Optimizing reproducibility of operant testing through reinforcer standardization: identification of key nutritional constituents determining reward strength in touchscreens
Bibliographic record
Abstract
Reliable and reproducible assessment of animal learning and behavior is a central aim of basic and translational neuroscience research. Recent developments in automated operant chamber technology have led to the possibility of universal standard protocols, in addition to increased translational potential, reliability and accuracy. However, the impact of regional and national differences in the supplies of available reinforcers in this system on behavioural performance and inter-laboratory variability is an unknown and at present uncontrolled variable. Therefore, we aimed to identify which constituent(s) of the reward determines reinforcer strength to enable improved standardization of this parameter across laboratories. Male C57BL/6 mice were examined in the touchscreen-based fixed ratio (FR) and progressive ratio (PR) schedules, reinforced with different kinds of milk-based reinforcers to directly compare the incentive values of plain milk (PM, high-calorie: high-fat/low-sugar), strawberry-flavored milk (SM, high-calorie: low-fat/high-sugar), and semi-skimmed low-fat milk (LM, low-calorie: low-fat/low-sugar) on the basis of differences in caloric content, sugar/fat content, and flavor. Use of a higher caloric content reward was effective in increasing operant training acquisition rate. Total trial number completed in FR and breakpoint in PR were higher using the two isocaloric milk products (PM and SM) than the lower caloric LM, with comparable outcomes between PM and SM conditions, suggesting that total caloric content determines reward strength. Analysis of within-session changes in response rate revealed that overall outputs in FR and PR primarily depend on the response rate at the initial phase of a session, which itself was dependent on reinforcer caloric content. Interestingly, the rate of satiation, indicated by decay in response rate within a FR session, was highest when reinforced with SM, suggesting a rapid satiating effect of sugar. The key contribution of reward caloric content to operant performance was confirmed in a multi-laboratory study using the touchscreen 5-choice serial reaction time task (5-CSRTT) reinforced by two isocaloric milk-based liquid rewards with different countries of origin, which yielded consistent performance parameters across sites. Our results indicate that milk-based liquid reinforcer standardization can be facilitated by matching caloric content across laboratories despite regional or national differences in other non-caloric aspects of the reinforcers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".