scholarly journals A counterfactual simulation model of causal judgment

2020 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Noah D. Goodman ◽  
David Lagnado ◽  
Joshua Tenenbaum
Keyword(s):  
Simulation Model ◽  
Causal Judgment ◽  
Physical Domain ◽  
Heuristic Model ◽  
Physical Support ◽  
Quantitative Mapping ◽  
Causal Judgments ◽  
Counterfactual Simulation

How do people make causal judgments? We introduce the counterfactual simulation model (CSM) which predicts causal judgments 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 three experiments in which participants make causal judgments about dynamic collision events. Experiment 1 establishes a very close quantitative mapping between causal judgments and counterfactual simulations. Experiment 2 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 3 features two 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.

scholarly journals A counterfactual simulation model of causal judgments for physical events.

2021 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Noah D. Goodman ◽  
David A. Lagnado ◽  
Joshua B. Tenenbaum
Keyword(s):  
Simulation Model ◽  
Causal Judgments ◽  
Counterfactual Simulation

scholarly journals A counterfactual simulation model of causation by omission

2020 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Simon Stephan
Keyword(s):  
Simulation Model ◽  
Causal Explanation ◽  
Physical Situation ◽  
Prior Work ◽  
Causal Responsibility ◽  
Causal Judgments ◽  
Relevant Variables ◽  
Normative Expectations ◽  
Physical Interactions ◽  
Counterfactual Simulation

When do people say that an event that didn't happen was a cause? We extend the counterfactual simulation model (CSM) of causal judgment and test it in a series of three experiments that look at people's causal judgments about omissions in dynamic physical interactions. The problem of omissive causation highlights a series of sub-problems that need to be addressed in order to give an adequate causal explanation of why something happened: what are the relevant variables, what are their possible values, how are putative causal relationships evaluated, and how is the causal responsibility for an outcome attributed to multiple causes? The CSM predicts that people make causal judgments about omissions by mentally simulating what would have happened in relevant counterfactual situations. People use their intuitive understanding of physics to run these mental simulations. While prior work has argued that normative expectations affect judgments of omissive causation, we suggest a concrete mechanism of how this happens: expectations affect what counterfactuals people consider, and the more certain people are that the counterfactual outcome would have been different from what actually happened, the more causal they judge the omission to be. Our experiments show that both the structure of the physical situation as well as expectations about what will happen affect people's judgments.

scholarly journals A counterfactual simulation model of causation by omission

Cognition ◽  
2021 ◽  
Vol 216 ◽  
pp. 104842
Author(s):  
Tobias Gerstenberg ◽  
Simon Stephan
Keyword(s):  
Simulation Model ◽  
Counterfactual Simulation

scholarly journals Eye-Tracking Causality

2017 ◽  
Vol 28 (12) ◽  
pp. 1731-1744 ◽  
Author(s):  
Tobias Gerstenberg ◽  
Matthew F. Peterson ◽  
Noah D. Goodman ◽  
David A. Lagnado ◽  
Joshua B. Tenenbaum
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Direct Evidence ◽  
Critical Role ◽  
Billiard Ball ◽  
Process Theories ◽  
Causal Judgments ◽  
High Level ◽  
Counterfactual Simulation

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.

Rules of Causal Judgment

2017 ◽  
Author(s):  
José C. Perales ◽  
Andrés Catena ◽  
Antonio Cándido ◽  
Antonio Maldonado
Keyword(s):  
Real Life ◽  
Statistical Information ◽  
Causal Judgment ◽  
Psychological Processes ◽  
Relevant Evidence ◽  
Clear Winner ◽  
Causal Judgments ◽  
The Media

Our environment is rich in statistical information. Frequencies and proportions—or their visual depictions—are pervasive in the media, and frequently used to support or weaken causal statements, or to bias people’s beliefs in a given direction. The topic of this chapter is how people integrate naturally available frequencies and probabilities into judgments of the strength of the link between a candidate cause and an effect. We review studies investigating various rules that have been claimed to underlie intuitive causal judgments. Given that none of these rules has been established as a clear winner, we conclude presenting a tentative framework describing the general psychological processes operating when people select, ponder, and integrate pieces of causally-relevant evidence with the goal of meeting real-life demands.

