Abstract
We propose a model to characterize the type of knowledge acquired in Artificial Grammar Learning (AGL). In particular, we suggest a way to compute the complexity of different test items in an AGL task, relative to the training items, based on the notion of Shannon entropy: The more predictable a test item is from training items, the higher the likelihood that it will be selected as compatible to the training items. Our model is an attempt to formalize some aspects of inductive inference by providing a quantitative measure of the knowledge abstracted by experience. We motivate our particular approach from research in reasoning and categorization, where reduction of entropy has also been seen as a plausible cognitive objective. This may suggest that reducing (Shannon) uncertainty may provide a single explanatory framework for modeling as diverse aspects of cognition, as learning, reasoning, and categorization.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
