Neurocognitive Model of Schema-Congruent and -Incongruent Learning in Clinical Disorders: Application to Social Anxiety and Beyond
Bibliographic record
Abstract
Negative schemas lie at the core of many common and debilitating mental disorders. Thus, intervention scientists and clinicians have long recognized the importance of designing effective interventions that target schema change. Here, we suggest that the optimal development and administration of such interventions can benefit from a framework outlining how schema change occurs in the brain. Guided by basic neuroscientific findings, we provide a memory-based neurocognitive framework for conceptualizing how schemas emerge and change over time and how they can be modified during psychological treatment of clinical disorders. We highlight the critical roles of the hippocampus, ventromedial prefrontal cortex, amygdala, and posterior neocortex in directing schema-congruent and -incongruent learning (SCIL) in the interactive neural network that comprises the autobiographical memory system. We then use this framework, which we call the SCIL model, to derive new insights about the optimal design features of clinical interventions that aim to strengthen or weaken schema-based knowledge through the core processes of episodic mental simulation and prediction error. Finally, we examine clinical applications of the SCIL model to schema-change interventions in psychotherapy and provide cognitive-behavior therapy for social anxiety disorder as an illustrative example.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".