Modulation of attentional bias by hypnosis: Disentangling the effect of induction and suggestion
Bibliographic record
Abstract
• Components of the hypnotic context modulate attentional bias in high suggestibles. • Induction and suggestion both contribute to this modulation. • A suggestion under hypnosis does not increase the size of the modulation. • The effects were not driven by session repetition or expectancy from verbal cues. • Our findings delineate the different components enabling hypnotic modulations. Hypnotic suggestions can modulate unintentional emotional processing. However, the specific contributions of hypnotic induction and suggestion — two central components of the hypnotic procedure — remain unclear. The present study aims to disentangle the effects of hypnotic induction and emotional numbing suggestion on the modulation of attentional bias in two experiments. In Experiment 1, high suggestible individuals (N = 34) performed an online emotional Stroop task in a two-by-two within-subject experimental design in which we crossed hypnotic induction and suggestion. Results show that both the emotional numbing suggestion — whether delivered within or outside the hypnotic context — and the relaxation-based hypnotic induction led to equivalent modulation of attentional bias. Experiment 2 tested the potential confounding effects of demand characteristics and of session repetition on the modulation of attentional bias in low suggestible individuals (N = 38). Results from this second experiment show no significant modulation of attentional bias across the four experimental sessions in this group. Our findings suggest that relaxation-based hypnotic induction and emotional numbing suggestion contribute to the modulation of attentional bias in high suggestible individuals. The results are discussed in line with socio-cognitive perspectives of the hypnotic induction, acting as a relaxation suggestion supporting emotional numbing 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 | 0.002 |
| 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 | 0.000 | 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".