Variations in the sense of agency during hypnotic responding: Insights from latent profile analysis.
Bibliographic record
Abstract
The primary phenomenological feature of a response to hypnotic suggestion is the perception that a person is not the author of their actions and experiences. This distortion in volition during hypnotic responding, known as the classic suggestion effect, has the potential to illuminate the neurocognitive mechanisms underlying hypnosis and inform broader models of agency. Here, we sought to clarify interindividual differences in the patterns of agency that participants experience during hypnosis. We applied latent profile analysis, a finite mixture modeling method for partitioning participants into homogeneous classes, to participants’ responses to a standardized behavioral measure of hypnotic suggestibility and an experiential measure of sense of agency during hypnotic responding. The best fitting model suggested that there were 4 discrete response patterns: a low suggestible class, 2 medium suggestible classes, and 1 highly suggestible class. The 2 medium suggestible classes displayed nearly equivalent patterns of behavioral hypnotic responding but diverged in their experience of agency during hypnotic responding: 1 class experienced greater involuntariness during responding, whereas the other experienced greater effortlessness during responding. These results reinforce previous research highlighting differential patterns of hypnotic responding and complement work suggesting that there may be 2 or more phenomenologically distinct modes of hypnotic responding. They also have a number of implications for the measurement of hypnotic responding and for the use of low and medium suggestible individuals in experimental hypnosis research designs.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".