Expectations affect physical causation judgments.

Icard, Thomas

doi:10.1037/xge0000670

Cited by 42 publications

(61 citation statements)

References 59 publications

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“…For example, if participants learn that one ball generally prevents another from going through the gate, while another ball generally tends to make it go through, one could expect asymmetric judgments in a situation in which both balls jointly caused another one to go through the gate. It will also be interesting to see whether the effects of normality that have thus far been demonstrated in vignette studies will generalize to the domain con-sidered here (see Gerstenberg & Icard, 2019;Kirfel et al, in prep, for evidence that this may be the case).…”

Section: Limitations and Open Challengesmentioning

confidence: 85%

“…For example, people cite the striking of a match rather than the presence of oxygen as having caused a forest fire. Both event normality (Hitchcock & Knobe, 2009;Kahneman & Miller, 1986;Kahneman & Tversky, 1982) and the causal structure of the situation have been shown to influence people's causal selections (Gerstenberg & Icard, 2019;Icard, Kominsky, & Knobe, 2017;Kirfel, Icard, & Gerstenberg, in prep;Kominsky, Phillips, Gerstenberg, Lagnado, & Knobe, 2015). In our experiments, we explicitly ask participants about the candidate causes, so the general problem of causal selection doesn't arise.…”

Section: Scope Of the Modelmentioning

confidence: 99%

“…Recently, it has been proposed that causal judgments further play the role of highlighting those aspects that not only made a difference in the actual situation, but that are also likely to continue to make a difference in other situations, too (Danks, 2013;Hitchcock, 2012;Lombrozo, 2010;Nagel & Stephan, 2016). A causal judgment now, may help to pinpoint a useful place for intervention in the future (Bramley, Gerstenberg, & Tenenbaum, 2016;Bramley, Mayrhofer, Gerstenberg, & Lagnado, 2017;Gerstenberg & Icard, 2019;Girotto, Legrenzi, & Rizzo, 1991;Meder, Gerstenberg, Hagmayer, & Waldmann, 2010).…”

Section: Limitations and Open Challengesmentioning

confidence: 99%

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A counterfactual simulation model of causal judgments for physical events.

Goodman

Lagnado

Tenenbaum

2021

Psychological Review

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How do people make causal judgments about physical events? We introduce the counterfactual simulation model (CSM) which predicts causal judgments in physical settings by comparing what actually happened with what would have happened in relevant counterfactual situations. The CSM postulates different aspects of causation that capture the extent to which a cause made a difference to whether and how the outcome occurred, and whether the cause was sufficient and robust. We test the CSM in several experiments in which participants make causal judgments about dynamic collision events. A preliminary study establishes a very close quantitative mapping between causal and counterfactual judgments. Experiment 1 demonstrates that counterfactuals are necessary for explaining causal judgments. Participants' judgments differed dramatically between pairs of situations in which what actually happened was identical, but where what would have happened differed. Experiment 2 features multiple candidate causes and shows that participants' judgments are sensitive to different aspects of causation. The CSM provides a better fit to participants' judgments than a heuristic model which uses features based on what actually happened. We discuss how the CSM can be used to model the semantics of different causal verbs, how it captures related concepts such as physical support, and how its predictions extend beyond the physical domain.

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Section: Limitations and Open Challengesmentioning

confidence: 85%

Section: Scope Of the Modelmentioning

confidence: 99%

Section: Limitations and Open Challengesmentioning

confidence: 99%

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A counterfactual simulation model of causal judgments for physical events.

Goodman

Lagnado

Tenenbaum

2021

Psychological Review

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“…Given the high reliability of choices on this task, particularly for adults (r = .95, children r = .61), we tested how well heuristic features of the physical interactions could predict these choices. In light of findings that people often single out one of several possible causes as "the" cause in judgements of physical causation (e.g., Gerstenberg & Icard, 2020), we might expect to find single features that are predictive of people's choices. We explored a wide range of features that we thought might be proportional to people's interest in particular object interactions (e.g., how far a dropped object might end up from the drop location), but not one of these features explained much of the trial-level variance in people's choices.…”

Section: Discussionmentioning

confidence: 99%

Predicting children’s and adults’ preferences in physical interactions via physics simulation

Kachergis¹,

Radwan²,

Long³

et al. 2021

Preprint

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Curiosity is a fundamental driver of human behavior, and yet because of its open-ended nature and the wide variety of behaviors it inspires in different contexts, it is remarkably difficult to study in a laboratory context. A promising approach to developing and testing theories of curiosity is to instantiate them in artificial agents that are able to act and explore in a simulated environment, and then compare the behavior of these agents to humans exploring the same stimuli. Here we propose a new experimental paradigm for examining children’s – and AI agents’ – curiosity about objects’ physical interactions. We let them choose which object to drop another object onto in order to create the most interesting effect. We compared adults’ (N=155) and children’s choices (N=66; 3-7 year-olds) and found that both children and adults show a strong preference for choosing target objects that could potentially contain the dropped object. Adults alone also make choices consistent with achieving support relations. We contextualize our results using heuristic computational models based on 3D physical simulations of the same scenarios judged by participants.

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“…It is also difficult to manipulate the relevant factors in precise quantitative ways in vignettes (cf. Gerstenberg & Icard, 2019). The limitations of this method constrains the kinds of theories that can be tested.…”

Section: Introductionmentioning

confidence: 99%

Moral Dynamics: Grounding Moral Judgment in Intuitive Physics and Intuitive Psychology

Sosa¹,

Ullman²,

Tenenbaum³

et al. 2021

Preprint

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When holding others morally responsible, we care about what they did, and what they thought. Traditionally, research in moral psychology has relied on vignette studies, in which a protagonist's actions and thoughts are explicitly communicated. While this research has revealed what variables are important for moral judgment, such as actions and intentions, it is limited in providing a more detailed understanding of exactly how these variables affect moral judgment. Using dynamic visual stimuli that allow for a more fine-grained experimental control, recent studies have proposed a direct mapping from visual features to moral judgments. We embrace the use of visual stimuli in moral psychology, but question the plausibility of a feature-based theory of moral judgment. We propose that the connection from visual features to moral judgments is mediated by an inference about what the observed action reveals about the agent's mental states, and what causal role the agent's action played in bringing about the outcome. We present a computational model that formalizes moral judgments of agents in visual scenes as computations over an intuitive theory of physics combined with an intuitive theory of mind. We test the model's quantitative predictions in three experiments across a wide variety of dynamic interactions between agent and patient.

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Expectations affect physical causation judgments.

Cited by 42 publications

References 59 publications

A counterfactual simulation model of causal judgments for physical events.

A counterfactual simulation model of causal judgments for physical events.

Predicting children’s and adults’ preferences in physical interactions via physics simulation

Moral Dynamics: Grounding Moral Judgment in Intuitive Physics and Intuitive Psychology

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