Department of Psychology.
MPRG iSearch, Max Planck Institute for Human Development.
J Exp Psychol Learn Mem Cogn. 2019 Nov;45(11):1923-1941. doi: 10.1037/xlm0000680. Epub 2019 May 16.
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) 2019 APA, all rights reserved).
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？一个突出的想法是，学习者应该单独操纵每个候选变量，以避免混淆（有时也称为变量控制[CV]策略）。我们证明 CV 并不总是学习的最有效方法。我们使用最优演员模型（aims to minimize the average number of tests），该模型旨在最小化平均测试次数，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只有少数甚至只有一个实际原因时），一次测试多个变量更有效。在一系列行为实验中，我们表明，人们对因果稀疏性敏感，并相应地调整他们的策略。当与密集的因果系统（候选变量中实际原因的比例较高）交互时，他们会一次更改一个变量。当与稀疏的因果系统交互时，他们更有可能一次测试多个变量。但是，我们还发现，即使系统稀疏，人们有时也会使用 CV 策略。（PsycINFO Database Record（c）2019 APA，保留所有权利）。
