@article {5084, title = {An approximate representation of objects underlies physical reasoning}, journal = {psyArXiv}, year = {2022}, month = {03/2022}, abstract = {

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may be using principled approximations, similar to models developed by engineers for the purposes of real-time physical simulations. We hypothesize that people use simplified object approximations for tracking and action (the "body" representation), as opposed to fine-grained forms for recognition (the "shape" representation). We used three classic psychophysical tasks (causality perception, collision detection, and change detection) in novel settings that dissociate body and shape. People{\textquoteright}s behavior across tasks indicates that they rely on approximate bodies for physical reasoning, and that this approximation lies between convex hulls and fine-grained shapes.

}, url = {https://psyarxiv.com/vebu5/}, author = {Yichen Li and YingQiao Wang and Tal Boger and Kevin A Smith and Samuel J Gershman and Tomer Ullman} } @article {5080, title = {Partial Mental Simulation Explains Fallacies in Physical Reasoning}, journal = {psyArXiv}, year = {2021}, month = {11/2021}, abstract = {

People can reason intuitively, efficiently, and accurately about everyday physical events. Recent accounts suggest that people use mental simulation to make such intuitive physical judgments. But mental simulation models are computationally expensive; how is physical reasoning relatively accurate, while maintaining computational tractability? We suggest that people make use of partial simulation, mentally moving forward in time only parts of the world deemed relevant. We propose a novel partial simulation model, and test it on the physical conjunction fallacy, a recently observed phenomenon (Ludwin-Peery, Bramley, Davis, \& Gureckis, 2020) that poses a challenge for full simulation models. We find an excellent fit between our model{\textquoteright}s predictions and human performance on a set of scenarios that build on and extend those used by Ludwin-Peery et al. (2020), quantitatively and qualitatively accounting for a deviation from optimal performance. Our results suggest more generally how we allocate cognitive resources to efficiently represent and simulate physical scenes.

}, url = {https://psyarxiv.com/y4a8x}, author = {Illona Bass and Kevin A Smith and Elizabeth Bonawitz and Tomer Ullman} } @conference {4820, title = {Unsupervised Discovery of 3D Physical Objects}, booktitle = {International Conference on Learning Representations}, year = {2021}, month = {07/2020}, abstract = {

We study the problem of unsupervised physical object discovery. Unlike existing frameworks that aim to learn to decompose scenes into 2D segments purely based on each object{\textquoteright}s appearance, we explore how physics, especially object interactions,facilitates learning to disentangle and segment instances from raw videos, and to infer the 3D geometry and position of each object, all without supervision. Drawing inspiration from developmental psychology, our Physical Object Discovery Network (POD-Net) uses both multi-scale pixel cues and physical motion cues to accurately segment observable and partially occluded objects of varying sizes, and infer properties of those objects. Our model reliably segments objects on both synthetic and real scenes. The discovered object properties can also be used to reason about physical events.

}, url = {https://openreview.net/forum?id=lf7st0bJIA5}, author = {Yilun Du and Kevin A Smith and Tomer Ullman and Joshua B. Tenenbaum and Jiajun Wu} } @proceedings {2605, title = {Critical Cues in Early Physical Reasoning}, year = {2017}, address = {Austin, TX}, author = {Tomer Ullman and Joshua B. Tenenbaum and Elizabeth S Spelke} } @article {2604, title = {Ten-month-old infants infer value from effort}, year = {2017}, address = {Austin, TX}, author = {Shari Liu and Tomer Ullman and Joshua B. Tenenbaum and Elizabeth S Spelke} } @article {2636, title = {Ten-month-old infants infer value from effort}, year = {2017}, author = {Shari Liu and Tomer Ullman and Joshua B. Tenenbaum and Elizabeth S Spelke} } @article {1984, title = {Building machines that learn and think like people}, year = {2016}, month = {04/2016}, abstract = {

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

}, author = {Brenden M Lake and Tomer Ullman and Joshua B. Tenenbaum and Samuel J Gershman} } @conference {1962, title = {Effort as a bridging concept across action and action understanding: Weight and Physical Effort in Predictions of Efficiency in Other Agents}, booktitle = {International Conference on Infant Studies (ICIS) }, year = {2016}, month = {05/2016}, address = {New Orleans, Louisiana}, author = {Tomer Ullman and Joshua B. Tenenbaum and Elizabeth S Spelke} }