Elucidating unconscious processing with instrumental hypnosis
Bibliographic record
Abstract
Most researchers leverage bottom-up suppression to unlock the underlying mechanisms of unconscious processing. However, a top-down approach - for example via hypnotic suggestion - paves the road to experimental innovation and complementary data that afford new scientific insights concerning attention and the unconscious. Drawing from a reliable taxonomy that differentiates subliminal and preconscious processing, we outline how an experimental trajectory that champions top-down suppression techniques, such as those practiced in hypnosis, is uniquely poised to further contextualize and refine our scientific understanding of unconscious processing. Examining subliminal and preconscious methods, we demonstrate how instrumental hypnosis provides a reliable adjunct that supplements contemporary approaches. Specifically, we provide an integrative synthesis of the advantages and shortcomings that accompany a top-down approach to probe the unconscious mind. Our account provides a larger framework for complementing the results from core studies involving prevailing subliminal and preconscious techniques.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".