Premium
Agents and Causes: Dispositional Intuitions As a Guide to Causal Structure
Author(s) -
Mayrhofer Ralf,
Waldmann Michael R.
Publication year - 2015
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12132
Subject(s) - causation , causality (physics) , causal model , attribution , psychology , markov chain , dependency (uml) , causal structure , causal reasoning , cognitive psychology , bayes' theorem , econometrics , computer science , cognition , social psychology , artificial intelligence , mathematics , machine learning , statistics , bayesian probability , epistemology , philosophy , physics , quantum mechanics , neuroscience
Abstract Currently, two frameworks of causal reasoning compete: Whereas dependency theories focus on dependencies between causes and effects, dispositional theories model causation as an interaction between agents and patients endowed with intrinsic dispositions. One important finding providing a bridge between these two frameworks is that failures of causes to generate their effects tend to be differentially attributed to agents and patients regardless of their location on either the cause or the effect side. To model different types of error attribution, we augmented a causal B ayes net model with separate error sources for causes and effects. In several experiments, we tested this new model using the size of M arkov violations as the empirical indicator of differential assumptions about the sources of error. As predicted by the model, the size of M arkov violations was influenced by the location of the agents and was moderated by the causal structure and the type of causal variables.