a forum where members can ask and answer questions. The advantages of the rodent touchscreen technology for cognitive neuroscience research has allowed neuroscientists from diverse backgrounds to test specific cognitive processes using well-validated and standardized apparatus, contributing to its rise in popularity and its relevance to modern neuroscience research. The commitment of the touchscreen community to data, task development and information sharing not only ensures an expansive future of the use of rodent touchscreen technology but additionally, quality research that will increase translation from preclinical studies to clinical successes.
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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 | 0.077 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".