ReviewFrontiers in psychology2022
A Functional Contextual Account of Background Knowledge in Categorization: Implications for Artificial General Intelligence and Cognitive Accounts of General Knowledge.
Review in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Autistic Individuals' Categorical Induction Abilities Improve by Mid-Adolescence.Journal of autism and developmental disorders · 2025Article
- FurtherFrontiers in computational neuroscience · 2025Article
- Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density.Frontiers in artificial intelligence · 2025Article
- Modeling arbitrarily applicable relational responding with the non-axiomatic reasoning system: a Machine Psychology approach.Frontiers in robotics and AI · 2025Article
- A functional contextual, observer-centric, quantum mechanical, and neuro-symbolic approach to solving the alignment problem of artificial general intelligence: safe AI through intersecting computational psychological neuroscience and LLM architecture for emergent theory of mind.Frontiers in computational neuroscience · 2024Article
- Icono: a universal language that shows what it says.Frontiers in psychology · 2023Article
- Functional contextual implementation of an evolutionary, entropy-based, and embodied free energy framework: Utilizing Lagrangian mechanics and evolutionary game theory's truth vs. fitness test of the veridicality of phenomenological experience.Frontiers in psychology · 2023Article
- An Open-Source Relational Network Derivation Script in R for Modeling and Visualizing Complex Behavior for Scientists and Practitioners.Frontiers in psychology · 2022Article
- Going beyond the DSM in predicting, diagnosing, and treating autism spectrum disorder with covarying alexithymia and OCD: A structural equation model and process-based predictive coding account.Frontiers in psychology · 2022Article
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3 authors.
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Abstract
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.
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