@article {5296, title = {Mental Jenga: A counterfactual simulation model of causal judgments about physical support}, journal = {PsyArXiv}, year = {2022}, month = {02/2022}, abstract = {

From building towers to picking an orange from a stack of fruit, assessing support is critical for successfully interacting with the physical world. But how do people determine whether one object supports another? In this paper, we develop the Counterfactual Simulation Model (CSM) of causal judgments about physical support. The CSM predicts that people judge physical support by mentally simulating what would happen to a scene if the object of interest were removed. Three experiments test the model by asking one group of participants to judge what would happen to a tower if one of the blocks were removed, and another group of participants how responsible that block was for the tower{\textquoteright}s stability. The CSM accurately captures participants{\textquoteright} predictions by running noisy simulations that incorporate different sources of uncertainty. Participants{\textquoteright} responsibility judgments are closely related to counterfactual predictions: a block is more responsible when many other blocks would fall if it were removed. By construing physical support as preventing from falling, the CSM provides a unified account of how causal judgments in dynamic and static physical scenes arise from the process of counterfactual simulation.

}, url = {https://psyarxiv.com/4a5uh}, author = {Liang Zhou and Kevin Smith and Joshua B. Tenenbaum and Tobias Gerstenberg} } @article {3514, title = {Lucky or clever? From changed expectations to attributions of responsibility}, journal = {Cognition}, year = {2018}, month = {08/2018}, author = {Tobias Gerstenberg and Ullman, Tomer D. and Nagel, Jonas and Max Kleiman-Weiner and D. A. Lagnado and Joshua B. Tenenbaum} } @proceedings {3445, title = {Causal learning from interventions and dynamics in continuous time}, year = {2017}, abstract = {

Event timing and interventions are important and intertwined cues to causal structure, yet they have typically been studied separately. We bring them together for the first time in an ex- periment where participants learn causal structure by performing interventions in continuous time. We contrast learning in acyclic and cyclic devices, with reliable and unreliable cause{\textendash} effect delays. We show that successful learners use interventions to structure and simplify their interactions with the de- vices and that we can capture judgment patterns with heuristics based on online construction and testing of a single structural hypothesis.

}, author = {Neil Bramley and Ralf Mayrhofer and Tobias Gerstenberg and D. A. Lagnado} } @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} } @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} } @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 {2536, title = {Marbles in inaction: Counterfactual simulation and causation by omission}, year = {2017}, month = {07/2017}, abstract = {

Consider\  the\  following\  causal\  explanation:\ \  The\  ball\  went through the goal because the defender didn{\textquoteright}t block it.\  There are at least two problems with citing omissions as causal ex- planations.\ \  First,\  how\  do\  we\  choose\  the\  relevant\  candidate omission (e.g. why the defender and not the goalkeeper). Sec- ond, how do we determine what would have happened in the relevant\  counterfactual\  situation\  (i.e.\ \  maybe\  the\  shot\  would still have gone through the goal even if it had been blocked). In this paper, we extend the counterfactual simulation model (CSM) of causal judgment (Gerstenberg, Goodman, Lagnado, \& Tenenbaum, 2014) to handle the second problem. In two ex- periments, we show how people{\textquoteright}s causal model of the situation affects their causal judgments via influencing what counterfac- tuals\  they\  consider.\ \  Omissions\  are\  considered\  causes\  to\  the extent that the outcome in the relevant counterfactual situation would have been different from what it actually was.

}, author = {Simon Stephan and Pascale Willemsen and Tobias Gerstenberg} } @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} } @inbook {1722, title = {Intuitive theories}, booktitle = {Oxford Handbook of Causal Reasoning}, year = {2016}, month = {02/2016}, publisher = {Oxford University Press}, organization = {Oxford University Press}, author = {Tobias Gerstenberg and Joshua B. Tenenbaum} } @proceedings {1723, title = {Natural science: Active learning in dynamic physical microworlds}, year = {2016}, publisher = {38th Annual Meeting of the Cognitive Science Society}, author = {Neil Bramley and Tobias Gerstenberg and Joshua B. Tenenbaum} } @proceedings {1724, title = {Understanding "almost": Empirical and computational studies of near misses}, year = {2016}, publisher = {38th Annual Meeting of the Cognitive Science Society}, author = {Tobias Gerstenberg and Joshua B. Tenenbaum} } @proceedings {755, title = {How, whether, why: Causal judgments as counterfactual contrasts}, year = {2015}, month = {07/22/2015}, pages = {782-787}, address = {Pasadena, CA}, issn = {978-0-9911967-2-2}, url = {https://mindmodeling.org/cogsci2015/papers/0142/index.html}, author = {Tobias Gerstenberg and Noah D. Goodman and D. A. Lagnado and Joshua B. Tenenbaum} } @proceedings {924, title = {Responsibility judgments in voting scenarios}, year = {2015}, month = {07/22/2015}, pages = {788-793}, address = {Pasadena, CA}, issn = {978-0-9911967-2-2}, url = {https://mindmodeling.org/cogsci2015/papers/0143/index.html}, author = {Tobias Gerstenberg and Joseph Y Halpern and Joshua B. Tenenbaum} } @article {449, title = {Concepts in a Probabilistic Language of Thought.}, number = {010}, year = {2014}, month = {06/2014}, abstract = {

Knowledge organizes our understanding of the world, determining what we expect given what we have already seen. Our predictive representations have two key properties: they are productive, and they are graded. Productive generalization is possible because our knowledge decomposes into concepts{\textemdash}elements of knowledge that are combined and recombined to describe particular situations. Gradedness is the observable effect of accounting for uncertainty{\textemdash}our knowledge encodes degrees of belief that lead to graded probabilistic predictions. To put this a different way, concepts form a combinatorial system that enables description of many different situations; each such situation specifies a distribution over what we expect to see in the world, given what we have seen. We may think of this system as a probabilistic language of thought (PLoT) in which representations are built from language-like composition of concepts and the content of those representations is a probability distribution on world states. The purpose of this chapter is to formalize these ideas in computational terms, to illustrate key properties of the PLoT approach with a concrete example, and to draw connections with other views of
conceptual structure.

Note: The book chapter is reprinted courtesy of The MIT Press, from the forthcoming edited collection {\textquotedblleft}The Conceptual Mind: New Directions in the Study of Concepts{\textquotedblright} edited by Eric Margolis and Stephen Laurence, print date Spring 2015.

}, keywords = {Development of Intelligence}, author = {Noah D. Goodman and Joshua B. Tenenbaum and Tobias Gerstenberg} }