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. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Interpolating causal mechanisms: the paradox of knowing more / Stephan, Simon; Tentori, Katya; Pighin, Stefania; Waldmann, Michael R. - In: JOURNAL OF EXPERIMENTAL PSYCHOLOGY. GENERAL. - ISSN 0096-3445. - 150:8(2021), pp. 1500-1527. [10.1037/xge0001016]

Interpolating causal mechanisms: the paradox of knowing more

Tentori, Katya;Pighin, Stefania;
2021-01-01

Abstract

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. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
2021
8
Stephan, Simon; Tentori, Katya; Pighin, Stefania; Waldmann, Michael R
Interpolating causal mechanisms: the paradox of knowing more / Stephan, Simon; Tentori, Katya; Pighin, Stefania; Waldmann, Michael R. - In: JOURNAL OF EXPERIMENTAL PSYCHOLOGY. GENERAL. - ISSN 0096-3445. - 150:8(2021), pp. 1500-1527. [10.1037/xge0001016]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/291293
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