Building Object-based Causal Programs for Human-like Generalization

Our framework integrates a symbolic approach to represent causal law generation, with non-parametric Bayesian categorization to model latent categories, emphasizing the constructive nature of causal belief formation, in which both the content and extension of our causal concepts are generated rather than pre-specified. The constructive nature of the PCFG calls upon a potentially infinite set of possible causal functions, yet is governed by the preference for parsimony, and encourages systematic composition (see also Goodman et al., 2008; Bramley et al., 2018). The extended Dirichlet Process for category construction goes beyond a hierarchical Baysian modeling approach where categories are pre-defined as inductive biases (e.g. Griffiths and Tenenbaum, 2009; Goodman et al., 2011), and thus better captures the flexibility of human generalization behaviors (see also Kemp et al., 2010). This method draws a close link with probabilistic program induction models (e.g.