@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 {5065, title = { AGENT: A Benchmark for Core Psychological Reasoning}, year = {2021}, month = {07/2021}, author = {Tianmin Shu and Abhishek Bhandwaldar and Chuang Gan and Kevin A Smith and Shari Liu and Dan Gutfreund and Elizabeth S Spelke and Joshua B. Tenenbaum and Tomer D. Ullman} } @article {5077, title = {Meta-strategy learning in physical problem solving: the effect of embodied experience}, journal = {bioRxiv}, year = {2021}, month = {08/2021}, abstract = {

Embodied cognition suggests that our experience in our bodies -- including our motor experiences -- shape our cognitive and perceptual capabilities broadly. Much work has studied how differences in the physical body (either natural or manipulated) can impact peoples cognitive and perceptual capacities, but often these judgments relate directly to those body differences. Here we focus instead on how natural embodied experience affects what kinds of abstract physical problem-solving strategies people use in a virtual task. We compare how groups with different embodied experience -- children and adults with congenital limb differences versus those born with two hands -- perform on this task, and find that while there is no difference in overall accuracy or time to complete the task, the groups use different meta-strategies to come to solutions. Specifically, both children and adults born with limb differences take a longer time to think before acting, and as a result take fewer overall actions to reach solutions to physical reasoning problems. Conversely, the process of development affects the particular actions children use as they age regardless of how many hands they were born with, as well as their persistence with their current strategy. Taken together, our findings suggest that differences in embodied experience drive the acquisition of different meta-strategies for balancing acting with thinking, deciding what kinds of actions to try, and deciding how persistent to be with a current action plan.

}, author = {Kelsey Allen and Kevin A Smith and Laura Bird and Joshua B. Tenenbaum and Tamar Makin and Dorothy Cowie} } @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} } @conference {4817, title = {The fine structure of surprise in intuitive physics: when, why, and how much?}, booktitle = {Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020}, year = {2020}, url = {https://cogsci.mindmodeling.org/2020/papers/0761/index.html}, author = {Kevin A Smith and Lingjie Mei and Shunyu Yao and Jiajun Wu and Elizabeth S Spelke and Joshua B. Tenenbaum and Tomer D. Ullman}, editor = {Stephanie Denison and Michael Mack and Yang Xu and Blair C. Armstrong} } @article {4683, title = {Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning}, journal = {Proceedings of the National Academy of Sciences}, year = {2020}, month = {11/2021}, pages = {201912341}, abstract = {

Many animals, and an increasing number of artificial agents, display sophisticated capabilities to perceive and manipulate objects. But human beings remain distinctive in their capacity for flexible, creative tool use{\textemdash}using objects in new ways to act on the world, achieve a goal, or solve a problem. To study this type of general physical problem solving, we introduce the Virtual Tools game. In this game, people solve a large range of challenging physical puzzles in just a handful of attempts. We propose that the flexibility of human physical problem solving rests on an ability to imagine the effects of hypothesized actions, while the efficiency of human search arises from rich action priors which are updated via observations of the world. We instantiate these components in the {\textquotedblleft}sample, simulate, update{\textquotedblright} (SSUP) model and show that it captures human performance across 30 levels of the Virtual Tools game. More broadly, this model provides a mechanism for explaining how people condense general physical knowledge into actionable, task-specific plans to achieve flexible and efficient physical problem solving.

}, keywords = {intuitive physics, physical problem solving, tool use}, issn = {0027-8424}, doi = {10.1073/pnas.1912341117}, url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1912341117}, author = {Kelsey Allen and Kevin A Smith and Joshua B. Tenenbaum} } @proceedings {4380, title = {Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations}, year = {2019}, month = {11/2019}, address = {Vancouver, Canada}, abstract = {

From infancy, humans have expectations about how objects will move and interact. Even young children expect objects not to move through one another, teleport, or disappear. They are surprised by mismatches between physical expectations and perceptual observations, even in unfamiliar scenes with completely novel objects. A model that exhibits human-like understanding of physics should be similarly surprised, and adjust its beliefs accordingly. We propose ADEPT, a model that uses a coarse (approximate geometry) object-centric representation for dynamic 3D scene understanding. Inference integrates deep recognition networks, extended probabilistic physical simulation, and particle filtering for forming predictions and expectations across occlusion. We also present a new test set for measuring violations of physical expectations, using a range of scenarios derived from de- velopmental psychology. We systematically compare ADEPT, baseline models, and human expectations on this test set. ADEPT outperforms standard network architectures in discriminating physically implausible scenes, and often performs this discrimination at the same level as people.

