andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-994 knowledge-graph by maker-knowledge-mining
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Introduction: Josh Tenenbaum describes some new work modeling people’s physical reasoning as probabilistic inferences over intuitive theories of mechanics. A general-purpose capacity for “physical intelligence”—inferring physical properties of objects and predicting future states in complex dynamical scenes—is central to how humans interpret their environment and plan safe and effective actions. The computations and representations underlying physical intelligence remain unclear, however. Cognitive studies have focused on mapping out judgment biases and errors, or on testing simple heuristic models suitable only for highly specific cases; they have not attempted to give general-purpose unifying models. In computer science, artificial intelligence and robotics researchers have long sought to formalize common-sense physical reasoning but without success in approaching human-level competence. Here we show that a wide range of human physical judgments can be explained by positing an “intuitive me
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1 Josh Tenenbaum describes some new work modeling people’s physical reasoning as probabilistic inferences over intuitive theories of mechanics. [sent-1, score-1.058]
2 A general-purpose capacity for “physical intelligence”—inferring physical properties of objects and predicting future states in complex dynamical scenes—is central to how humans interpret their environment and plan safe and effective actions. [sent-2, score-0.613]
3 The computations and representations underlying physical intelligence remain unclear, however. [sent-3, score-0.758]
4 Cognitive studies have focused on mapping out judgment biases and errors, or on testing simple heuristic models suitable only for highly specific cases; they have not attempted to give general-purpose unifying models. [sent-4, score-0.53]
5 In computer science, artificial intelligence and robotics researchers have long sought to formalize common-sense physical reasoning but without success in approaching human-level competence. [sent-5, score-1.11]
6 Here we show that a wide range of human physical judgments can be explained by positing an “intuitive mechanics”, an implicit theory in the brain that embodies abstract laws of motion surprisingly faithful to the laws of classical (Newtonian) mechanics. [sent-6, score-1.351]
7 A formal model of intuitive mechanics closely fits human judgments in a number of behavioral experiments, explaining both how people make such rich and accurate physical inferences in general and also why they show several systematic biases. [sent-8, score-1.611]
8 Here’s a short conference paper by Hamrick, Battaglia, and Tenenbaum that contains a preliminary report on some of the above work. [sent-13, score-0.079]
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Introduction: Josh Tenenbaum describes some new work modeling people’s physical reasoning as probabilistic inferences over intuitive theories of mechanics. A general-purpose capacity for “physical intelligence”—inferring physical properties of objects and predicting future states in complex dynamical scenes—is central to how humans interpret their environment and plan safe and effective actions. The computations and representations underlying physical intelligence remain unclear, however. Cognitive studies have focused on mapping out judgment biases and errors, or on testing simple heuristic models suitable only for highly specific cases; they have not attempted to give general-purpose unifying models. In computer science, artificial intelligence and robotics researchers have long sought to formalize common-sense physical reasoning but without success in approaching human-level competence. Here we show that a wide range of human physical judgments can be explained by positing an “intuitive me
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