The Optimal Learning Cocktail for Placebo Analgesia: A Randomized Controlled Trial Comparing Individual and Combined Techniques
Bibliographic record
Abstract
This study investigated for the first time the effects of individual and combined application of 3 learning techniques (verbal suggestions, classical conditioning, and observational learning) on placebo analgesia and extinction. Healthy participants (N = 206) were assigned to 8 different groups in which they were taught through either a verbal suggestion, a conditioning paradigm, a video observing someone, or any combination thereof that a placebo device (inactive transcutaneous electric nerve stimulation [TENS]) was capable of alleviating heat pain, whereas one group did not (control). Placebo analgesia was quantified as the within-group difference in experienced pain when the placebo device was (sham) 'activated' or 'inactivated' during equal pain stimuli, and compared between groups. Placebo analgesia was induced in groups with 2 or 3 learning techniques. Significantly stronger placebo analgesia was induced in the combination of all 3 learning techniques as compared to the individual learning techniques or control condition, underlining the additional contribution of 3 combined techniques. Extinction did not differ between groups. Furthermore, pain expectancies, but not state anxiety or trust, mediated placebo analgesia. Our findings emphasize the added value of combining 3 learning techniques to optimally shape expectancies that lead to placebo analgesia, which can be used in experimental and clinical settings. PERSPECTIVE: This unique experimental study compared the individual versus combined effects of 3 important ways of learning (verbal suggestions, classical conditioning, and observational learning) on expectation-based pain relief. The findings indicate that placebo effects occurring in clinical practice could be optimally strengthened if healthcare providers apply these techniques in combination.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".