Bayesian Models of Conceptual Development: Learning as Building Models of the World
Name
Annual_Review_Dev_2020__psyarxiv_version.pdf
Description
Submitted version
Size
923.82 KB
Format
Unknown
Checksum (MD5)
a01ccb95e8451d125864689da6500107
Author(s)
Ullman, Tomer D
Tenenbaum, Joshua B
Date Issued
2020
Journal
Annual Review of Developmental Psychology
Publisher
Annual Reviews
Citation
Ullman, Tomer D and Tenenbaum, Joshua B. 2020. "Bayesian Models of Conceptual Development: Learning as Building Models of the World." Annual Review of Developmental Psychology, 2 (1).
Version
Original manuscript
Abstract
A Bayesian framework helps address, in computational terms, what knowledge children start with and how they construct and adapt models of the world during childhood. Within this framework, inference over hierarchies of probabilistic generative programs in particular offers a normative and descriptive account of children's model building. We consider two classic settings in which cognitive development has been framed as model building: ( a) core knowledge in infancy and ( b) the child as scientist. We interpret learning in both of these settings as resource-constrained, hierarchical Bayesian program induction with different primitives and constraints. We examine what mechanisms children could use to meet the algorithmic challenges of navigating large spaces of potential models, in particular the proposal of the child as hacker and how it might be realized by drawing on recent computational advances. We also discuss prospects for a unifying account of model building across scientific theories and intuitive theories, and in biological and cultural evolution more generally.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1146/ANNUREV-DEVPSYCH-121318-084833
