J Exp Psychol Learn Mem Cogn. 2023 Jan;49(1):177. doi: 10.1037/xlm0001012. Epub 2021 Feb 8.
Reports an error in "Testing one or multiple: How beliefs about sparsity affect causal experimentation" by Anna Coenen, Azzurra Ruggeri, Neil R. Bramley and Todd M. Gureckis (, 2019[Nov], Vol 45[11], 1923-1941). In the article, there were errors in Equations 2 through 5. In Equation 2, ( l, ) should have been (l, = ). In Equation 3, the Sum should have been from = 1 to , and the log should not have been italicized. In Equation 4, the denominator of the fraction on the right hand side should have been\∑ {} ( l ) ( ). In Equation 5, the Sum should have been from = 1 to , and the log should not have been italicized. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2019-27247-001.) What is the best way of discovering the underlying structure of a causal system composed of multiple variables? One prominent idea is that learners should manipulate each candidate variable in isolation to avoid confounds (sometimes known as the control of variables [CV] strategy). We demonstrate that CV is not always the most efficient method for learning. Using an optimal actor model, which aims to minimize the average number of tests, we show that when a causal system is sparse (i.e., when the outcome of interest has few or even just one actual cause among the candidate variables), it is more efficient to test multiple variables at once. Across a series of behavioral experiments, we then show that people are sensitive to causal sparsity and adapt their strategies accordingly. When interacting with a dense causal system (high proportion of actual causes among candidate variables), they use a CV strategy, changing one variable at a time. When interacting with a sparse causal system, they are more likely to test multiple variables at once. However, we also find that people sometimes use a CV strategy even when a system is sparse. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
报告安娜·科嫩、阿祖拉·鲁杰里、尼尔·R·布拉姆利和托德·M·古雷基斯所著的《测试一个或多个：关于稀疏性的信念如何影响因果实验》（《》，2019年[11月]，第45卷[第11期]，1923 - 1941页）中的一处错误。在该文章中，方程2至5存在错误。在方程2中，( l, )应为(l, = )。在方程3中，求和应从 = 1到 ，且对数不应为斜体。在方程4中，右侧分数的分母应为\∑ {} ( l ) ( )。在方程5中，求和应从 = 1到 ，且对数不应为斜体。本文的在线版本已作修正。（以下是原始文章的摘要，记录于2019 - 27247 - 001）发现由多个变量组成的因果系统的潜在结构的最佳方法是什么？一个突出的观点是，学习者应单独操纵每个候选变量以避免混淆（有时称为变量控制[CV]策略）。我们证明CV并不总是学习的最有效方法。使用旨在最小化平均测试次数的最优参与者模型，我们表明当因果系统稀疏时（即当感兴趣的结果在候选变量中几乎没有或甚至只有一个实际原因时），一次性测试多个变量更有效。在一系列行为实验中，我们接着表明人们对因果稀疏性敏感并相应地调整他们的策略。当与密集因果系统（候选变量中实际原因的比例高）交互时，他们使用CV策略，一次改变一个变量。当与稀疏因果系统交互时，他们更有可能一次性测试多个变量。然而，我们也发现即使系统稀疏时人们有时也会使用CV策略。（PsycInfo数据库记录 (c) 版权所有2023美国心理学会）
