Triple equivalence in modelling insight and creativity: Classical and quantum perspectives.

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Understanding novel idea generation in the brain (insight and creativity) is a key neuroscience challenge.
• Traditional models focus on approximating external states, neglecting internal idea generation.
• Internal generative processes for creating new ideas remain underexplored.

Purpose of the Study:

• Propose a normative account of insight and creativity.
• Provide a formal framework for understanding spontaneous idea generation in neural circuits.
• Explore the role of efficient algorithmic space exploration in creativity.

Main Methods:

• Reviewing existing neuroscience frameworks (predictive coding, Bayesian brain, free-energy principle).
• Proposing a triple equivalence: neural dynamics, Bayesian inference, and algorithmic computation.
• Introducing quasi-quantum neural networks for efficient global search.

Main Results:

• Neural circuits driven by mental actions can spontaneously generate creative ideas.
• Creativity requires efficient exploration of algorithmic space to minimize free energy.
• Insight involves non-local search strategies, distinguishing it from predictive processing.

Conclusions:

• Insight and creativity emerge from a self-organizing exploratory process.
• The brain can be viewed as a spontaneous algorithm generator.
• This framework offers a novel perspective beyond the brain as a mere predictor.