Predicting Self-Esteem Instability Using Rejection Sensitivity, Daily Stress Reactivity, Social Feedback Valence, and Emotional Reactivity in LSTM Models
Keywords:
Self-esteem instability, Long Short-Term Memory (LSTM), Ecological Momentary Assessment, Rejection sensitivity, Emotional reactivity, Machine learningAbstract
Objective: To evaluate the efficacy of a Long Short-Term Memory ( ) neural network in predicting intra-individual self-esteem instability utilizing continuous ecological momentary assessment data encompassing trait rejection sensitivity, daily stress reactivity, social feedback valence, and momentary emotional reactivity.
Methods and Materials: A longitudinal Ecological Momentary Assessment study was conducted over days with a final sample of young adults from Spain. Participants completed the Rejection Sensitivity Questionnaire and responded to multiple daily prompts assessing momentary stress, social feedback valence, emotional reactivity, and state self-esteem. An neural network was designed to capture the non-linear, time-dependent relationships within this multi-dimensional data, and its performance was benchmarked against traditional linear regression and standard Recurrent Neural Network ( ) architectures.
Findings: The proposed architecture significantly outperformed baseline models, capturing of the variance in self-esteem instability ( ) with a low prediction error ( ). Permutation feature importance analysis revealed that momentary emotional reactivity and trait rejection sensitivity were the strongest predictors of self-esteem fluctuations. Additionally, an ablation study on look-back windows identified a -hour period ( assessments) as the optimal temporal framework for forecasting instability.
Conclusion: Advanced deep learning models, specifically networks, can accurately decode the complex, non-linear temporal dynamics of self-esteem, providing a powerful computational foundation for future personalized, real-time psychological interventions.
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References
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