The Touchscreen‐Based Trial‐Unique, Nonmatching‐To‐Location (TUNL) Task as a Measure of Working Memory and Pattern Separation in Rats and Mice
Bibliographic record
Abstract
The TUNL task is an automated touchscreen task used to evaluate the cognitive processes involved in working memory (WM) and spatial pattern separation in rodents. Both rats and mice can be used. To elicit working memory processes, the rodent must distinguish between a sample (familiar) light stimulus and a novel light stimulus after a delay. With a correct selection, the rodent will receive a food reward. A major benefit of TUNL compared to other similar tasks is the circumvention of spatial "mediating strategies" that the rodent may use to supplement or replace working memory processes to complete the task successfully. Each trial is 'unique', as the stimuli are pseudo-randomized between trials in an array of spatial locations. The TUNL task uses a progression of six training steps to teach the rodent the associated rules necessary to complete the full task. Task performance is typically measured by trials completed and by accuracy. Task accuracy can be evaluated across various spatial separations to engage hippocampal-dependent processes involved in spatial pattern separation. The latency between trial responses can also be evaluated, with food reward collection latency as a measure of motivation. The TUNL task can be used to assess working memory and cognitive deficits in rodent models with neurodegenerative and neurological disorders, providing a valuable tool to screen for new treatment options, in addition to assessing basic neurobiology. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Handling and habituation prior to training Basic Protocol 2: Initial Touch Training Basic Protocol 3: Must Touch Training Basic Protocol 4: Must Initiate Training Basic Protocol 5: Punish Incorrect Training Basic Protocol 6: Initial TUNL Training Basic Protocol 7: Full TUNL Training Support Protocol 1: Using ABET II touch program Support Protocol 2: Preparation of touchscreen chambers prior to training sessions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".