BACKGROUND
Early maladaptive schemas (EMS) linked to depression are persistent, dysfunctional belief and behavior patterns acquired during critical developmental periods, resistant to change and challenging to address therapeutically. Peak emotional experiences have been shown to mitigate such schemas, suggesting a novel intervention pathway.
OBJECTIVE
This study aimed to evaluate whether AI-generated personalized clones of peak emotional stimuli could selectively deactivate negative schemas, offering a new approach to addressing entrenched cognitive patterns in depression.
METHODS
A total of 182 healthy participants were exposed to three variants of a peak emotional stimulus: the original, a cloned version, and a schema-specific clone targeting negativity. Emotional shifts in valence and arousal were measured pre- and post-exposure to assess the impact on negative schemas. Schema were assessed using the Young Schema Questionnaire (YSQ). Statistical analyses were conducted to compare the effects of the stimuli on schema mitigation.
RESULTS
All stimuli induced robust positive emotional shifts, confirming the method's effectiveness in generating positive affect. However, only the schema-specific stimulus significantly mitigated the targeted negativity schema, outperforming the other stimuli in reducing negative thinking patterns. Qualitative reports underscored the personalized stimulus's relevance and its potential for schema change.
CONCLUSIONS
The findings suggest that AI-generated, schema-specific peak emotional exposures can effectively modify stable maladaptive cognitions, offering a promising new approach to dismantling depressive schemas. This supports the utility of personalized emotional stimuli in mental health interventions, highlighting the potential of AI in enhancing therapeutic outcomes.