Norms Affect Prospective Causal Judgments

Henne, Paul; O’Neill, Kevin; Bello, Paul; Khemlani, Sangeet; Brigard, Felipe De

doi:10.1111/cogs.12931

Cited by 26 publications

(22 citation statements)

References 48 publications

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“…Among the many possibilities, counterfactual sampling models have had particular success [10,11,12,13,14]. These models account for known effects of probability [2,13,15,16], the presence of alternative causes [17,18], temporal recency [12,19,20], and foreseeability [21] on causal judgments, among other phenomena. Counterfactual sampling models have even been shown to predict eye movements during causal judgment [22,23] and judgments of omissive causation [24,25].…”

Section: Introductionmentioning

confidence: 99%

Modeling Confidence in Causal Judgments

O’Neill

Henne

Pearson³

et al. 2021

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The human capacity for causal judgment has long been thought to depend on an ability to consider counterfactual alternatives: the lightning strike caused the forest fire because had it not struck, the forest fire would not have ensued. To accommodate psychological effects on causal judgment, a range of recent accounts of causal judgment have proposed that people probabilistically sample counterfactual alternatives from which they compute a graded index of causal strength. While such models have had success in describing the influence of probability on causal judgments, among other effects, we show that these models make further untested predictions: probability should also influence people's metacognitive confidence in their causal judgments. In a large (N=3020) sample of participants in a causal judgment task, we found evidence that normality indeed influences people's confidence in their causal judgments and that these influences were predicted by a counterfactual sampling model. We take this result as supporting evidence for existing Bayesian accounts of causal judgment.

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Section: Introductionmentioning

confidence: 99%

Modeling Confidence in Causal Judgments

O’Neill

Henne

Pearson³

et al. 2021

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“…As we saw with the case of the lightning and the forest fire, causal judgments are sensitive to normality, among other factors (Henne et al, 2017;Henne et al, 2021b;Icard et al, 2017;Kominsky & Phillips, 2019;Knobe & Fraser, 2008;Morris et al, 2019). The causal strength explanation interprets these results as meaning that people adjust their beliefs about how causal an event is based on how normal it is: abnormal events are seen as more causal than normal ones.…”

Section: Methodsmentioning

confidence: 85%

“…A vast amount of research on causal judgment has sought to answer this question. A popular explanation is that lightning is statistically abnormal, which allows it to stand out from more normal events like the lack of rain (Gerstenberg & Icard, 2020;Hart & Honoré, 1985;Henne et al, 2017;Henne et al, 2021b;Hilton & Slugoski, 1986;Icard et al, 2017;Kahneman & Miller, 1986;Knobe & Fraser, 2008;McGrath, 2005). In addition to normality, research also indicates that people are more likely to judge events as causal when they are temporally recent (Henne et al, 2021a;Lagnado & Channon, 2008;Spellman, 1997), necessary or sufficient (Icard et al, 2017;Pearl, 2009), robust to a range of background circumstances (Gerstenberg et al, 2021;Grinfeld et al, 2020;Hitchcock, 2012;Lombrozo, 2010;Quillien, 2020;Vasilyeva et al, 2018;Woodward, 2006), intentional or agentive (Alicke et al, 2011;Kirfel & Lagnado, 2021;Lagnado & Channon, 2008), connected through a physical process (Wolff, 2007;Wolff et al, 2010), and when there are few alternate causes (Lagnado et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Confidence and gradation in causal judgment

et al. 2022

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When comparing the roles of the lightning strike and the dry climate in causing the forest fire, one might think that the lightning strike is more of a cause than the dry climate, or one might think that the lightning strike completely caused the fire while the dry conditions did not cause it at all. Psychologists and philosophers have long debated whether such causal judgments are graded; that is, whether people treat some causes as stronger than others. To address this debate, we first reanalyzed data from four recent studies. We found that causal judgments were actually multimodal: although most causal judgments made on a continuous scale were categorical, there was also some gradation. We then tested two competing explanations for this gradation: the confidence explanation, which states that people make graded causal judgments because they have varying degrees of belief in causal relations, and the strength explanation, which states that people make graded causal judgments because they believe that causation itself is graded. Experiment 1 tested the confidence explanation and showed that gradation in causal judgments was indeed moderated by confidence: people tended to make graded causal judgments when they were unconfident, but they tended to make more categorical causal judgments when they were confident. Experiment 2 tested the causal strength explanation and showed that although confidence still explained variation in causal judgments, it did not explain away the effects of normality, causal structure, or the number of candidate causes. Overall, we found that causal judgments were multimodal and that people make graded judgments both when they think a cause is weak and when they are uncertain about its causal role.

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“…With growing evidence that counterfactual thinking explains other scenarios that motivate causal pluralism-for instance, in the case of omission (Henne et al, 2019) and latepreemption (Henne, Kulesza, et al, 2021)-we think that it is time to return to thinking more carefully about unified counterfactual models of causal judgment and how they might explain patterns of causal judgment. We hope future work investigates these issues using different causal measures (Rose et al, 2021), measures of confidence in causal judgment (O'Neill et al, 2022), and prospective scenarios (Byrne, 2016;Henne, O'Neill, et al, 2021). We also hope future work will explore whether this account can explain past work on intentionality in double-prevention cases (Lombrozo, 2010).…”

Section: Discussionmentioning

confidence: 97%

Double Prevention, Causal Judgments, and Counterfactuals

Henne¹,

O’Neill²

2022

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Mike accidentally knocked against a bottle. Seeing that the bottle was about to fall, Jack was just about to catch it when Peter accidentally knocked against him, making Jack unable to catch it. Jack did not grab the bottle, and it fell to the ground and spilled. In double-prevention cases like these, philosophers and non-philosophers alike tend to judge that Mike knocking into the bottle caused the beer to spill and that Peter knocking into Jack did not cause the beer to spill. This difference in causal judgment is a difficult puzzle for counterfactual theories of causal judgment; if each event had not happened, the outcome would not have, yet there is a difference in people’s causal judgments. In four experiments and three supplemental experiments, we confirm this difference in causal judgments. We also show that differences in people’s counterfactual thinking can explain this difference in their causal judgments and that recent counterfactual models of causal judgment can account for these patterns. We discuss these results in relation to work on counterfactual thinking and causal modeling.

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Norms Affect Prospective Causal Judgments

Cited by 26 publications

References 48 publications

Modeling Confidence in Causal Judgments

Modeling Confidence in Causal Judgments

Confidence and gradation in causal judgment

Double Prevention, Causal Judgments, and Counterfactuals

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