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引用次数: 0
精神疾病的计算方法
对精神病理学的理论理解一直依赖于现象学(即临床观察)。这种描述性方法虽然简单易懂,但在解决精神疾病在诊断和本体论方面的复杂性问题上却受到严重限制。昆廷-怀斯及其同事雄辩地概述了这种计算视角,并对判别建模和生成建模进行了区分。前者包括统计模型(如潜在变量模型、网络模型和机器学习模型),这些模型已经成为心理学家方法论武器库的一部分。这些数据驱动的模型在揭示诸如精神病理学症状之间的强化关系或失业与成瘾之间的紧密联系等现象方面非常有用。然而,这些模型在解释这些现象方面却存在不足:由于其数据生成机制具有一定的普遍性,它们无法说明(最有可能)产生拟合反应的精确过程。
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