# Coupling quantum-like cognition with the neuronal networks within generalized probability theory

arXiv: https://arxiv.org/abs/2411.00036 · HTML: https://mlsanity.com/paper/2411.00036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 119 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [A probabilistic framework for analysing the compositionality of conceptual combinations](https://mlsanity.com/paper/1305.5753/) (2013)
- [Quantum Models for Psychological Measurements: An Unsolved Problem](https://mlsanity.com/paper/1403.3654/) (2014)
- [Nondistributivity of human logic and violation of response replicability effect in cognitive psychology](https://mlsanity.com/paper/2208.12946/) (2022)
- [Snow Queen is Evil and Beautiful: Experimental Evidence for Probabilistic Contextuality in Human Choices](https://mlsanity.com/paper/1711.00418/) (2017)
- [True Contextuality Beats Direct Influences in Human Decision Making](https://mlsanity.com/paper/1807.05684/) (2018)
- [A Quantum-Classical Model of Brain Dynamics](https://mlsanity.com/paper/2301.09569/) (2023)
- [Quantum Circuit Components for Cognitive Decision-Making](https://mlsanity.com/paper/2302.03012/) (2023)
- [Quantum Information Biology: from information interpretation of quantum mechanics to applications in molecular biology and cognitive psychology](https://mlsanity.com/paper/1503.02515/) (2015)
- [Quantum-like modeling in biology with open quantum systems and instruments](https://mlsanity.com/paper/2010.15573/) (2020)
- [Open systems, quantum probability and logic for quantum-like modeling in biology, cognition, and decision making](https://mlsanity.com/paper/2304.08599/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum-like cognition and decision making in the light of quantum measurement theory](https://mlsanity.com/paper/2503.05859/) (2025)

## Abstract (arXiv)

The past few years have seen a surge in the application of quantum theory methodologies and quantum-like modeling in fields such as cognition, psychology, and decision-making. Despite the success of this approach in explaining various psychological phenomena such as order, conjunction, disjunction, and response replicability effects there remains a potential dissatisfaction due to its lack of clear connection to neurophysiological processes in the brain. Currently, it remains a phenomenological approach. In this paper, we develop a quantum-like representation of networks of communicating neurons. This representation is not based on standard quantum theory but on generalized probability theory (GPT), with a focus on the operational measurement framework. Specifically, we use a version of GPT that relies on ordered linear state spaces rather than the traditional complex Hilbert spaces. A network of communicating neurons is modeled as a weighted directed graph, which is encoded by its weight matrix. The state space of these weight matrices is embedded within the GPT framework, incorporating effect observables and state updates within the theory of measurement instruments a critical aspect of this model. This GPT based approach successfully reproduces key quantum-like effects, such as order, non-repeatability, and disjunction effects (commonly associated with decision interference). Moreover, this framework supports quantum-like modeling in medical diagnostics for neurological conditions such as depression and epilepsy. While this paper focuses primarily on cognition and neuronal networks, the proposed formalism and methodology can be directly applied to a wide range of biological and social networks.