Eye-Tracking Causality

Gerstenberg, Tobias; Peterson, Matthew; Goodman, Noah D.; Lagnado, David A.; Tenenbaum, Joshua B.

doi:10.1177/0956797617713053

Cited by 75 publications

(72 citation statements)

References 43 publications

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“…The CSM is a concrete implementation of this idea. It predicts that people use a mental model of the situation to simulate what would have happened in different counterfactual contingencies (Chater & Oaksford, 2013;Gerstenberg, Peterson, Goodman, Lagnado, & Tenenbaum, 2017;Kahneman & Tversky, 1982;Roese, 1997;Waskan, 2003). A detailed generative model of the situation allows one to express both whether a candidate cause made a difference to whether the outcome occurred as well as to how it came about (Jensen, 2019;Lewis, 2000;Woodward, 2011a).…”

Section: Structural Causal Model (Scm)mentioning

confidence: 99%

“…Vignettes are limited in that the counterfactuals need to be explicitly communicated. This way, one cannot be sure whether participants' causal judgments merely reflect demand effects, or whether they would have spontaneously sought out the relevant counterfactual information (see Gerstenberg, Peterson, et al, 2017). Our task provides a naturally graded, probabilistic sense of "whether-causation" due to the nature of the simulation mechanisms that people have available for predicting the outcomes of counterfactuals in this physical domain.…”

Section: Counterfactual Simulation Model (Csm)mentioning

confidence: 99%

“…We will talk about (1) the process by which people reach causal judgments, (2) the implications of treating objects versus events as the locus of counterfactual interventions, (3) how the Recently, we have shown that this account not only accurately predicts participants' causal judgments but that it also captures the cognitive process by which people reach these judgments. In Gerstenberg, Peterson, et al (2017), we tracked participants' eye-movements while they were watching clips similar to those of Experiment 1 (but without a brick or teleport, see Figure 2 for examples). Between participants, we varied what question participants were asked to answer about the clip.…”

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: Structural Causal Model (Scm)mentioning

confidence: 99%

Section: Counterfactual Simulation Model (Csm)mentioning

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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“…For instance, people might say that the arsonist was most causal; the lack of rain somewhat causal; and the arsonist’s birth the least causal. Although gradation was commonly assumed in many models of type causation [11], the graded nature of token causation has only more recently attracted attention [3, 4, 12–14].…”

Section: Introductionmentioning

confidence: 99%

Quantitative causal selection patterns in token causation

2019

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“…For instance, people might say that the arsonist was most causal; the lack of rain somewhat causal; and the arsonist's birth the least causal. Although gradation was commonly assumed in many models of type causation [11], the graded nature of token causation has only more recently attracted attention [3,4,[12][13][14].Despite its importance, however, our empirical understanding of gradation in token causal judgment is limited. Many studies have investigated how qualitative shifts in the parameters of causal systems (e.g.…”

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confidence: 99%

Quantitative causal selection patterns in token causation

Morris¹,

Phillips²,

Cushman³

2019

Preprint

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When many events contributed to an outcome, people consistently judge some more causal than others, based in part on the prior probabilities of those events. For instance, when a tree bursts into flames, people judge the lightning strike more of a cause than the presence of oxygen in the air-in part because oxygen is so common, and lightning strikes are so rare. These effects, which play a major role in several prominent theories of token causation, have largely been studied through qualitative manipulations of the prior probabilities. Yet, there is good reason to think that people's causal judgments are on a continuum-and relatively little is known about how these judgments vary quantitatively as the prior probabilities change. In this paper, we measure people's causal judgment across parametric manipulations of the prior probabilities of antecedent events. Our experiments replicate previous qualitative findings, and also reveal several novel patterns that are not well-described by existing theories. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. singular outcome, rather than type causation, where people judge the causes of an outcome in general).When attempting to understand why some causes stand out while others recede, a key fact is that this distinction is not dichotomous, but rather graded [10]. Rather than judge one event entirely a cause and the others entirely not a cause, people appear to judge causation on some kind of continuum. For instance, people might say that the arsonist was most causal; the lack of rain somewhat causal; and the arsonist's birth the least causal. Although gradation was commonly assumed in many models of type causation [11], the graded nature of token causation has only more recently attracted attention [3,4,[12][13][14].Despite its importance, however, our empirical understanding of gradation in token causal judgment is limited. Many studies have investigated how qualitative shifts in the parameters of causal systems (e.g. the prior probability of the antecedent events) produce qualitative shifts in causal judgment (see [3] for an overview), but fewer have mapped the quantitative relationship between those parameters and the resulting judgments. (For a notable exception, see [15][16][17]). Here, we contribute to closing this empirical gap. We present people with two basic causal systems, systematically manipulate the prior probabilities of the antecedent events in those systems, and elicit people's causal judgments. We find that, even in the most basic systems, the quantitative form of people's judgments can be complex, and is incompletely described by existing theories. This result highlights the need for future research on the quantitative form of token causal judgments. Qualitative manipulations of token causationSeveral qualitative manipulations are known to affect which events people consider causal. We do not attempt an exhaustive review; rather, we follow Icard et al. [3] and highlight certain ...

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Eye-Tracking Causality

Cited by 75 publications

References 43 publications

A counterfactual simulation model of causal judgments for physical events.

A counterfactual simulation model of causal judgments for physical events.

Quantitative causal selection patterns in token causation

Quantitative causal selection patterns in token causation

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