用深度学习提升人格测试诊断精度与可解释性
A Forced-Choice Neural Cognitive Diagnostic Model of Personality Testing
- 基于神经网络建模被试与题项的非线性交互
- 在真实与模拟数据上均实现高准确率与鲁棒性
- 适合心理测评、人才选拔等需可解释诊断的场景
智能时代，心理测量在人才选拔、职业发展和心理健康评估中愈发重要。强迫选择测试因要求受试者在相近选项间抉择，能有效降低应答偏差，是人格评估常用形式。本文提出一种基于深度学习的强迫选择神经认知诊断模型（FCNCD），克服传统模型局限，适用于强迫选择测试中最常见的三种题项结构。为处理题项单维度特性，模型构建可解释的被试与题项参数，通过非线性映射提取特征后，利用多层神经网络建模其交互关系，并引入单调性假设增强诊断结果可解释性。在真实世界及模拟数据集上的实验验证了该模型在准确性、可解释性和鲁棒性方面的有效性。
原文摘要 · Abstract (English)
In the smart era, psychometric tests are becoming increasingly important for personnel selection, career development, and mental health assessment. Forced-choice tests are common in personality assessments because they require participants to select from closely related options, lowering the risk of response distortion. This study presents a deep learning-based Forced-Choice Neural Cognitive Diagnostic Model (FCNCD) that overcomes the limitations of traditional models and is applicable to the three most common item block types found in forced-choice tests. To account for the unidimensionality of items in forced-choice tests, we create interpretable participant and item parameters. We model the interactions between participant and item features using multilayer neural networks after mining them using nonlinear mapping. In addition, we use the monotonicity assumption to improve the interpretability of the diagnostic results. The FCNCD's effectiveness is validated by experiments on real-world and simulated datasets that show its accuracy, interpretability, and robustness.
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