TY - JOUR
T1 - Inferring mass in complex scenes by mental simulation
AU - Hamrick, Jessica B.
AU - Battaglia, Peter W.
AU - Griffiths, Thomas L.
AU - Tenenbaum, Joshua B.
N1 - Funding Information:
This work was supported by a Berkeley Fellowship and NSF Graduate Fellowship awarded to J.B. Hamrick, as well as Grant No. N00014-13-1-0341 from the Office of Naval Research . We would also like to thank Michael Pacer and Tobias Pfaff for helpful discussions and feedback.
Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - After observing a collision between two boxes, you can immediately tell which is empty and which is full of books based on how the boxes moved. People form rich perceptions about the physical properties of objects from their interactions, an ability that plays a crucial role in learning about the physical world through our experiences. Here, we present three experiments that demonstrate people's capacity to reason about the relative masses of objects in naturalistic 3D scenes. We find that people make accurate inferences, and that they continue to fine-tune their beliefs over time. To explain our results, we propose a cognitive model that combines Bayesian inference with approximate knowledge of Newtonian physics by estimating probabilities from noisy physical simulations. We find that this model accurately predicts judgments from our experiments, suggesting that the same simulation mechanism underlies both peoples’ predictions and inferences about the physical world around them.
AB - After observing a collision between two boxes, you can immediately tell which is empty and which is full of books based on how the boxes moved. People form rich perceptions about the physical properties of objects from their interactions, an ability that plays a crucial role in learning about the physical world through our experiences. Here, we present three experiments that demonstrate people's capacity to reason about the relative masses of objects in naturalistic 3D scenes. We find that people make accurate inferences, and that they continue to fine-tune their beliefs over time. To explain our results, we propose a cognitive model that combines Bayesian inference with approximate knowledge of Newtonian physics by estimating probabilities from noisy physical simulations. We find that this model accurately predicts judgments from our experiments, suggesting that the same simulation mechanism underlies both peoples’ predictions and inferences about the physical world around them.
KW - Inference
KW - Learning
KW - Mental simulation
KW - Physical reasoning
KW - Probabilistic simulation
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U2 - 10.1016/j.cognition.2016.08.012
DO - 10.1016/j.cognition.2016.08.012
M3 - Article
C2 - 27592412
AN - SCOPUS:84984808402
SN - 0010-0277
VL - 157
SP - 61
EP - 76
JO - Cognition
JF - Cognition
ER -