2019
DOI: 10.31219/osf.io/2nwb4
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Norms Affect Prospective Causal Judgments

Abstract: 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 only retrospective vignette-based paradigms. In within-participants designs, we use a novel set of videos to investigate this effect for prospective causal judgments—i.e., judgments about the cause of some future outcome. Three experiments show that people more frequently select norm-violating factors, relative to norm-confo… Show more

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Cited by 2 publications
(3 citation statements)
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“…The extension of a recent counterfactual model of causal strength-i.e., the degree to which a factor is regarded as the cause of an outcome-however, shows promise for explaining and unifying the recency and primacy effects. This recent computational model, which we will refer to as the necessitysufficiency model (Icard et al, 2017), has been used to explain the effects of norms on causal judgments (Henne et al, 2021;Henne et al, 2017;Hitchcock & Knobe, 2009;Kominsky et al, 2015;Morris et al, RECENCY EFFECTS IN CAUSAL JUDGMENT 2019;Samland et al, 2016;Willemsen & Kirfel, 2018). Uniquely, this model's assumptions allow it to predict different patterns of causal judgments for different causal structures (see General Discussion for details).…”
Section: A Counterfactual Accountmentioning
confidence: 99%
“…The extension of a recent counterfactual model of causal strength-i.e., the degree to which a factor is regarded as the cause of an outcome-however, shows promise for explaining and unifying the recency and primacy effects. This recent computational model, which we will refer to as the necessitysufficiency model (Icard et al, 2017), has been used to explain the effects of norms on causal judgments (Henne et al, 2021;Henne et al, 2017;Hitchcock & Knobe, 2009;Kominsky et al, 2015;Morris et al, RECENCY EFFECTS IN CAUSAL JUDGMENT 2019;Samland et al, 2016;Willemsen & Kirfel, 2018). Uniquely, this model's assumptions allow it to predict different patterns of causal judgments for different causal structures (see General Discussion for details).…”
Section: A Counterfactual Accountmentioning
confidence: 99%
“…The culpable control model is able to explain, among other effects, that people view a norm-violating agent as a greater cause of an effect than a norm-compliant agent when the effect is negative, but the reverse is true when the effect is positive (Alicke & Rose, 2012;Alicke et al, 2011). However, this model does not predict causal judgments of inanimate objects without appealing to anthropomorphism of simple objects (Henne et al, 2021;Kominsky & Phillips, 2019). More importantly, the culpable control model does not readily provide any explanation of certainty in causal judgment.…”
Section: Social Cognitive Theoriesmentioning
confidence: 99%
“…It should not be terribly surprising that the Lewis-inspired account does not suffice as a psychological account of certainty in causal judgment; after all, Lewis (1974) sought to describe what causation is, not how we reason causally. But recent extensions of the general idea that counterfactuals underlie causal judgment have gained much popularity for their ability to explain how causal judgments are sensitive to dependence between events (Cheng & Novick, 1990;Davis & Rehder, 2020;Pearl, 2009), normality (Gerstenberg & Icard, 2020;Henne et al, 2021;Hitchcock & Knobe, 2009;Icard et al, 2017;Kirfel & Lagnado, 2019;Knobe & Fraser, 2008;Kominsky & Phillips, 2019;Sytsma, 2019), whether the events are actions or omissions (Henne, Bello, et al, 2019;Henne, Niemi, et al, 2019;Henne et al, 2017), the presence of alternative causes (Kominsky et al, 2015;Lu et al, 2008;Morris et al, 2019;O'Neill et al, 2021), and the perceived effectiveness of an intervention on the cause (Kushnir & Gopnik, 2005;Lagnado & Sloman, 2004;Morris et al, 2018;Sobel & Kushnir, 2006;Woodward, 2003) among other factors.…”
Section: Counterfactual Sampling and Probabilitymentioning
confidence: 99%