Literature DB >> 26522238

Sufficiency and Necessity Assumptions in Causal Structure Induction.

Ralf Mayrhofer1, Michael R Waldmann1.   

Abstract

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism (sufficiency or necessity; Experiment 1). These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally (Experiment 2) and appear to be domain-general (Experiment 3). Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors.
Copyright © 2015 Cognitive Science Society, Inc.

Entities:  

Keywords:  Bayes nets; Causal induction; Causal learning; Structure induction

Mesh:

Year:  2015        PMID: 26522238     DOI: 10.1111/cogs.12318

Source DB:  PubMed          Journal:  Cogn Sci        ISSN: 0364-0213


  3 in total

Review 1.  How to never be wrong.

Authors:  Samuel J Gershman
Journal:  Psychon Bull Rev       Date:  2019-02

2.  Successful structure learning from observational data.

Authors:  Anselm Rothe; Ben Deverett; Ralf Mayrhofer; Charles Kemp
Journal:  Cognition       Date:  2018-07-02

3.  Failures of explaining away and screening off in described versus experienced causal learning scenarios.

Authors:  Bob Rehder; Michael R Waldmann
Journal:  Mem Cognit       Date:  2017-02
  3 in total

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