Bibliographic record
Abstract
Behavioural scientists typically classify cognitive processes as either controlled or automatic. Whereas controlled processes are slow and effortful, automatic processes are fast and involuntary. Cognitive researchers have recently begun investigating how top-down influence in the form of suggestion can allow individuals to modulate the automaticity of deeply ingrained processes. The present thesis surveys a background of converging findings that collectively indicate that certain individuals can derail involuntary processes, such as reading. We extend previous Stroop findings to several other well-established automatic paradigms, including the McGurk effect. We thus demonstrate how, in the case of highly suggestible individuals, suggestion seems to wield control over a process that is likely even more automatic than the Stroop effect. Furthermore, we present findings from two novel experimental paradigms exploring the potential of shifting automaticity in the opposite direction – i.e., transforming, without practice, a controlled task into one that is automatic. In addition, we present findings from an experiment leveraging de-automatization to illuminate a longstanding debate on the nature of hypnotic suggestibility: whether it reflects a stable trait determined by cognitive aptitude or a flexible skill amenable to attitudinal factors such as beliefs and expectations. We surreptitiously controlled light and sound stimuli to convince participants that they were responding strongly to hypnotic suggestions for visual and auditory hallucinations. Extending our previous findings, we indexed hypnotic suggestibility by de-automatizing an involuntary audiovisual phenomenon—the McGurk effect. Our findings intimate that, at least in the present experimental context, expectation hardly correlates with—and is unlikely to be a primary determinant of—high hypnotic suggestibility. Finally, the thesis concludes by addressing related evidence from the neuroscience of contemplative practices and discussing how these findings pave the road to a more scientific understanding of voluntary control and automaticity.
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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 | 0.000 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".