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Cited by 11Quantum Mechanics and ApplicationsNoncommutative and Quantum Gravity TheoriesAdvanced Algebra and Logic

Contextual measurement model and quantum theory

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Khrennikov, Andrei·2024·Royal Society Open Science
DOI 10.1098/rsos.231953PMC PMC10977392
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Abstract

We develop a contextual measurement model (CMM) that is used for the clarification of the quantum foundations. This model matches Bohr’s views on the role of experimental contexts. CMM is based on a contextual probability theory that is connected with generalized probability theory. CMM covers measurements in classical, quantum and semi-classical physics. The CMM formalism is illustrated by a few examples. We consider the CMM framing of classical probability, the von Neumann measurement theory and the quantum instrument theory. CMM can also be applied outside of physics, e.g. in cognition, decision-making and psychology, the so-called quantum-like modelling.

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  • Probabilistically non-classical properties of mental phenomena do not necessarily imply that they should be described using quantum formalism (such as complex Hilbert space), or quantum probability, information, and measurement theory.

    “It is important to note that the probabilistically non-classical properties of mental phenomena do not necessarily imply that they should be described using quantum formalism (such as the complex Hilbert space), or quantum probability, information, and measurement theory.”

    From Coupling quantum-like cognition with the neuronal networks within generalized probability theory (2025) · cites this paper as Khrennikov (2024)

  • Some GPT-based models may better align with cognitive information processing and decision-making.

    “Some GPT-based models may better align with cognitive information processing and decision-making, as highlighted in Khrennikov (2024).”

    From Coupling quantum-like cognition with the neuronal networks within generalized probability theory (2025) · cites this paper as Khrennikov (2024)

    The Introduction says quantum-like modelling is applied to cognition, decision-making and AI. Neither that passage nor searches for GPT-based models and large language models address whether such models better align with cognitive information processing or decision-making. (LLM, conf=0.75)

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References 63 · Cited by 11

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Authors
Khrennikov, A.
Affiliations
Linnaeus University (SE)
Corresponding author
Khrennikov, Andrei
ORCID
Khrennikov, Andrei
Venue
Royal Society Open Science
Publisher
The Royal Society
Published
2024-03
Volume
11
Issue
3
Pages
231953-231953
ISSN
2054-5703 (ISSN-L)
DOI
10.1098/rsos.231953
PMC
PMC10977392
OpenAlex
W4393008685
Cited by
11
References
63
Citations by year
2026: 4 · 2025: 7
FWCI
1.75
Type
journal article
Language
en
Open access
Licence
Locations
  • Royal Society Open Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Noncommutative and Quantum Gravity Theories
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Algebra and Logic
Keywords
quantum foundations, experimental context, generalized probability theory, classical probability, cognition, decision-making

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