Quantum probability in decision making from quantum information representation of neuronal states

Area of Science:

• Neuroscience
• Quantum Physics
• Cognitive Science

Background:

• Growing interest in applying quantum formalism to model decision-making processes.
• Existing cognitive psychology and social science data exhibit quantum-like statistical patterns.
• Uncertainty in neural action potential generation stems from complex electrochemical processes.

Purpose of the Study:

• To address how neurons generate quantum-like statistical data.
• To propose a model for decision-making based on quantum principles.
• To explain the link between electrochemical processes and quantum-like neural behavior.

Main Methods:

• Representing uncertainty in action potential generation using a quantum-like formalism.
• Utilizing quantum information state spaces as extensions of classical neural codes (e.g., quiescent/firing).
• Modeling neuronal groups as open quantum systems interacting with their electrochemical environment.
• Describing decision-making as a decoherence process in the eigenstate basis of a psychological function (F).

Main Results:

• Neural information processing involves superpositions of quantum-like states.
• Decision-making culminates in a steady state achieved through decoherence.
• The model provides a linear representation of complex, nonlinear electrochemical dynamics.
• The linear representation ensures exponentially rapid convergence to a decision state.

Conclusions:

• The quantum-like model offers a framework for understanding quantum-like statistics in neural data.
• Decision-making can be effectively described as a quantum decoherence process.
• This approach bridges the gap between electrochemical brain processes and quantum information theory.