A self-initiated cue-reward learning procedure for neural recording in rodents
Bibliographic record
Abstract
Abstract Background Single-unit recording in Pavlovian conditioning tasks requires the use of within-subject designs as well as sampling a considerable number of trials per trial type and session, which increases the total trial count. Pavlovian conditioning, on the other hand, requires a long average intertrial interval (ITI) relative to cue duration for cue-specific learning to occur. These requirements combined can make the session duration unfeasibly long. New Method To circumvent this issue, we developed a self-initiated variant of the Pavlovian magazine-approach procedure in rodents. Unlike the standard procedure, where the animals passively receive the trials, the self-initiated procedure grants animals agency to self-administer and self-pace trials from a predetermined, pseudorandomized list. Critically, whereas in the standard procedure the typical ITI is in the order of minutes, our procedure uses a much shorter ITI (10 s). Results Despite such a short ITI, discrimination learning in the self-initiated procedure is comparable to that observed in the standard procedure with a typical ITI, and superior to that observed in the standard procedure with an equally short ITI. Comparison with Existing Method(s) The self-initiated procedure permits delivering 100 trials in a ∼1-h session, almost doubling the number of trials safely attainable over that period with the standard procedure. Conclusions The self-initiated procedure enhances the collection of neural correlates of cue-reward learning while producing good discrimination performance. Other advantages for neural recording studies include ensuring that at the start of each trial the animal is engaged, attentive and in the same location within the conditioning chamber.
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".