Journal article 229 views 72 downloads
A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge
Frontiers in Psychology, Volume: 13
Swansea University Author: Darren Edwards
PDF | Version of Record
© 2022 Edwards, McEnteggart and Barnes-Holmes. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY)Download (11.15MB)
DOI (Published version): 10.3389/fpsyg.2022.745306
Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory wh...
|Published in:||Frontiers in Psychology|
Frontiers Media SA
Check full text
No Tags, Be the first to tag this record!
Psychology has benefited from an enormous wealth of knowledge about processes of cognition in relation to how the brain organizes information. Within the categorization literature, this behavior is often explained through theories of memory construction called exemplar theory and prototype theory which are typically based on similarity or rule functions as explanations of how categories emerge. Although these theories work well at modeling highly controlled stimuli in laboratory settings, they often perform less well outside of these settings, such as explaining the emergence of background knowledge processes. In order to explain background knowledge, we present a non-similarity-based post-Skinnerian theory of human language called Relational Frame Theory (RFT) which is rooted in a philosophical world view called functional contextualism (FC). This theory offers a very different interpretation of how categories emerge through the functions of behavior and through contextual cues, which may be of some benefit to existing categorization theories. Specifically, RFT may be able to offer a novel explanation of how background knowledge arises, and we provide some mathematical considerations in order to identify a formal model. Finally, we discuss much of this work within the broader context of general semantic knowledge and artificial intelligence research.
functional contextualism, machine learning, Relational Frame Theory (RFT), categorization,background knowledge
Faculty of Medicine, Health and Life Sciences