}, url = {http: //physadept.csail.mit.edu/}, author = {Kevin A Smith and Lingjie Mei and Shunyu Yao and Jiajun Wu and Elizabeth S Spelke and Joshua B. Tenenbaum and Tomer D. Ullman} } @proceedings {4179, title = {Differentiable physics and stable modes for tool-use and manipulation planning}, year = {2018}, month = {06/2018}, abstract = {

We consider the problem of sequential manipulation and tool-use planning in domains that include physical interactions such as hitting and throwing. The approach integrates a Task And Motion Planning formulation with primitives that either impose stable kinematic constraints or differentiable dynamical and impulse exchange constraints at the path optimization level. We demonstrate our approach on a variety of physical puzzles that involve tool use and dynamic interactions. We then compare manipulation sequences generated by our approach to human actions on analogous tasks, suggesting future directions and illuminating current limitations.

}, author = {Marc Toussaint and Kelsey Allen and Kevin A Smith and Joshua B. Tenenbaum} } @article {4181, title = {End-to-end differentiable physics for learning and control}, year = {2018}, author = {Filipe de Avila Belbute-Peres and Kevin A Smith and Kelsey Allen and Joshua B. Tenenbaum and Zico Kolter} } @proceedings {2535, title = {Faulty Towers: A counterfactual simulation model of physical support}, year = {2017}, month = {07/2017}, abstract = {

In this paper we extend the counterfactual simulation model (CSM)\  {\textendash}\  originally\  developed\  to\  capture\  causal\  judgments about\  dynamic\  events\  (Gerstenberg,\  Goodman,\  Lagnado,\  \& Tenenbaum, 2014) {\textendash} to explain judgments of physical support. The CSM predicts that people judge physical support by men- tally\  simulating\  what\  would\  happen\  if\  the\  object\  of\  interest were removed. Two experiments test the model by asking par- ticipants to evaluate the extent to which one brick in a tower is responsible for the rest of the bricks staying on the table. The results of both experiments show a very close correspon- dence\  between\  counterfactual\  simulations\  and\  responsibility judgments. We compare three versions of the CSM which dif- fer in how they model people{\textquoteright}s uncertainty about what would have happened. Participants{\textquoteright} selections of which bricks would fall are best explained by assuming that counterfactual inter- ventions only affect some aspects while leaving the rest of the scene unchanged.

}, keywords = {causality, counterfactual, intuitive physics, mental simulation, support}, author = {Tobias Gerstenberg and Liang Zhou and Kevin A Smith and Joshua B. Tenenbaum} } @article {2531, title = {Rapid Physical Predictions from Convolutional Neural Networks}, year = {2016}, url = {http://phys.csail.mit.edu/papers/9.pdf}, author = {Filipe Peres and Kevin A Smith and Joshua B. Tenenbaum} } @article {461, title = {Abstracts of the 2014 Brains, Minds, and Machines Summer Course}, number = {024}, year = {2014}, month = {09/2014}, abstract = {

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.

}, author = {Nadav Amir and Tarek R. Besold and Raffaello Camoriano and Goker Erdogan and Thomas Flynn and Grant Gillary and Jesse Gomez and Ariel Herbert-Voss and Gladia Hotan and Jonathan Kadmon and Scott W. Linderman and Tina T. Liu and Andrew Marantan and Joseph Olson and Garrick Orchard and Dipan K. Pal and Giulia Pasquale and Honi Sanders and Carina Silberer and Kevin A Smith and Carlos Stein N. de Briton and Jordan W. Suchow and M. H. Tessler and Guillaume Viejo and Drew Walker and Leila Wehbe and Andrei Barbu and Leyla Isik and Emily Mackevicius and Yasmine Meroz} }