The Phenomenological Control Scale: Measuring the Capacity for Creating Illusory Nonvolition, Hallucination and Delusion
Bibliographic record
Abstract
Phenomenological control is the ability to generate experiences to meet expectancies. There are stable trait differences in this ability, as shown by responses to imaginative suggestions of, for example, paralysis, amnesia, and auditory, visual, gustatory and tactile hallucinations. Phenomenological control has primarily been studied within the context of hypnosis, in which suggestions are delivered following a hypnotic induction. Reports of substantial relationships between phenomenological control in a hypnotic context (hypnotizability) and experimental measures (e.g., the rubber hand illusion) suggest the need for a broad investigation of the influence of phenomenological control in psychological experiments. However, hypnosis is not required for successful response to imaginative suggestion. Because misconceptions about the hypnotic context may influence hypnotizability scores, a non-hypnotic scale which better matches the contextual expectancies of other experiments and avoids the hypnotic context is potentially better suited for such investigation. We present norms for the Phenomenological Control Scale (PCS), an adaptation of the Sussex Waterloo Scale of Hypnotizability (SWASH) which is free of the hypnotic context. Mean scores for the PCS are higher than for SWASH, and the subjective scales of PCS and SWASH show similar reliability. The PCS subjective scale is a reliable tool for measuring trait response to imaginative suggestion (i.e., phenomenological control) outside the context of hypnosis.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".