An Active Inference Approach to Modeling Structure Learning: Concept Learning as an Example Case.

ABSTRACT: Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modelin

SUBMITTER: Smith R 

PROVIDER: S-EPMC7250191 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature