11 research outputs found

    Inferring mass in complex physical scenes via probabilistic simulation

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    <p>How do people learn underlying properties, such as mass and friction, from objects’ interactions in complex scenes? Such inferences are difficult: the parameters cannot be directly observed and have nonlinear effects on the physical dynamics. Yet, people learn them. Participants predicted the stability of blocks stacked in complex tower configurations. After observing the true outcome, they answered, “which blocks are heavier?”. Their responses indicate rapid learning of the blocks’ relative masses. We view such learning as probabilistic inference in a generative model of Newtonian rigid-body dynamics, and express this hypothesis in a model observer that infers parameters using a procedure of approximate physical simulation. While participants’ judgments qualitatively matched the model’s, they also deviated in key ways that may be explained by resource limitations. This work advances our understanding of how people infer unobserved physical properties, and offers a framework for modeling such behavior in complex, real-world scenes.</p

    Think again? The amount of mental simulation tracks uncertainty in the outcome

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    <p>We investigate how people use mental simulations: do people vary the number of simulations that they run in order to optimally balance speed and accuracy? We combined a model of noisy physical simulation with a decision making strategy called the sequential probability ratio test, or S P RT (Wald, 1947). Our model predicted that people should use more samples when it is harder to make an accurate prediction due to higher simulation uncertainty. We tested this through a task in which people had to judge whether a ball bouncing in a box would go through a hole or not. We varied the uncertainty across trials by changing the size of the holes and the margin by which the ball went through or missed the hole. Both people’s judgments and response times were well-predicted by our model, demonstrating that people have a systematic strategy to allocate resources for mental simulation.</p

    Additional file 1: Table S1. of Charting health system reconstruction in post-war Liberia: a comparison of rural vs. remote healthcare utilization

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    Receipt of Maternal and Child Health Services (Adjusted): Percent of the population receiving maternal and child health services in the rural subsection of DHS 2007, DHS 2013 and the Konobo survey, with 95 % confidence intervals. (DOCX 96 kb
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