Data-Efficient Model for Psychological Resilience Prediction based on Neurological Data

• URL: http://arxiv.org/abs/2502.01377v1
• Date: Mon, 03 Feb 2025 14:15:15 GMT
• Title: Data-Efficient Model for Psychological Resilience Prediction based on Neurological Data
• Authors: Zhi Zhang, Yan Liu, Mengxia Gao, Yu Yang, Jiannong Cao, Wai Kai Hou, Shirley Li, Sonata Yau, Yun Kwok Wing, Tatia M. C. Lee,
• Abstract summary: This paper proposes a novel data-efficient model to address the scarcity of neurological data. We employ Neuro Kolmogorov-Arnold Networks as the structure of the prediction model.
• Score: 15.388682152950002
• License:
• Abstract: Psychological resilience, defined as the ability to rebound from adversity, is crucial for mental health. Compared with traditional resilience assessments through self-reported questionnaires, resilience assessments based on neurological data offer more objective results with biological markers, hence significantly enhancing credibility. This paper proposes a novel data-efficient model to address the scarcity of neurological data. We employ Neuro Kolmogorov-Arnold Networks as the structure of the prediction model. In the training stage, a new trait-informed multimodal representation algorithm with a smart chunk technique is proposed to learn the shared latent space with limited data. In the test stage, a new noise-informed inference algorithm is proposed to address the low signal-to-noise ratio of the neurological data. The proposed model not only shows impressive performance on both public datasets and self-constructed datasets but also provides some valuable psychological hypotheses for future research.

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