
TL;DR
This paper proposes a novel theoretical framework using Hilbert space structures from classical field theory to model non-Bayesian human cognition, extending affect models and aligning with fear conditioning data.
Contribution
It introduces a field theoretic model of cognition based on Hilbert spaces, offering a new approach to understanding non-Bayesian cognitive processes.
Findings
Extended the circumplex model of affect with Poincaré sphere representation
Developed a toy field theoretic brain model consistent with fear conditioning studies
Provided a qualitative agreement with Pavlovian fear conditioning data
Abstract
The Hilbert space structure of classical field theory is proposed as a general theoretical framework to model human cognitive processes which do not often follow classical (Bayesian) probability principles. This leads to an extension of the circumplex model of affect and a Poincar\'{e} sphere representation. A specific toy field theoretic model of the brain as a coherent structure in the presence of noise is also proposed that agrees qualitatively with Pavlovian fear conditioning studies.
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Taxonomy
TopicsMental Health Research Topics · Neural dynamics and brain function · Memory and Neural Mechanisms
