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Record W4404902730 · doi:10.1080/02699931.2024.2433516

Targeting schema change in social anxiety via autobiographical memory reconstruction

2024· article· en· W4404902730 on OpenAlexafffund
Signy Sheldon, Luke Atack, Nguyet Ngo, Morris Moscovitch, David A. Moscovitch

Bibliographic record

VenueCognition & Emotion · 2024
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of WaterlooUniversity of TorontoMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyAutobiographical memorySchema (genetic algorithms)RecallSocial anxietyCognitive psychologyCognitionPerceptionAnagramsConceptual schemaAnxietyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.052
GPT teacher head0.345
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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