For a long time, we’ve thought of the mind as a kind of ghost in the machine—a brilliant, disembodied computer living inside the skull. We imagine that if we could just plug a brain into a vat and feed it information, it could think, reason, and understand. Embodied Cognition is a radical theory that argues this is a profound mistake. It proposes that the “machine”—our body—is not just a life-support system for the brain. The body, with all its physical quirks and its constant interaction with the world, is an indispensable part of the thinking process itself. This is the story of how intelligence is not something your brain has, but something your whole body does.
To understand the revolution of embodied cognition, we must first understand the old guard: Computationalism. This is the classic view that the mind is a software program and the brain is the hardware it runs on.
Analogy: The “Brain as a CPU.” In this model, the brain is the Central Processing Unit, doing all the “real” thinking. The body is just a set of peripherals. The eyes are a camera, the ears are a microphone, the hands are robotic grippers, and the mouth is a speaker. They are simple input/output devices that feed abstract symbols to the brain, which then processes those symbols and sends commands back out. The messy, physical nature of the body is considered irrelevant to the pure, logical process of thought.
Embodied cognition argues that this separation is an illusion. The peripheral is the processor.
Embodied cognition is not a single, unified theory, but a broad movement with several key ideas.
The type of body you have doesn’t just influence your thoughts; it determines the very concepts you are capable of having. Our thinking is shaped by the fact that we have two arms, two legs, forward-facing eyes, and an upright posture.
Analogy: The Mind of an Octopus. Imagine the consciousness of an octopus. It has eight semi-autonomous arms, each packed with neurons, that can taste and touch the world independently. Its “brain” is distributed throughout its body. How would it think about the world? Its concept of “self” might be radically different from our own unified sense. Its understanding of “grasping,” “exploring,” or even “thinking” would be fundamentally alien to us. It doesn’t have a “body image”; its body is its mind in action. This demonstrates that our human concepts like “up,” “down,” “forward,” and “backward” are not abstract truths of the universe; they are concepts that exist for us because we have bodies that stand upright and move in a particular way.
The primary purpose of the brain did not evolve to solve abstract logic puzzles or play chess. It evolved to enable an organism to move, survive, and interact successfully with a dynamic and often dangerous world. Thought is in service of action.
Example: The Outfielder Catching a Ball. How does a baseball player run to the perfect spot to catch a fly ball? The computationalist view would say their brain is a physics engine. It sees the initial velocity and angle of the ball, calculates the parabolic arc in real-time, factors in air resistance, and computes the exact spot to run to. The embodied view says this is absurdly complex and unnecessary. Instead, the outfielder uses a simple, body-based heuristic: they run in a way that keeps the image of the ball appearing to move in a straight line in their field of view. If the ball’s image curves up, they run back; if it curves down, they run forward. Their “thinking” is not a separate calculation followed by an action. The thinking is the dynamic coupling of their running and their perception.
We don’t just think inside our heads. We actively use the environment to reduce our internal cognitive load. This is the Extended Mind Thesis.
Example: The Tetris Player. A novice Tetris player will look at a block, mentally rotate it in their head several times, decide on the best fit, and then move and rotate the block on screen. An expert player does something completely different. They don’t do the rotation in their head. They physically hit the rotate button as fast as they can, using the screen as an external aid to their visual processing. The physical act of rotating the block on the screen is part of their thought process. They are thinking with their fingers and the screen, not just their brain.
Example: Using a Notepad. When you write down a complex list of ideas on a piece of paper to organize them, that paper is not just a record of your thoughts. For that moment, it is your memory. You are offloading your working memory onto an external medium, allowing your brain to focus on higher-level connections between the ideas.
The theory of embodied cognition poses a fundamental challenge to the way we have traditionally built artificial intelligence, especially purely software-based AIs like Large Language Models.
For a disembodied AI that has only ever learned from text, the symbols it manipulates are ungrounded. It knows that the word “heavy” is statistically associated with words like “rock,” “weight,” and “effort.” But it has no embodied, physical understanding of what the sensation of “heavy” actually is—the feeling of muscle strain, the force of gravity. A human’s concept of “heavy” is grounded in a lifetime of physical interaction. An AI’s is a free-floating symbol in a web of other symbols. Embodied cognition argues that without this grounding, true, human-like understanding is impossible.
Why do AI systems struggle with what we call “common sense”? An embodied perspective would say it’s because common sense is not a database of facts, but a set of intuitions we have learned from having a body and living in the physical world. We know a glass will spill if we tip it too far not because we read it in a book, but because we have spilled things. An AI that has never had a body has no access to this deep well of physical intuition.
If this theory is correct, the path to more general and robust AI is not just about bigger datasets and faster processors. It is about giving AIs bodies. The argument is that by forcing an AI to learn through physical interaction—by allowing a robot to bump into walls, to learn how to grasp a fragile object without breaking it, to navigate a cluttered room—we allow it to ground its internal models in physical reality. Learning would cease to be an abstract, statistical process and would become an embodied, experiential one.
Embodied cognition asks us to fundamentally rethink the nature of intelligence. It suggests that the mind is not a neat, logical program running on top of a messy biological machine. Instead, it proposes that the mind is a messy, emergent property of the continuous, dynamic dance between the brain, the body, and the world. It shifts the classic philosophical starting point from René Descartes’ “I think, therefore I am” to a new, more integrated perspective: “I am a body in the world, therefore I think.”
