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计算参数与明显行为的测试-重新测试可靠性,用于精神病中的决策灵活性

Güldamla Kalender¹, Sarah T Olsen¹, Edward H Patzelt²

¹Department of Psychiatry and Behavioral Sciences, University of Minnesota Twin Cities.

Psychological assessment

|April 7, 2025

概括

计算精神病学评估显示,对行为和计算参数的测试-重新测试可靠性差到中等. 对于这些有前途的计算工具的临床使用,需要进一步优化.

科学领域:

计算精神病学是一种计算精神病学.
神经科学是一个神经科学.
心理测量心理测量心理测量

背景情况:

计算精神病学使用行为和神经计算措施量化精神病理.
计算评估的心理测量特性,特别是测试-重试可靠性,研究不足,但对于临床应用至关重要.

研究的目的:

从概率奖励和反转学习任务中评估行为和计算参数的测试-重新测试可靠性.
评估两个计算模型的可靠性:回归和隐藏的马尔科夫模型.

主要方法:

179名成年人,包括患有精神病谱系障碍的人和对照人,完成了双臂强盗任务两次.
类内相关性 (ICC) 用于测量明显行为和计算参数的可靠性.
分析了两个计算模型:回归建模和隐藏的马尔科夫模型.

主要成果:

对明显行为 (ICCs 0.240.54) 和计算参数 (ICCs 0.300.61) 的测试-再测试可靠性从差到中等.
计算参数没有显示出比明显行为更高的可靠性.
与临床组相比,在健康成年人中,可靠性通常更高,尽管不显著.

结论:

目前在精神病学中的计算评估对广泛的临床实用性不够可靠.
任务和分析方法需要优化,以增强计算精神病学工具的心理测量特性.
尽管存在局限性,但计算建模仍然是理解和量化精神疾病的有希望的途径.

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