How effective is incidental learning of the shape of probability distributions?
How effective is incidental learning of the shape of probability distributions?
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DOI：
10.1098/rsos.170270
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发表时间：
2017-08
影响因子：
3.5
通讯作者：
Pashler H
中科院分区：
文献类型：
--
作者：
Tran R;Vul E;Pashler H
人们学习他们与之互动的环境的详细概率生成模型的想法直观上很吸引人，并且得到了最近对日常生活中获得的内隐知识的研究的支持。这项研究的目的是看看人们是否有效地诱导概率分布的基础上偶然暴露于一个未知的生成过程。受试者玩了一个“打鼹鼠”游戏，在这个游戏中，他们试图点击屏幕上短暂出现的物体，每次一个。对象的水平位置从双峰分布生成。在玩了180次游戏后，受试者被要求从相同的分布中生成另外180个目标位置。他们的反应甚至没有显示出双峰分布，更不用说一个准确的（实验1）。这同样适用于预先宣布的测试（实验2）。另一方面，在中间区域具有零密度的更极端的双峰性确实产生了一些分布学习（实验3），这可能反映了有意识的假设检验。我们讨论了这对高效、准确的分布式学习理念带来的挑战。
The idea that people learn detailed probabilistic generative models of the environments they interact with is intuitively appealing, and has received support from recent studies of implicit knowledge acquired in daily life. The goal of this study was to see whether people efficiently induce a probability distribution based upon incidental exposure to an unknown generative process. Subjects played a ‘whack-a-mole’ game in which they attempted to click on objects appearing briefly, one at a time on the screen. Horizontal positions of the objects were generated from a bimodal distribution. After 180 plays of the game, subjects were unexpectedly asked to generate another 180 target positions of their own from the same distribution. Their responses did not even show a bimodal distribution, much less an accurate one (Experiment 1). The same was true for a pre-announced test (Experiment 2). On the other hand, a more extreme bimodality with zero density in a middle region did produce some distributional learning (Experiment 3), perhaps reflecting conscious hypothesis testing. We discuss the challenge this poses to the idea of efficient accurate distributional learning.
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