用少量脑数据高效预测心理韧性，提升评估客观性。
Data-Efficient Model for Psychological Resilience Prediction based on Neurological Data
- 基于神经柯尔莫戈洛夫-阿诺德网络，设计小样本学习架构。
- 在公开与自建数据集上表现优异，验证模型有效性。
- 适合心理韧性研究、脑机接口及小样本医疗建模人群。
心理韧性指从逆境中恢复的能力，对心理健康至关重要。相较于依赖自我报告问卷的传统评估方式，基于神经数据的评估能提供生物标记物支持，显著提升结果客观性。本文提出一种新型数据高效模型以应对神经数据稀缺问题。采用神经柯尔莫戈洛夫-阿诺德网络作为预测模型结构。训练阶段引入一种受特质启发的多模态表示算法，结合智能分块技术，在有限数据下学习共享潜在空间；测试阶段提出一种受噪声启发的推理算法，以应对神经数据信噪比低的问题。所提模型在多个公开数据集和自建数据集上均表现出色，并为未来研究提供了有价值的心理学假设。
原文摘要 · Abstract (English)
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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