Using Physical Theories to Infer Hidden Causal Structure
Thomas L. Griffiths, Elizabeth Baraff, Joshua B. Tenenbaum · eScholarship (California Digital Library) · 2004
We argue that human judgments about hidden causal structure can be explained as the operation of domain-general statistical inference over causal models constructed using domain knowledge. We present Bayesian models of causal induction in two previous experiments and a new study. Hypothetical causal models are generated by theories expressing two essential aspects of abstract knowledge about causal mechanisms: which causal relations are plausible, and what functional form they take.