@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 {4261, title = {Draping an Elephant: Uncovering Children{\textquoteright}s Reasoning About Cloth-Covered Objects}, year = {2019}, month = {07/2019}, address = {Montreal, Canada}, abstract = {

Humans have an intuitive understanding of physics. They can predict how a physical scene will unfold, and reason about how it came to be. Adults may rely on such a physical representation for visual reasoning and recognition, going beyond visual features and capturing objects in terms of their physical properties. Recently, the use of draped objects in recognition was used to examine adult object representations in the absence of many common visual features. In this paper we examine young children{\textquoteright}s reasoning about draped objects in order to examine the develop of physical object representation. In addition, we argue that a better understanding of the development of the concept of cloth as a physical entity is worthwhile in and of itself, as it may form a basic ontological category in intuitive physical reasoning akin to liquids and solids. We use two experiments to investigate young children{\textquoteright}s (ages 3{\textendash}5) reasoning about cloth-covered objects, and find that they perform significantly above chance (though far from perfectly) indicating a representation of physical objects that can interact dynamically with the world. Children{\textquoteright}s success and failure pattern is similar across the two experiments, and we compare it to adult behavior. We find a small effect, which suggests the specific features that make reasoning about certain objects more difficult may carry into adulthood.

}, keywords = {analysis-by-synthesis, cloth, cognitive development, imagination, intuitive physics, object recognition, occlusion, perception, vision}, url = {https://mindmodeling.org/cogsci2019/papers/0506/index.html}, author = {Tomer D Ullman and Eliza Kosoy and Ilker Yildirim and Amir Arsalan Soltani and Max Siegel and Joshua B. Tenenbaum and Elizabeth S Spelke} } @article {3513, title = {Learning physical parameters from dynamic scenes.}, journal = {Cognitive Psychology}, volume = {104}, year = {2018}, month = {8/2018}, pages = {57-82}, abstract = {

Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum et al., 2011), we work with more ex- pressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultane- ously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.

}, keywords = {intuitive physics, intuitive theory, learning, physical reasoning, probabilistic inference}, doi = {10.1016/j.cogpsych.2017.05.006}, url = {https://www-sciencedirect-com.libproxy.mit.edu/science/article/pii/S0010028517301822}, author = {Ullman, Tomer D. and Stuhlm{\"u}ller, Andreas and Noah D. Goodman and Joshua B. Tenenbaum} } @article {3444, title = {Eye-Tracking Causality}, journal = {Psychological Science}, year = {2017}, abstract = {

How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants{\textquoteright} eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants{\textquoteright} looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.

}, keywords = {causality, counterfactuals, eye tracking, intuitive physics, mental simulation}, author = {Tobias Gerstenberg and M.F. Peterson and Noah D. Goodman and D. A. Lagnado and Joshua B. Tenenbaum} } @article {3088, title = {Eye-Tracking Causality}, journal = {Psychological Science}, volume = {73}, year = {2017}, month = {10/2017}, abstract = {

How do people make causal judgments? What role, if any, does counterfactual simulation play? Counterfactual theories of causal judgments predict that people compare what actually happened with what would have happened if the candidate cause had been absent. Process theories predict that people focus only on what actually happened, to assess the mechanism linking candidate cause and outcome. We tracked participants{\textquoteright} eye movements while they judged whether one billiard ball caused another one to go through a gate or prevented it from going through. Both participants{\textquoteright} looking patterns and their judgments demonstrated that counterfactual simulation played a critical role. Participants simulated where the target ball would have gone if the candidate cause had been removed from the scene. The more certain participants were that the outcome would have been different, the stronger the causal judgments. These results provide the first direct evidence for spontaneous counterfactual simulation in an important domain of high-level cognition.

}, keywords = {causality, counterfactuals, eye tracking, intuitive physics, mental simulation, open data, open materials}, issn = {0956-7976}, doi = {10.1177/0956797617713053}, url = {http://journals.sagepub.com/doi/10.1177/0956797617713053}, author = {Tobias Gerstenberg and M.F. Peterson and Noah D. Goodman and D. A. Lagnado and Joshua B. Tenenbaum} } @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} } @proceedings {2537, title = {Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints}, year = {2017}, month = {07/2017}, abstract = {

In this paper, we present a new task that investigates how peo- ple interact with and make judgments about towers of blocks. In Experiment 1, participants in the lab solved a series of prob- lems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so.\  In Experiment 2, we asked participants online to judge whether they think the person in the lab used one or two hands.\ \  The results revealed a close correspondence between participants{\textquoteright} actions in the lab,\  and the mental simulations of participants online.\  To explain par- ticipants{\textquoteright} actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, exe- cutes the plan using a geometric solver, and checks the plan{\textquoteright}s feasibility by taking into account the physical constraints of the scene. Our model explains participants{\textquoteright} actions and judgments to a high degree of quantitative accuracy.

}, keywords = {intuitive physics, logic-geometric programming, planning, problem solving, scene understanding}, author = {Ilker Yildirim and Tobias Gerstenberg and Basil Saeed and Marc Toussant and Joshua B. Tenenbaum} }