Neural implementation of operations used in quantum cognition
Abstract
Quantum probability theory has been successfully applied outside of physics to account for numerous findings from psychology regarding human judgement and decision making behavior. However, the researchers who have made these applications do not rely on the hypothesis that the brain is some type of quantum computer. This raises the question of how could the brain implement quantum algorithms other than quantum physical operations. This article outlines one way that a neural based system could perform the computations required by applications of quantum probability to human behavior.
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Busemeyer et al. explored how a neural-based system could perform the computations required for applying quantum probability to human behavior.
“Busemeyer et al. (2017) explored how a neural-based system could perform the computations required for applying quantum probability to human behavior.”
From Coupling quantum-like cognition with the neuronal networks within generalized probability theory (2025) · cites this paper as Busemeyer et al. (2017)
There is still no proper mathematical model that links the neurophysiological processes in the brain with a QL description.
“However, there is still no proper mathematical model that links the neurophysiological processes in the brain with a QL description.”
From Coupling quantum-like cognition with the neuronal networks within generalized probability theory (2025) · cites this paper as Busemeyer, Fakhari, and Kvam (2017)
The QL approach lacks a clear connection to neurophysiological processes in the brain, and the neurophysiological mechanisms responsible for generating macroscopic QL behavior remain unknown.
“A key challenge for the QL approach lies in the fact that the neurophysiological mechanisms responsible for generating macroscopic QL behavior remain unknown.”
From Coupling quantum-like cognition with the neuronal networks within generalized probability theory (2025) · cites this paper as Busemeyer, Fakhari, and Kvam (2017)
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References 48 · Cited by 41
- Authors
- Busemeyer, J. R., Fakhari, P., & Kvam, P.
- Affiliations
- Indiana University (US)
- Michigan State University (US)
- Corresponding author
- Busemeyer, Jerome R.
- Venue
- Progress in Biophysics and Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 130
- Issue
- Pt A
- Pages
- 53-60
- ISSN
- 0079-6107 (ISSN-L)
- OpenAlex
- W2611516981
- Cited by
- 41
- References
- 48
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 2 · 2020: 7 · 2019: 1 · 2018: 5 · 2017: 4
- FWCI
- 0.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Progress in Biophysics and Molecular Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- quantum cognition, quantum probability, neural implementation, quantum algorithms, neural computation
- MeSH terms
- Humans, Nerve Net, Nervous System, Probability Theory, Quantum Theory
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