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Non-commutative structures of brain and cognition: quantum and contextual probability as a translational framework

Haruki Emori, Andrei Khrennikov, Atsushi Iriki

Frontiers in Human Neuroscience July 17, 2026 DOI: 10.3389/fnhum.2026.1882287 (opens in new tab) via OpenAlex

Summary

AI-generated from the abstract

Cognitive neuroscience has accumulated robust findings that systematically resist explanation within classical probabilistic and causal frameworks, including order effects in judgment, multi-path motor preparation, perceptual binding, attentional selection, and the recursive construction of self and time. The authors argue these are signatures of a deeper, non-commutative architecture of cognition and brain dynamics. They propose quantum probability theory together with contextual probability theory as rigorous translational languages for cognitive processes, where observation actively transforms underlying state spaces. The framework suggests neural network dynamics are intrinsically organized to generate quantum-like representations, with structural primitives mapping onto specific brain activities. They outline a new sub-domain called Cognitive Structural Science and a research program combining human-macaque experiments with quantum-computer simulation.

Study at a glance

Characteristics Theoretical or philosophical paper Peer reviewed
Keywords Probabilistic logic Sketch Cognition Population Perception
Key finding Proposes that cognitive phenomena resistant to classical explanation reflect a non-commutative architecture of brain dynamics, which can be rigorously described using quantum probability theory and contextual probability theory, and that neural network dynamics generate quantum-like representations.

Abstract

Cognitive neuroscience has accumulated robust findings (e.g., order effects in judgment, multi-path motor preparation, perceptual binding, attentional selection, and the recursive construction of self and time) that systematically resist explanation within classical probabilistic and causal frameworks. We argue that these are not anomalies but signatures of a deeper, non-commutative architecture of cognition and brain dynamics. We propose quantum probability theory together with contextual probability theory, not as metaphorical analogies but as rigorous translational languages for cognitive processes in which observation actively transforms underlying state spaces. Underlying this framework is the conjecture that neural network dynamics in the brain are intrinsically organized to generate quantum-like representations—rather than merely being described by quantum mathematics from the outside. Their structural primitives (i.e., superposition, entanglement, projection, and non-commutativity) map onto premotor population coding, long-range cortical synchrony, prefrontal state dynamics, and default-mode network activity. On this basis we sketch a new sub-domain (namely, Cognitive Structural Science) that treats the geometry and algebra of cognitive state spaces as primary explananda, and outline a research program combining homologous human–macaque experiments with quantum-computer simulation as a constrained testbed.

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