Bayesian modelling of Jumping-to-Conclusions bias in delusional patients
Bayesian modelling of Jumping-to-Conclusions bias in delusional patients
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DOI：
10.1080/13546805.2010.548678
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发表时间：
2011-01-01
影响因子：
1.7
通讯作者：
Dayan, Peter
中科院分区：
文献类型：
--
作者：
Moutoussis, Michael;Bentall, Richard P.;Dayan, Peter
导论.在决定连续呈现事件的潜在原因时，妄想症患者比对照组使用更少的事件，显示出“跳跃结论”偏倚。这被广泛假设是因为患者期望如果他们采样更多的信息，则会产生更高的成本。然而，这一假设是未经证实的。通过使用两种模型分析患者和对照数据来检验该假设。模型提供了明确的，定量的变量特征的决策。一个模型是基于计算做出决策的潜在成本;另一个模型是将确定性度量与固定阈值进行比较。偏执的参与者和对照组之间的差异被发现，但不是以前假设的方式。决策过程中更大的“噪音”（相对于完成任务的有效动机），而不是更大的感知成本，最能解释群体差异。偏执的参与者也偏离了理想的贝叶斯推理比健康对照组。跳到结论偏差不太可能是由于高估了收集更多信息的成本。我们使用的分析方法，涉及贝叶斯模型来估计不同参与者人群的特征参数，非常适合于测试关于支撑观察到的行为的“隐藏”变量的假设。
Introduction. When deciding about the cause underlying serially presented events, patients with delusions utilise fewer events than controls, showing a "Jumping-to-Conclusions'' bias. This has been widely hypothesised to be because patients expect to incur higher costs if they sample more information. This hypothesis is, however, unconfirmed.Methods. The hypothesis was tested by analysing patient and control data using two models. The models provided explicit, quantitative variables characterising decision making. One model was based on calculating the potential costs of making a decision; the other compared a measure of certainty to a fixed threshold.Results. Differences between paranoid participants and controls were found, but not in the way that was previously hypothesised. A greater "noise'' in decision making (relative to the effective motivation to get the task right), rather than greater perceived costs, best accounted for group differences. Paranoid participants also deviated from ideal Bayesian reasoning more than healthy controls.Conclusions. The Jumping-to-Conclusions Bias is unlikely to be due to an overestimation of the cost of gathering more information. The analytic approach we used, involving a Bayesian model to estimate the parameters characterising different participant populations, is well suited to testing hypotheses regarding "hidden'' variables underpinning observed behaviours.