scholarly journals Norms Affect Prospective Causal Judgments

2019 ◽  
Author(s):  
Paul Henne ◽  
Kevin O'Neill ◽  
Paul Bello ◽  
Sangeet Khemlani ◽  
Felipe De Brigard
Keyword(s):  
Selection Effect ◽  
Causal Judgment ◽  
Selection Effects ◽  
Causal Judgments

People more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Four experiments show that people more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment.

scholarly journals A Counterfactual Explanation for the Action Effect

2019 ◽  
Author(s):  
Paul Henne ◽  
Laura Niemi ◽  
Angel Pinillos ◽  
Felipe De Brigard ◽  
Joshua Knobe
Keyword(s):  
Causal Judgment ◽  
Action Effect ◽  
Original Action ◽  
Causal Judgments

People’s causal judgments are susceptible to the action effect, whereby they judge actions to be more causal than inactions. We offer a new explanation for this effect, the counterfactual explanation: people judge actions to be more causal than inactions because they are more inclined to consider the counterfactual alternatives to actions than to consider counterfactual alternatives to inactions. Experiment 1a conceptually replicates the original action effect for causal judgments. Experiment 1b confirms a novel prediction of the new explanation, the reverse action effect, in which people judge inactions to be more causal than actions in overdetermination cases. Experiment 2 directly compares the two effects in joint-causation and overdetermination scenarios and conceptually replicates them with new scenarios. Taken together, these studies provide support for the new counterfactual explanation for the action effect in causal judgment.

scholarly journals An interaction effect of norm violations on causal judgment

2021 ◽  
Author(s):  
Maureen Gill ◽  
Jonathan F. Kominsky ◽  
Thomas Icard ◽  
Joshua Knobe
Keyword(s):  
Interaction Effect ◽  
Causal Judgment ◽  
Interaction Pattern ◽  
Moral Norms ◽  
Norm Violations ◽  
Proper Functioning ◽  
Causal Judgments ◽  
Robust Evidence

Existing research has shown that norm violations influence causal judgments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interaction pattern in people's judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the situation violates norms. A study of moral norms (N = 1000) and norms of proper functioning (N = 3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed.

scholarly journals Quantitative causal selection patterns in token causation

2019 ◽  
Author(s):  
Adam Morris ◽  
Jonathan Scott Phillips ◽  
Tobias Gerstenberg ◽  
Fiery Andrews Cushman
Keyword(s):  
Good Reason ◽  
Causal Judgment ◽  
Lightning Strike ◽  
Lightning Strikes ◽  
Prior Probabilities ◽  
Causal Judgments ◽  
Causal Selection

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.

scholarly journals Certainty and Singular Causal Judgment

2021 ◽  
Author(s):  
Kevin O'Neill
Keyword(s):  
Decision Making ◽  
Causal Judgment ◽  
Metacognitive Judgments ◽  
Formal Framework ◽  
Wide Range ◽  
Causal Judgments ◽  
Causal Strength

In this paper, I map out broad aims, challenges, predictions, and implica- tions for the resulting intersection of singular causal judgment and metacognition that I (tentatively) call causal metacognition. First, I will overview research on sin- gular causal judgment, focusing on popular counterfactual theories that provide a formal framework for evaluating dependency relationships, as well as several compet- ing definitions of singular causal strength. Next, I will provide relevant background in the literature on metacognition for perception and decision-making, discussing major computational theories of metacognitive judgments. After covering the small amount of work on uncertainty in causal judgments, I will then argue that although singular causal judgments pose a particular problem for some theories of metacognition, coun- terfactual theories of singular causal judgment already provide testable predictions for confidence in causal judgments and can be extended to account for a wide range of patterns in confidence in singular causal judgments. Finally, I will summarize why we need a study of causal metacognition, and what empirical and theoretical advancements in that field might look like.

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