Targeting schema change in social anxiety via autobiographical memory reconstruction
Bibliographic record
Abstract
Negative self-schemas are fundamental to social anxiety disorder and contribute to its persistence, thus understanding how to change schemas is of critical importance. Memory-based interventions and associated theories propose that reconstructing autobiographical memories tethered to schemas with conceptual details that challenge the associated expectations will lead to schema change. Here, we test this proposal in a between-subjects behavioural experiment with undergraduate participants with social anxiety. All participants were asked to recall aversive social memories, evaluated these memories on a series of scales, including estimates of reoccurrence, and provided ratings of negative and positive schema beliefs. Next, half the participants reconstructed (rescripted) these aversive memories with conceptual details that challenged the active schema (conceptual condition) and the other half reconstructed the memories with additional experiential details (perceptual condition). All participants provided again evaluations of the original memory and their schema beliefs. Our analysis revealed that the conceptual condition led to significant reductions in negative self-schemas, increases in positive self-schemas, and decreases in estimates of future negative event reoccurrence. Thus, effective schema-change, both a weakening of negative schemas and a strengthening of more positive, adaptive schemas, is dependent on altering the underlying meaning of associated autobiographical memories.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".