Psychotherapy Augmentation through Preconscious Priming
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that repeated preconscious (masked) priming of personalized positive cognitions could augment cognitive change and facilitate achievement of patients' goals following a therapy. METHODS: Twenty social phobic patients (13 women) completed a 36-weeks study beginning by 12 weeks of group behavioral therapy. After the therapy, they received 6 weeks of preconscious priming and 6 weeks of a control procedure in a randomized cross-over design. The Priming condition involved listening twice daily with a passive attitude to a recording of individualized formulations of appropriate cognitions and attitudes masked by music. The Control condition involved listening to an indistinguishable recording where the formulations had been replaced by random numbers. Changes in social cognitions were measured by the Social Interaction Self Statements Test (SISST). RESULTS: Patients improved following therapy. The Priming procedure was associated with increased positive cognitions and decreased negative cognitions on the SISST while the Control procedure was not. The Priming procedure induced more cognitive change when applied immediately after the group therapy. CONCLUSION: An effect of priming was observed on social phobia related cognitions in the expected direction. This self administered addition to a therapy could be seen as an augmentation strategy.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".