Norm conflicts and conditionals.

Skovgaard-Olsen, Niels; Kellen, David; Hahn, Ulrike; Klauer, Karl Christoph

doi:10.1037/rev0000150

Cited by 26 publications

(33 citation statements)

References 167 publications

(249 reference statements)

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“…Previously, this effect has only been reported using verbal scenarios (Skovgaard-Olsen, Singmann, et al, 2016;Skovgaard-Olsen, Kellen, et al, 2019;Vidal & Baratgin, 2017), which was replicated in Experiment 1. Now we show that this Relevance Effect can also be found in a trial-by-trial learning paradigm in the presence of mechanistic knowledge for the first time.…”

Section: Resultssupporting

confidence: 55%

Conditionals and the hierarchy of causal queries.

Skovgaard-Olsen¹,

Stephan²,

Waldmann³

2021

Journal of Experimental Psychology: General

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Recent studies indicate that indicative conditionals like "If people wear masks, the spread of Covid-19 will be diminished" require a probabilistic dependency between their antecedents and consequents to be acceptable (Skovgaard-Olsen et al., 2016). But it is easy to make the slip from this claim to the thesis that indicative conditionals are acceptable only if this probabilistic dependency results from a causal relation between antecedent and consequent.According to Pearl ( 2009), understanding a causal relation involves multiple, hierarchically organized conceptual dimensions: prediction, intervention, and counterfactual dependence. In a series of experiments, we test the hypothesis that these conceptual dimensions are differentially encoded in indicative and counterfactual conditionals. If this hypothesis holds, then there are limits as to how much of a causal relation is captured by indicative conditionals alone. Our results show that the acceptance of indicative and counterfactual conditionals can become dissociated. Furthermore, it is found that the acceptance of both is needed for accepting a causal relation between two co-occurring events. The implications that these findings have for the hypothesis above, and for recent debates at the intersection of the psychology of reasoning and causal judgment, are critically discussed. Our findings are consistent with viewing indicative conditionals as answering predictive queries requiring evidential relevance (even in the absence of direct causal relations). Counterfactual conditionals in contrast target causal relevance, specifically. Finally, we discuss the implications our results have for the yet unsolved question of how reasoners succeed in constructing causal models from verbal descriptions.

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Section: Resultssupporting

confidence: 55%

Conditionals and the hierarchy of causal queries.

Skovgaard-Olsen¹,

Stephan²,

Waldmann³

2021

Journal of Experimental Psychology: General

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“…The majority of studies on biases in human reasoning compare judgments with how normative accounts, such as probability theory or logic, would represent the tasks at hand (see Newell et al, 2015, for an overview). In some cases, multiple normative accounts compete so that one goal for the psychologist is to coordinate participants' understanding of the task with the most adequate normative account (see Skovgaard-Olsen et al, 2019, for an analysis of this process). In contrast to cases in which probability theory or logic are taken as the normative standard, the bias we discovered is based on an assessment of whether observed patterns of belief revision are consistent with objective causal features of the world, which is a topic of philosophical theories of causation and scientific theories.…”

Section: The Weakening Bias and Causal Stabilitymentioning

confidence: 99%

Interpolating causal mechanisms: The paradox of knowing more.

Stephan

Tentori

Pighin

et al. 2021

Journal of Experimental Psychology: General

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Causal knowledge is not static; it is constantly modified based on new evidence. The present set of seven experiments explores 1 important case of causal belief revision that has been neglected in research so far: causal interpolations. A simple prototypic case of an interpolation is a situation in which we initially have knowledge about a causal relation or a positive covariation between 2 variables but later become interested in the mechanism linking these 2 variables. Our key finding is that the interpolation of mechanism variables tends to be misrepresented, which leads to the paradox of knowing more: The more people know about a mechanism, the weaker they tend to find the probabilistic relation between the 2 variables (i.e., weakening effect). Indeed, in all our experiments we found that, despite identical learning data about 2 variables, the probability linking the 2 variables was judged higher when follow-up research showed that the 2 variables were assumed to be directly causally linked (i.e., C¡E) than when participants were instructed that the causal relation is in fact mediated by a variable representing a component of the mechanism (M; i.e., C¡M¡E). Our explanation of the weakening effect is that people often confuse discoveries of preexisting but unknown mechanisms with situations in which new variables are being added to a previously simpler causal model, thus violating causal stability assumptions in natural kind domains. The experiments test several implications of this hypothesis.

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“…Another reaction has been to call for the adoption of extended or alternative normative frameworks (e.g., Buchak, 2013;Busemeyer & Bruza, 2012;Dzhafarov & Kujala, 2016;Oaksford & Chater, 2007;Skovgaard-Olsen, Kellen, Hahn, & Klauer, 2019;Spohn, 2012).…”

Section: Normative Accounts Of Illusory Correlationsmentioning

confidence: 99%

Normative accounts of illusory correlations.

Bott¹,

Kellen²,

Klauer³

2021

Psychological Review

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When learning about the joint occurrence of different variables, individuals often manifest biases in the associations they infer. In some cases they infer an association when none is present in the observed sample. Other times they infer an association that is contrary to the one that is in fact observed. These illusory correlations are often interpreted as being the byproduct of selective processing or as the outcome of an 'illogical' pseudocontingency heuristic. More recently, a normative account of illusory correlations has been proposed, according to which they result from an application of Laplace's Rule of Succession. The present work will discuss the empirical and theoretical limitations associated with this normative account, and argue for its dismissal. As an alternative, we propose a normative account that casts illusory correlations as the expected outcome of a Bayesian reasoner relying on marginal frequencies. We show that this account succeeds in capturing the qualitative patterns found in a corpus of published studies.

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Norm conflicts and conditionals.

Cited by 26 publications

References 167 publications

Conditionals and the hierarchy of causal queries.

Conditionals and the hierarchy of causal queries.

Interpolating causal mechanisms: The paradox of knowing more.

Normative accounts of illusory correlations.

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