Learning a theory of causality.
The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA. ngoodman@stanford.edu
Noah D
ND
Goodman
Tomer D
TD
Ullman
Joshua B
JB
Tenenbaum
D001499:Bayes Theorem; D015984:Causality; D003071:Cognition; D003198:Computer Simulation; D003210:Concept Formation; D006801:Humans; D007323:Instinct; D007858:Learning; D008960:Models, Psychological