Ping Pong in Church: Productive use of concepts in human probabilistic inference
Tobias Gerstenberg, Noah D. Goodman · eScholarship (California Digital Library) · 2012
How do people make inferences from complex patterns of ev-idence across diverse situations? What does a computational model need in order to capture the abstract knowledge peo-ple use for everyday reasoning? In this paper, we explore a novel modeling framework based on the probabilistic lan-guage of thought (PLoT) hypothesis, which conceptualizes thinking in terms of probabilistic inference over composition-ally 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 repre-sents 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 partici-pants ’ judgments. Our model accurately predicts how people reason with confounded and indirect evidence and how differ-ent sources of information are integrated.