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

TL;DR

This paper introduces a data-efficient neurological model for predicting psychological resilience, leveraging novel algorithms to handle limited data and noisy signals, thereby improving objectivity and reliability of resilience assessments.

Contribution

It presents a novel Neuro Kolmogorov-Arnold Network-based model with trait-informed multimodal representation and noise-informed inference algorithms for resilience prediction.

Findings

Impressive performance on public and self-constructed datasets.

Provides valuable psychological hypotheses for future research.

Enhances objectivity of resilience assessment using neurological data.

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.