Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory theory.
University of California, Berkeley · University of Michigan–Ann Arbor
Abstract
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the…
Citation impact
- FWCI
- 33.57
- Percentile
- 100%
- References
- 171
Authors
2Topics & keywords
- Probabilistic logic
- Learning theory
- Causal model
- Cognitive science
- Causal structure
- Social constructivism
- Psychology
- Imitation