Is Quantum Cognition the Path to Strong AI (or Artificial General Intelligence)?

Quantum cognition may be the next frontier to explaining the mind.

The existing scientific literature about cognitive architectures does not define the “correct path” to follow when it comes to new research initiatives. Inspecting the papers from a peer-reviewed journal dedicated to the Common Model of Cognition Project (aka CMCB, Common Model of Cognition Bulletin) I found that the progress follows an emerging pattern (bottom-up).

With that dynamic, it seems that we must let the applications of the model direct its development efforts.

Going beyond the constructive and incremental development of theories and cognitive models to explain the mind, we find in the literature some alternative approaches that try to achieve the same goal from another perspective.

Within these approaches, we find the field of quantum cognition.

We must not confuse the term with the so-called quantum brain, quantum mind, or quantum consciousness which is a hypothesis that assumes that quantum processes occur in the brain.

Quantum cognition is an emerging field of research where the mathematical formalisms of quantum theory inspire the development of new models of cognition that show a better explanation of superior cognitive phenomena.

Examples of these human phenomena are memory, information retrieval, language, decision making, social interaction, personality psychology, and philosophy of mind.

Cognitive scientists are said to face the same kind of problems that have forced physicists to abandon classical physics. They find that it is only possible to obtain partial information about a complex system at a deterministic time since each measure disturbs the next measure.

The Quantum Theory allows combining the partial information of a system to understand system-wide consistency through a fundamentally different approach to logic, reasoning, and probabilistic inference.

The cognitive revolution of the 1960s was based on computational logic classical and the emergence of neural networks, and in the 1970s was based on classical dynamic systems.

These elements constitute the pillars of the theories on current cognitive architectures and neural networks and are based on a series of assumptions. However, we find that there are complex phenomena of human behavior that do not obey the restrictions imposed by classical logic.

Building on the work of John Von Neumann and other authors (Kronz & Lupher, 2021) it has become apparent that the heart of the quantum theory is a new kind of theory of probability, based on ortho algebras rather than Boolean algebras.

This theory is more general than the traditional probability theory. It turns out to be more powerful for solving difficult problems that have resisted traditional approaches to rationality, logical thinking, and probabilistic reasoning, which opens up new horizons for cognitive modeling and its rationale.

For example, note that quantum logic does not always follow the distributive axiom of logic boolean, or that quantum probabilities do not always obey the law of Total Probability Kolmogorov. Nor does quantum reasoning always fulfill the principle of monotone reasoning.

Thus, it can be seen that the classical theory is a restrictive case of a more general quantum theory.

Jerome Busemeyer and Peter Bruza in Quantum Models of Cognition and Decision (Busemeyer & Bruza, 2012) argue that the underlying mathematical structures in quantum theory provide a better explanation of human thought than traditional models, introducing the fundamentals for modeling systems dynamic-probabilistic models that use two aspects of quantum theory:

1. contextuality of judgments and decisions, which is captured in quantum theory through the idea of ​​"interference" understood as the context generated by doing that a first judgment (or decision) interferes with subsequent judgments (or decisions) to produce effects of order, for which judgments and decisions are non-commutative.

   
   

2. quantum entanglement: in quantum physics, it refers to the phenomenon by which observation of a part of a system instantly affects the state of another part of the system, even though sidereal distances separate their respective systems.

The crucial element is that interlocking systems can no validly be decomposed and modeled as separate subsystems. This promotes the development of quantum-type models for cognitive phenomena that are not decomposable by their very nature and for which quantum theory provides formal tools to model them as non-interacting systems, decomposable (or non-reductionist).

We are witnessing the rise of a field with applications in areas such as perception, conceptual judgments, decision making, and information retrieval.

Busemeyer and Other researchers (Wang et al., 2013) try to answer very important questions such as:

• Why apply quantum concepts to cognition? human?
• How do quantum models differ from traditional ones?
• What cognitive processes have been already modeled using a quantum model?

The relevancy now lies in the advance of the state of the art following a vision of where we are and where research efforts are directed. If we join these efforts to the rise of quantum computing, perhaps we could become closer to validating the cognitive models and observing artificial general intelligence's birth.

Or maybe not.