The Hierarchically Mechanistic Mind (HMM) offers an ambitious and integrative new framework for understanding the human brain. Proposed in a 2019 paper in Cognitive, Affective, & Behavioral Neuroscience by Paul B. Badcock and a team of prominent researchers including Karl J. Friston, this theory aims to bridge long-standing divisions between psychology and neuroscience.
The goal is to provide a unified model of the brain’s structure, function, and dynamics. The authors state, “the purpose of this review is to suggest that many extant models of the structure, dynamics, and function of the brain can be integrated under the unifying framework of the Hierarchically Mechanistic Mind (HMM)”.
What is the Hierarchically Mechanistic Mind?
At its core, the Hierarchically Mechanistic Mind views the brain as a complex, adaptive system constantly working to minimize uncertainty and disorder, also known as entropy. It achieves this through continuous perception and action cycles. The paper describes the brain as a system that “functions to minimize the entropy of our sensory and physical states via action-perception cycles generated by hierarchical neural dynamics”.
This system is organized in a hierarchy. Lower levels consist of specialized neural mechanisms that handle basic sensorimotor processing. Higher levels are made up of more flexible, integrated mechanisms responsible for executive functions like metacognition, which is thinking about one’s own thinking. This organization allows the brain to process information efficiently, balancing specialized local processing with global integration. This is a core tenet of the Hierarchically Mechanistic Mind model.
An Evolutionary Theory and the Free-Energy Principle
The Hierarchically Mechanistic Mind model is built upon a foundation of Evolutionary Systems Theory (EST) and is mathematically formalized by the Free-Energy Principle (FEP). EST explains how complex systems evolve through the interplay of selection and self-organization over different timescales, from real-time neural activity to long-term evolution.
The FEP, a major theory in computational neuroscience developed by co-author Karl Friston, complements this view. It describes the brain as an “inference machine” that constantly makes predictions about the world to minimize surprise. According to the authors, “to remain alive, all living systems must minimize the quantity ‘variational free-energy’ to reduce the entropy (i.e., the decay or dispersion) of their sensory and physiological states”.
Essentially, our brains are not passive recipients of information. Instead, they actively generate models of the world and use sensory input to correct errors in their predictions. This error-minimization process happens across the brain’s entire hierarchy, from basic perception to complex thought.
A New Heuristic for Research
The Hierarchically Mechanistic Mind is more than just a model; it’s presented as a powerful tool for generating new research. The authors suggest it “can be used as a systematic heuristic to generate unique, integrative hypotheses from which more specific, testable predictions can be derived”.
By synthesizing insights from evolutionary psychology, developmental psychology, and various subdisciplines, the HMM provides a multi-level approach to research based on Tinbergen’s four questions about the causation of behavior.
The paper provides a compelling example by applying the HMM to depression. It reframes depression not simply as a disorder, but as a potential adaptive strategy. It suggests depression may be a “risk-averse adaptive prior that minimizes uncertainty in the social world” when an individual perceives a high probability of negative outcomes like rejection or defeat. This strategy manifests as changes in perception, such as a heightened sensitivity to social risks, and action, such as social withdrawal.
This integrative theory provides a fresh perspective, aiming to harmonize different fields of science to create a more complete picture of the human mind. The Hierarchically Mechanistic Mind ultimately presents a cohesive explanation for the brain and behavior by connecting evolutionary pressures, developmental pathways, and real-time neural processing under a single, powerful principle.