Contextualizing Concepts using a Mathematical Generalization of the Quantum Formalism
Liane Gabora, Diederik Aerts
Abstract
We outline the rationale and preliminary results of using the State Context Property (SCOP) formalism, originally developed as a generalization of quantum mechanics, to describe the contextual manner in which concepts are evoked, used, and combined to generate meaning. The quantum formalism was developed to cope with problems arising in the description of (1) the measurement process, and (2) the generation of new states with new properties when particles become entangled. Similar problems arising with concepts motivated the formal treatment introduced here. Concepts are viewed not as fixed representations, but entities existing in states of potentiality that require interaction with a context--a stimulus or another concept--to 'collapse' to an instantiated form (e.g. exemplar, prototype, or other possibly imaginary instance). The stimulus situation plays the role of the measurement in physics, acting as context that induces a change of the cognitive state from superposition state to collapsed state. The collapsed state is more likely to consist of a conjunction of concepts for associative than analytic thought because more stimulus or concept properties take part in the collapse. We provide two contextual measures of conceptual distance--one using collapse probabilities and the other weighted properties--and show how they can be applied to conjunctions using the pet fish problem
Create a lesson
Related papers
An entangling Hubbard quasiparticle collider on a digital quantum processor
Chan Bin Bark, Hanbyul Kim, Yoonil Koo et al.
A Warped Born Rule Turns an Observer into a Maxwell's Demon
Olumakinde Ogunnaike
A scalable sample-based quantum diagonalization solver for general Pauli-sum Hamiltonians
Yutaro Iiyama
Quantum Query Complexity of Maximum Degree and its Generalizations
Amin Shiraz Gilani, Nikhil S. Mande, Subhasree Patro et al.
Maximally antiflat fixed-particle-number fermionic states
Rahul Gupta, Arya Haghighian, James D. Whitfield
An Annealing-based Quantum Neural Network with Discrete Wavelet Transforms for Enhanced Air Pollution Nowcasting
Otto Menegasso Pires, André Saimon, Daniela Herrmann et al.