Preface to the Special Issue “Touchscreen Testing to Investigate the Neurochemistry of Cognition”
Bibliographic record
Abstract
Touchscreen-based methodologies have enabled significant advancements in cognitive neuroscience by providing standardized, translationally relevant assessments of advanced cognitive functions in rodent models. This special issue highlights the potential of these systems to bridge animal and human research, providing insights into the neurochemical and biological mechanisms underlying cognition. The included studies explore diverse applications, from understanding the cognitive impacts of chronic stress and maternal immune activation to evaluating the effectiveness of novel therapeutics and assessing cross-species cognitive testing approaches that enhance translational relevance. By combining touchscreen technologies with cutting-edge approaches like electrophysiology and open science databases, these contributions underscore the critical role of automated systems in advancing translational research. Together, they lay the foundation for novel therapeutic strategies to address cognitive deficits in brain 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".