精神分裂症的概率推理是反复无常的,但没有偏见

G. Pfuhl, H. Tjelmeland
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引用次数: 1

摘要

我们根据证据更新我们的信念。异常的信念更新与精神分裂症和自闭症有关。目前尚不清楚错误的更新是由于一般认知能力的降低,对最近信息的过度重视,还是从一种信念转换到另一种信念的阈值较低。评估信念更新的一个常见任务是珠子任务。精神分裂症患者表现出草率的决策。本文提出了一个模型,描述了与理想贝叶斯观察者的偏差,并将该模型应用于三个独立的数据集,共n=176名健康对照和n=128名精神分裂症患者。参数描述了a)考虑的珠子数量(内存),b)系统偏差和c)概率估计的非系统偏差(波动性)。我们发现,平均而言,患者使用较少的珠子和或更不稳定的反应。然而,平均而言,患者的概率估计更接近真实概率。更深入的调查发现了所使用的数据集和序列之间的相关差异。更具挑战性的序列可以提高患者的表现。我们的模型很好地捕捉了导致珠子任务中表现差异的认知机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probabilistic reasoning in schizophrenia is volatile but not biased
We update our beliefs based on evidence. Aberrant belief updating has been linked to schizophrenia and autism. It is not clear whether the faulty updating is due to reducedgeneral cognitive abilities, overweighting of recent information, or lower thresholds for switching from one belief to another. A common task to assess belief updating isthe beads task. Patients with schizophrenia show hasty decision-making.We here present a model describing the deviations from an ideal Bayesian observer and apply the model to three independent datasets, totalling n=176 healthy controlsand n=128 patients with schizophrenia. The parameters describe a) the number of beads considered (memory), b) systematic deviation and c) unsystematic deviations (volatility) from probability estimates.We find that, on average, patients use fewer beads and or more volatile responding. However, patients have, on average, probability estimates that are closer to the true probabilities. Closer investigations yielded relevant differences among the datasets and sequences used. Morechallenging sequences improve the performance of patients.Our model captures well the cognitive mechanisms proposed to contribute to the performance differences in the beads task.
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