Garrett Neil, Sharot Tali
School of Psychology, University of East Anglia, Norwich, UK.
Affective Brain Lab, Department of Experimental Psychology, University College London, London, UK.
J Cogn Psychol (Hove). 2023 Aug 14;35(8):876-886. doi: 10.1080/20445911.2023.2245112. eCollection 2023.
In a recent paper, Burton et al. claim that individuals update beliefs to a greater extent when learning an event is less likely compared to more likely than expected. Here, we investigate Burton's et al.'s, findings. First, we show how Burton et al.'s data do not in fact support a belief update bias for neutral events. Next, in an attempt to replicate their findings, we collect a new data set employing the original belief update task design, but with neutral events. A belief update bias for neutral events is not observed. Finally, we highlight the statistical errors and confounds in Burton et al.'s design and analysis. This includes mis-specifying a reinforcement learning approach to model the data and failing to follow standard computational model fitting sanity checks such as parameter recovery, model comparison and out of sample prediction. Together, the results find little evidence for biased updating for neutral events.
在最近的一篇论文中,伯顿等人声称,与预期可能性更高的情况相比,当个体了解到某一事件不太可能发生时,他们会在更大程度上更新信念。在此,我们对伯顿等人的研究结果进行调查。首先,我们展示了伯顿等人的数据实际上并不支持中性事件的信念更新偏差。接下来,为了试图复制他们的研究结果,我们采用原始的信念更新任务设计收集了一个新的数据集,但使用的是中性事件。未观察到中性事件的信念更新偏差。最后,我们强调了伯顿等人的设计和分析中的统计错误及混淆因素。这包括错误地指定一种强化学习方法来对数据进行建模,以及未能遵循标准的计算模型拟合合理性检查,如参数恢复、模型比较和样本外预测。综合来看,这些结果几乎没有发现中性事件存在偏差更新的证据。