Ping Pong in Church: Productive use of concepts in human probabilistic inference
Ping Pong in Church: Productive use of concepts in human probabilistic inference
复制标题
教堂里的乒乓球：在人类概率推理中有效地运用概念
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Noah D. Goodman
中科院分区：
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作者：
Tobias Gerstenberg;Noah D. Goodman
人们如何从潜水员的复杂证据中推断出来？思想（情节）假设，它概念化了对综合形式的概率推断的思考。在概率编程教堂（Goodman，Mansinghka，Roy，Bonawitz和Tenenbaum，2008年）中实现。在两个实验中，不同的证据表明了我们的模型的预测与参与者的法官之间的贴合性非常紧密。人们以混杂和间接的证据以及如何整合不同的信息来源来理解。
How do people make inferences from complex patterns of evidence across diverse situations? What does a computational model need in order to capture the abstract knowledge people use for everyday reasoning? In this paper, we explore a novel modeling framework based on the probabilistic language of thought (PLoT) hypothesis, which conceptualizes thinking in terms of probabilistic inference over compositionally structured representations. The core assumptions of the PLoT hypothesis are realized in the probabilistic programming language Church (Goodman, Mansinghka, Roy, Bonawitz, & Tenenbaum, 2008). Using “ping pong tournaments” as a case study, we show how a single Church program concisely represents the concepts required to specify inferences from diverse patterns of evidence. In two experiments, we demonstrate a very close fit between our model’s predictions and participants’ judgments. Our model accurately predicts how people reason with confounded and indirect evidence and how different sources of information are integrated.