Can any procedure be hypnosis? Exploring the effect of framing on hypnotic depth and electrophysiological correlates of hypnosis in a balanced placebo design.
Bibliographic record
Abstract
Expectancy theory of hypnosis posits that any procedure can serve as a hypnotic induction provided it is labelled as “hypnosis”. The present study explored this hypothesis by contrasting the effects of two conventional and two unconventional (sham) hypnotic inductions on hypnotic experiences and electrophysiological correlates. In a 2x2 balanced placebo design, all participants were exposed to four conditions: conventional induction labeled as “hypnosis”, conventional induction labeled as “control”, unconventional induction labeled as “hypnosis”, and unconventional induction labeled as “control”. EEG was recorded from 128 channels. We computed EEG features that were identified in previous studies as correlates of hypnosis or hypnotizability. Consistent with the predictions of expectancy theory, we found that one of the unconventional (sham) inductions, “white noise hypnosis”, evoked comparable hypnosis depth to the conventional hypnotic inductions. However, contrary to its predictions, “embedded hypnosis”, another unconventional induction, evoked smaller hypnosis depth reports than the other three inductions. Most EEG features we explored did not differ between conventional and unconventional induction conditions. A possible exception is frontal theta activity, which appeared to increase more in conventional induction trials. The change in frontal gamma power negatively correlated with hypnosis depth, and occipital theta activity positively correlated with hypnotizability in both conventional and unconventional inductions. Overall, our results provide partial support for the expectancy theory of hypnosis. However, our findings should be considered exploratory. Confirmatory research is required to strengthen our confidence in these effects.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".