Brain Health & Human Movement: Theoretically your brain is a computer. In reality, it is not.

Brain Health & Human Movement

Theoretically your brain is a computer. In reality, it is not.

By James McLoughlin

It is Brain Awareness Week, and as I prepare an updated block of teaching on human motor control, I found myself reflecting on a metaphor that has quietly shaped decades of our thinking: the idea that the brain functions like a computer.

It’s a compelling comparison. Neat. Logical. Comforting, even. Inputs come in, the brain processes them, and outputs are delivered. For years, this framework has served us well. It gave us elegant models, clean diagrams, and a structured way to understand movement. Feedforward control, predictive processing, optimal motor plans—it all seemed to fit theoretically. In fact, I’ve taught it this way for over 20 years, since I first studied computational theories of motor control at Queen Square, University College London in 2003. And while I am not abandoning all of these ideas, new knowledge is helping me colour-in the many grey areas that still exist. Computational theories have helped us build a foundation; it is becoming increasingly clear that the foundation is not the whole building.

The challenge is that biology does not behave like code. The brain may be modelled in mathematics, but it functions through biology. Unlike computers, which execute precise, sequential instructions at extraordinary speeds, the human nervous system is inherently noisy, nonlinear, and constrained by the biological system it inhabits. Motoneurons do not act like binary switches. Muscles fatigue. Energy fluctuates. Mood varies. Context changes everything.

Trying to explain human movement purely through computational precision is a bit like explaining a symphony using only sheet music—technically accurate but missing the performance entirely. Even the most advanced humanoid robots, capable of jumping, flipping, and kicking, still lack the subtlety, adaptability, and richness of human movement.

Part of the issue lies in how we have tried to organise the brain. For convenience, we have divided it into categories—visual, vestibular, cognitive, psychological, physical. These divisions make teaching easier and research cleaner, but they do not reflect how the brain operates. The brain does not function like a filing cabinet. It does not separate perception from action, or cognition from movement, or the individual from their environment and culture. These boundaries are constructs of our own making, and increasingly, they are limiting our progress.

What is emerging instead is a more integrated and dynamic view of human movement. Rather than seeing the brain as an isolated processor calculating movement in a vacuum, we can begin to understand movement as arising from adaptive, interacting systems—within the body, between people, and across environments.

In this view, perception is not passive. We do not simply receive information about the world and then act upon it. Instead, we perceive through action. Movement shapes perception, and perception shapes movement in a continuous loop. The brain is tuned not just to sensory inputs, but to affordances—the opportunities for action that the environment presents. A staircase is not simply seen; it is climbable. A puddle is not just measured; it is jumpable—or not. Even something as simple as reaching down for an object may be shaped by pain, fear, or prior experience, altering both perception and movement in real time.

This perspective also challenges the idea that the brain must compute precise solutions for every movement. The body has what we describe as motor abundance—multiple ways to achieve the same goal. Rather than controlling every joint and muscle with exact precision, the system stabilises outcomes while allowing variability. Movement, therefore, is not something that is perfectly engineered in advance; it is something that is continuously negotiated in interaction with the environment.

Perhaps most importantly, this approach dissolves the artificial boundaries between domains that we have traditionally treated as separate. In clinical practice, we are increasingly seeing that self-efficacy influences movement, attention shapes balance, emotion alters coordination, and cognition changes the experience of pain. These are not overlapping systems—they are fundamentally integrated. What we often describe as “movement coaching” is, in reality, the modulation of an entire human system operating as a network.

When we take this perspective seriously, everything changes. The brain is no longer a statistician sitting in a dark room calculating probabilities. It is a living organ embedded within a body, shaped by experience, and constantly interacting with a complex and unpredictable world. If we continue to teach, assess, and rehabilitate movement through rigid computational models, we risk missing the very mechanisms that drive meaningful change.

Personally, I find this both exciting and challenging. There is a certain comfort in boxes, arrows, and Venn diagrams. They give us clarity, structure, and a sense of control—particularly when teaching. But they are beginning to limit how deeply we can think and how effectively we can translate that thinking into practice. The future of understanding human movement is not in cleaner diagrams, but in embracing a more complex, messier reality—one defined by simultaneous, parallel processes that evolve and adapt over time.

This is where brain networks and brain health come into focus as the next frontier. When combined with an ecological approach to motor control, they offer a pathway toward better understanding—and better care—for some of the most complex conditions we encounter, including functional neurological disorders, chronic pain, and neurodegenerative disease. Not through increasingly complex computational models, but by recognising and working with neural systems that operate beyond the simplicity of those frameworks.

From falls and basic mobility, to elite sport, we explore this space in depth in the Applied Neuroscience for Human Movement course, where we attempt to unpack the complexity and rebuild a practical framework through discussion and application. For now, this blog may be enough to pause, step back, and consider that the way forward in understanding human movement may not lie in refining old models—but in being willing to move beyond them.

Associate Professor James McLoughlin is Director of Advanced Neuro Rehab, Lead Educator at Advanced Neuro Education and Chief Academic Officer at Your Brian Health.

Some related references:

Cano-de-la-Cuerda, R., Molero-Sánchez, A., Carratalá-Tejada, M., Alguacil-Diego, I. M., Molina-Rueda, F., Miangolarra-Page, J. C., & Torricelli, D. (2015). Theories and control models and motor learning: clinical applications in neurorehabilitation. Neurología (English Edition), 30(1), 32–41.

Levin, M. F., & Piscitelli, D. (2022). Motor Control: A Conceptual Framework for Rehabilitation. Motor Control, 26, 497–517.

Luo, L. (2018). Why Is the Human Brain So Efficient? How massive parallelism lifts the brain's performance above that of AI. Nautilus.

Mangalam, M. (2025). The myth of the Bayesian brain. European Journal of Applied Physiology, 125, 2643–2677.

McLoughlin, J (2020) Ten guiding principles for movement training in neurorehabilitation – OpenPhysio. https://www.openphysiojournal.com/article/ten-guiding-principles-for-movement-training-in-neurorehabilitation/

McLoughlin, J. (2025/2026). Applied Neuroscience for Movement: 100+ Treatment Strategies. Advanced Neuro Education.

Roemmich, R. T., & Bastian, A. J. (2018). Closing the Loop: From Motor Neuroscience to Neurorehabilitation. Annual Review of Neuroscience, 41, 415–429.