Cognitive shortcuts in causal inference
Cognitive shortcuts in causal inference
复制标题
因果推理中的认知捷径
DOI：
10.1080/19462166.2012.682655
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
B. Rehder
中科院分区：
文献类型：
--
作者：
Philip M. Fernbach;B. Rehder
本文探讨的想法，基于cavity的概率判断是由两个相互竞争的驱动器：一个对veridicality和一个对努力减少。参与者被教导的因果结构的新类别，并要求作出预测和诊断概率判断的类别范例的功能。我们发现，参与者违反了规范的因果贝叶斯网络模型的预测，因为他们忽略了相关变量（实验1-3），因为他们未能整合隐藏变量（实验2）。当任务变得更容易，说明是否存在替代原因，而不是不确定的，判断接近规范的预测（实验3）。我们的结论是，增加流行的因果贝叶斯网络计算框架与认知捷径，减少处理需求，可以提供一个更完整的因果推理。
The paper explores the idea that causality-based probability judgments are determined by two competing drives: one towards veridicality and one towards effort reduction. Participants were taught the causal structure of novel categories and asked to make predictive and diagnostic probability judgments about the features of category exemplars. We found that participants violated the predictions of a normative causal Bayesian network model because they ignored relevant variables (Experiments 1–3) and because they failed to integrate over hidden variables (Experiment 2). When the task was made easier by stating whether alternative causes were present or absent as opposed to uncertain, judgments approximated the normative predictions (Experiment 3). We conclude that augmenting the popular causal Bayes net computational framework with cognitive shortcuts that reduce processing demands can provide a more complete account of causal inference.
DOI：
10.1037/0278-7393.29.6.1141
发表时间：
2003-11-01
影响因子：
2.6
作者：
Rehder, B
通讯作者：
Rehder, B
影响因子：
5.4
作者：
Gopnik, A;Glymour, C;Danks, D
通讯作者：
Danks, D
影响因子：
5.4
作者：
Rottenstreich, Y;Tversky, A
通讯作者：
Tversky, A