The Model of Hierarchical Complexity
We use the rationale and application of the Model of Hierarchical Complexity at the basis of all our work — mathematical formulation, developmental cognitive neuroscience, AI, and Robotics.
The Model of Hierarchical Complexity (MHC) is a formal theory and a mathematical psychology framework for scoring how complex a behavior is, stage by stage. Developed by Michael Lamport Commons and colleagues at Harvard Medical School and Dare Association, it quantifies 17 stages of development.
There is a lot to learn about the MHC and how it quantifies and describes stages. Learn more here or contact us for detailed interest and information.
Artificial Intelligence & the Model of Hierarchical Complexity
Ants achieve stage 3. This is where AI is at the moment. The mask to this fact is that current energetic and computational resources are so strong and powerful, that data is handled in huge quantities, which clearly surpass the activity of a human’s conscious mind. AI is achieved by applying brute force, not through the elegance and economy of mimicking natural processes.
Corvids achieve stage 4.
Elephants achieve stage 5.
Chimpanzees are the species that achieve the highest stage after humans, stage 8 or 9.
In adulthood, the highest stage humans usually achieve is between 10 and 11 (abstract and formal stages). But they can go up until stage 17 (metacrossparadigmatic).
Cognitive Neuroscience & the Model of Hierarchical Complexity
How do we know that the MHC actually mimics natural processes underlying intelligence?
We have data! And want to gather more.
