Expectancies and the generation of perceptual experience: Predictive processing and phenomenological control
Bibliographic record
Abstract
Phenomenological control is the stable trait ability to alter subjective experience in accordance with goals. Although voluntary, phenomenological control is experienced as involuntary and has predominantly been studied within the context of hypnosis, in which direct verbal imaginative suggestions for a range of experiences are given by a designated ‘hypnotist’ (e.g., involuntary movement, paralysis, analgesia, amnesia or auditory, gustatory, tactile and visual hallucinations). However, hypnosis is not required for phenomenological control and the ability can be exercised in a variety of contexts, including in response to demand characteristics in scientific experiments. Here we trace the modern history of phenomenological control from the context of mesmerism in 18th century (which employed indirect, non-verbal suggestion) through to contemporary accounts. We focus on three theories: response expectancy theory (in which experience arises directly from expectancies), cold control theory (in which voluntary acts are experienced as involuntary due to being unaware of relevant intentions) and the predictive processing theory of hypnosis (in which voluntary acts arise from aberrant interplay of top-down predictions and bottom-up prediction error signals). We consider the pros and cons of each and explore the extent to which predictive processing might be extended to offer a full-fledged theory of phenomenological control.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".