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Updated: Jun 12, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Probabilistic programming versus meta-learning as models of cognition
Desmond C Ong1, Tan Zhi-Xuan2, Joshua B Tenenbaum2
1Department of Psychology, University of Texas at Austin, Austin, TX, USA desmond.ong@utexas.edu https://cascoglab.psy.utexas.edu/desmond/.
Abstract:
We summarize the recent progress made by probabilistic programming as a unifying formalism for the probabilistic, symbolic, and data-driven aspects of human cognition. We highlight differences with meta-learning in flexibility, statistical assumptions and inferences about cogniton. We suggest that the meta-learning approach could be further strengthened by considering Connectionist and Bayesian approaches, rather than exclusively one or the other.
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