The role of the response–outcome association in the nature of inhibitory Pavlovian-instrumental transfer in rats
Bibliographic record
Abstract
Inhibitory stimuli can reduce animals' reward seeking in an outcome-specific manner or outcome-general manner. However, we do not understand the factors that determine which of these effects are produced. To address this, we carried out three experiments which examined whether instrumental training with one or multiple outcomes determined the nature of subsequently observed Pavlovian-instrumental transfer (PIT). Rats underwent Pavlovian training to produce inhibitors and excitors for two outcomes using a feature-negative procedure. In Experiment 1, these stimuli were tested for their effects on a single response trained with one of those outcomes in a PIT procedure. Here, stimuli trained as inhibitors and excitors were found to produce outcome-general effects on reward seeking (in addition to an outcome-specific effect for excitors). In Experiment 2, we trained two responses, one for each of the Pavlovian outcomes, and tested the effect of the stimuli on each response individually. This design also produced outcome-general inhibitory and excitatory PIT effects. Experiment 3 followed the procedure of Experiment 2, except for implementation of a shorter Pavlovian training phase and an additional choice test, where both responses were concurrently available. This procedure produced putative inhibitory effects that were also outcome-general. However, outcome-specific excitatory effects were observed, indicating that the general inhibitory results may not be attributable to the duration of Pavlovian training. Overall, this study suggests that variations in the number of response-outcome contingencies experienced by animals do not readily determine the specificity of putative inhibitors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".