Suggesting adds an edge to automaticity: measuring, elucidating, and understanding positive hypnotic hallucinations
Bibliographic record
Abstract
A visual variation of the abstract is available in an interactive video format at razlab.mcgill.ca/thesis_aubertbonn.html* .Once automatized, cognitive processes seldom return to the purview of control; when they do, however, this reversal happens with much difficulty.Inspired by recent evidence introducing the role of suggestion in deautomatization, the present thesis elucidates how hypnotic suggestion renders a difficult task more automatic without extensive practice.Using MoTraK, a task inspired by a documented visual illusion, we investigated whether a specific hypnotic suggestion to view non-existent visual cues would increase performance.Our results show that highly suggestible individuals (i.e., participants who are likely to respond to hypnotic suggestion), but not controls, improved their accuracy aer receiving the suggestion.We discuss how these findings, beyond theoretical accounts of hypnosis and visual perception, hold potential clinical implications.In this regard, MoTraK may serve as a stepping stone in investigations concerning the regulation of mind and body through placebo responses/effects and top-down modulation.-vi
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".