Causal Learning With Interrupted Time Series
Causal Learning With Interrupted Time Series
复制标题
DOI：
--
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Yiwen Zhang;Benjamin M. Rottman
中科院分区：
文献类型：
--
作者：
Yiwen Zhang;Benjamin M. Rottman
中断时间序列分析（ITSA）是一种统计程序，用于评估干预是否会导致时间序列的截距和/或斜率发生变化。然而，很少有研究访问中断的时间序列情况下的因果学习。我们系统地研究了人们是否能够从类似于ITSA的过程中学习因果影响，并比较了四种不同的刺激呈现格式。我们发现，在干预前斜率为零或与截距或斜率变化方向相同的情况下，参与者的判断与ITSA一致。然而，当干预前斜率与截距或斜率的变化方向相反时，参与者很难控制干预前斜率。在大多数情况下，演示格式并不影响判断，但在一个。我们讨论了这些结果的两个潜在的appropriistics，人们可能会使用除了一个类似于ITSA的过程。
Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants’ judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. However, participants had considerable difficulty controlling for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn’t affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA.