For causal knowledge to be worth learning, it must remain valid when that knowledge is applied. Because unknown background causes are potentially present, and may vary across the learning and application contexts, extricating the strength of a candidate cause requires an assumption regarding the decomposition of the observed outcome into the unobservable influences from the candidate and from background causes. Acquiring stable, useable causal knowledge is challenging when the search space of candidate causes is large, such that the reasoner's current set of candidates may fail to include a cause that generalizes well to an application context. We have hypothesized that an indispensable navigation device that shapes our causal representations toward useable knowledge involves the concept of causal invariance – the sameness of how a cause operates to produce an effect across contexts. Here, we tested our causal invariance hypothesis by making use of the distinct mathematical functions expressing causal invariance for two outcome-variable types: continuous and binary. Our hypothesis predicts that, given identical prior domain knowledge, intuitive causal judgments should vary in accord with the causal-invariance function for a reasoner's perceived outcome-variable type. The judgments are made as if the reasoner aspires to formulate causally invariant knowledge. Our experiments involved two cue-competition paradigms: blocking and overexpectation. Results show that adult humans tacitly use the appropriate causal-invariance functions for decomposition. Our analysis offers an explanation for the apparent elusiveness of the blocking effect and the adaptiveness of intuitive causal inference to the representation-dependent reality in the mind.
Bibliographical noteFunding Information:
Jooyong Park was supported by Seoul National University Research Grant for Humanities and Social Sciences. Preparation of this paper was in part supported by NSF Grant DBS 9121298 to Cheng. A preliminary report of Experiment 1 was presented at the 54th Annual Meeting of the Psychonomics Society. We thank Chris Carroll, Clark Glymour, Kelly Goerdert, and George Sperling for helpful discussion and Ulrike Hahn for help on the clarity of our paper. We also thank Chris Carroll for his assistance in data collection.
© 2021 The Authors
- Analytic knowledge
- Causal induction
- Causal invariance
- Causal-knowledge generalization
- Integration functions
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.