Beta-Blocking Pavlov’s Bells: Propranolol Attenuates Compound Extinction in an Error-Dependent Manner
Bibliographic record
Abstract
One hundred years ago, Pavlov observed that omitting the reinforcer that was previously paired with a conditional stimulus resulted in a decrease in the conditional response evoked by that stimulus, a phenomenon labeled extinction. Notably, Pavlov found that extinction was not permanent and the conditional response could recover under a range of conditions. As extinction is the basis of many modern therapies, the aim of much current work is to identify ways to enhance its longevity. To this end, one strategy is compound extinction, where a combination of previously reinforced stimuli is presented together for the first time during extinction training. This treatment has been shown to enhance extinction learning, evidenced by reduced future spontaneous recovery. However, the mechanisms are not fully understood. Experiment 1 assessed whether the compound extinction effect is the result of increased expectation of reward generated by the compound of stimuli that is then violated, driving further learning, or more simply, whether the novelty of the compound is able to re-engage attention and thus promotes extinction without increasing prediction error. Experiment 2 tested whether the effects of the noradrenaline β-receptor antagonist propranolol, shown elsewhere to reduce the compound extinction effect, relate to prediction error or novelty. We found that, when equating the novelty of the stimulus compound, larger prediction error resulted in better extinction evidenced as reduced spontaneous recovery. Further, we found that propranolol reduced this effect suggesting that prediction error rather than novelty recruits noradrenergic signalling. Together our results identify behavioural and pharmacological strategies that can improve the long-term expression of extinction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".