%0 Journal Article %J PsyArXiv %D 2022 %T Mental Jenga: A counterfactual simulation model of causal judgments about physical support %A Liang Zhou %A Kevin Smith %A Joshua B. Tenenbaum %A Tobias Gerstenberg %X
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's stability. The CSM accurately captures participants' predictions by running noisy simulations that incorporate different sources of uncertainty. Participants' 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.
%B PsyArXiv %8 02/2022 %G eng %U https://psyarxiv.com/4a5uh %0 Journal Article %J Cognition %D 2018 %T Lucky or clever? From changed expectations to attributions of responsibility %A Tobias Gerstenberg %A Ullman, Tomer D. %A Nagel, Jonas %A Max Kleiman-Weiner %A D. A. Lagnado %A Joshua B. Tenenbaum %B Cognition %8 08/2018 %G eng %0 Conference Proceedings %B Cognitive Science Conference %D 2017 %T Causal learning from interventions and dynamics in continuous time %A Neil Bramley %A Ralf Mayrhofer %A Tobias Gerstenberg %A D. A. Lagnado %XEvent 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– 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.
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’ 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’ 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.
%B Psychological Science %V 73 %8 10/2017 %G eng %U http://journals.sagepub.com/doi/10.1177/0956797617713053 %! Psychol Sci %) First Published online October 17, 2017 %R 10.1177/0956797617713053 %0 Journal Article %J Psychological Science %D 2017 %T Eye-Tracking Causality %A Tobias Gerstenberg %A M.F. Peterson %A Noah D. Goodman %A D. A. Lagnado %A Joshua B. Tenenbaum %K causality %K counterfactuals %K eye tracking %K intuitive physics %K mental simulation %X
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’ 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’ 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.
In this paper we extend the counterfactual simulation model (CSM) – originally developed to capture causal judgments about dynamic events (Gerstenberg, Goodman, Lagnado, & Tenenbaum, 2014) – 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’s uncertainty about what would have happened. Participants’ 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.
%B Proceedings of the 39th Annual Conference of the Cognitive Science Society %8 07/2017 %G eng %0 Conference Proceedings %B Proceedings of the 39th Annual Conference of the Cognitive Science Society %D 2017 %T Marbles in inaction: Counterfactual simulation and causation by omission %A Simon Stephan %A Pascale Willemsen %A Tobias Gerstenberg %XConsider the following causal explanation: The ball went through the goal because the defender didn’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’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.
%B Proceedings of the 39th Annual Conference of the Cognitive Science Society %8 07/2017 %G eng %0 Conference Proceedings %B Proceedings of the 39th Annual Conference of the Cognitive Science Society %D 2017 %T Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints %A Ilker Yildirim %A Tobias Gerstenberg %A Basil Saeed %A Marc Toussant %A Joshua B. Tenenbaum %K intuitive physics %K logic-geometric programming %K planning %K problem solving %K scene understanding %XIn 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’ actions in the lab, and the mental simulations of participants online. To explain par- ticipants’ 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’s feasibility by taking into account the physical constraints of the scene. Our model explains participants’ actions and judgments to a high degree of quantitative accuracy.
%B Proceedings of the 39th Annual Conference of the Cognitive Science Society %8 07/2017 %G eng %0 Book Section %B Oxford Handbook of Causal Reasoning %D 2016 %T Intuitive theories %A Tobias Gerstenberg %A Joshua B. Tenenbaum %B Oxford Handbook of Causal Reasoning %I Oxford University Press %8 02/2016 %G eng %0 Conference Proceedings %B 38th Annual Meeting of the Cognitive Science Society %D 2016 %T Natural science: Active learning in dynamic physical microworlds %A Neil Bramley %A Tobias Gerstenberg %A Joshua B. Tenenbaum %B 38th Annual Meeting of the Cognitive Science Society %I 38th Annual Meeting of the Cognitive Science Society %G eng %0 Conference Proceedings %B 38th Annual Meeting of the Cognitive Science Society %D 2016 %T Understanding "almost": Empirical and computational studies of near misses %A Tobias Gerstenberg %A Joshua B. Tenenbaum %B 38th Annual Meeting of the Cognitive Science Society %I 38th Annual Meeting of the Cognitive Science Society %G eng %0 Conference Proceedings %B Annual Meeting of the Cognitive Science Society (CogSci) %D 2015 %T How, whether, why: Causal judgments as counterfactual contrasts %A Tobias Gerstenberg %A Noah D. Goodman %A D. A. Lagnado %A Joshua B. Tenenbaum %B Annual Meeting of the Cognitive Science Society (CogSci) %S Proceedings of the 37th Annual Meeting of the Cognitive Science Society %C Pasadena, CA %P 782-787 %8 07/22/2015 %G eng %U https://mindmodeling.org/cogsci2015/papers/0142/index.html %0 Conference Proceedings %B Annual Meeting of the Cognitive Science Society (CogSci) %D 2015 %T Responsibility judgments in voting scenarios %A Tobias Gerstenberg %A Joseph Y Halpern %A Joshua B. Tenenbaum %B Annual Meeting of the Cognitive Science Society (CogSci) %S Proceedings of the 37th Annual Meeting of the Cognitive Science Society %C Pasadena, CA %P 788-793 %8 07/22/2015 %G eng %U https://mindmodeling.org/cogsci2015/papers/0143/index.html %0 Generic %D 2014 %T Concepts in a Probabilistic Language of Thought. %A Noah D. Goodman %A Joshua B. Tenenbaum %A Tobias Gerstenberg %K Development of Intelligence %XKnowledge 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—elements of knowledge that are combined and recombined to describe particular situations. Gradedness is the observable effect of accounting for uncertainty—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 “The Conceptual Mind: New Directions in the Study of Concepts” edited by Eric Margolis and Stephen Laurence, print date Spring 2015.
%8 06/2014 %2http://hdl.handle.net/1721.1/100174