AI and Neuro-Narratives: Moving Beyond Mechanistic Minds

AI and Neuro-Narratives: Moving Beyond Mechanistic Minds

Embracing an integrated vision of what it means to think, feel, and be

Hello Interactors,

All the talk and evidence of AI, chips in the brain, and robotic overlords has created emotions ranging from hysteria to malaise to clinical depression. How much of this is caused or influenced by narratives spun by favored voices telling tall tales of proximal parables and are there other ways to think about our brain than just a processor?

Let’s find out…

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Our brain is an energy intense organ. It consumes 20% of our energy but accounts for just 2% of our body weight. To manage this high demand for energy, the brain employs various strategies to simplify tasks and processes. One of those is to simplify how the world works. Like dividing it into discernable individual component parts.

In a world increasingly seduced by these crisp edges of in groups and out groups, there exists a tribe of techno-optimists, guardians of an old tale, who look to the brain as humanity's ultimate processor and a promise and desire for digital immortality. This romanticized notion of the “mind as computer” is facing competition as feats of AI reveal a seemingly superior capability to their own self-assuming super-intelligence. So, they want their outdated hardware upgraded. It's all positioned as cutting edge and futuristic but harks back to the clockwork dualistic and mechanistic universe of the Enlightenment.

We’ve been preached a digital gospel that suggests the warm wetware within our skulls operates like baked silicon chips, crunching data of daily existence with the cold precision of a CPU. Yet, simmering in the biochemistry that hosts these digital dreams are ripples of evidence captured and crunched by computers and displayed in the form of MRI’s, fMRI’s, PET scans, SPECT scans, NIRS, and MEG’s. These images lead some cognitive scientists, with the help of various forms of AI, to slowly dismantle the mechanistic metaphor of ‘the brain as CPU’, piece by intricate piece.

The foreground is a brain made of thread-like neural networks in blue and red. Circular lights blink on some neurons. Decorative black background.
“Computing systems that appear to generate brain-like activity may be the result of researchers guiding them to a specific outcome.” “Neural networks, a type of computing system loosely modeled on the organization of the human brain, form the basis of many artificial intelligence systems for applications such speech recognition, computer vision, and medical image analysis. But one study urges caution when comparing neural networks to the brain.” Source: MIT News

The metaphor of the brain functioning as a processor is as old as Alan Turing and the mid-20th century computational theories that birthed computer science. These ideas and experiments propagated as mass media proliferated and now serve as common conceptions of how the mind works. Other historical and cultural factors contribute to the persistence of this metaphor and perpetuated among teachers, scientists, and attention seeking tech moguls.

But it was centuries before, during the Enlightenment and the scientific revolution, that a significant shift towards rationalist, determinist, and mechanistic views of nature were put forth by figures like René Descartes and Isaac Newton. The world and its phenomena, including human beings and human thought, began to be understood in terms of mechanical laws and principles, laying the groundwork for comparing the brain to a machine.

The advancements in machinery and technology during the Industrial Revolution further reinforced the mechanistic view of life processes, including human cognition, making it easier to draw parallels between the operations of machines and the functions of the human brain. I recently wrote about Mary Shelley’s Frankenstein as a prime example from that period.

Fast forwarding a century later, to the 1970s, I remember watching the “Six Million Dollar Man” on TV as a kid. This show was based on a Martin Caidin novel called Cyborg depicting an astronaut who survives a plane crash and is brought to life by replacing body parts with robotics. The “Six Million Dollar Man” was soon joined by “The Bionic Woman” and episodes that featured the faces of human robots being ripped off to reveal a computer inside. Naturally, these two computer-powered bionic superpowers worked as secret agents in U.S. Office of…wait for it…“Scientific Intelligence.”

Source: YouTube

This was all occurring alongside emerging discoveries in artificial intelligence and cognitive science, further cementing the brain-CPU analogy. Like science fiction writers and directors, early AI researchers and scientists aimed to replicate human cognitive processes in computers, leading to conceptual overlaps between how brains and computers function in science and society.

The CPU metaphor provides a simplified way to understand the complex workings of the brain, making it accessible to people without specialized knowledge in neuroscience or cognitive science. This metaphor continues to be used in educational contexts to teach basic concepts about brain functions, reinforcing its prevalence.

The tendency toward reductionism — to reduce complex phenomena to their simplest components — is present in many scientific and engineering disciplines and has long contributed to the organ-as-part metaphor. Viewing the brain as akin to a computer's CPU aligns with reductionist approaches reminiscent of those early Enlightenment thinkers seeking to understand biological systems by dissecting their individual parts and drawing useful, but also isolated and simplified conclusions.

While the brain-CPU metaphor has historical roots and provides a convenient framework for understanding some aspects of cognitive function, many believe it is ultimately flawed. It can overlook the brain's integrated and dynamic nature, its entanglement within a larger biological organism, and its continuous interaction with a complex environment. These are themes under exposed and under explained in popular science, media, and most of the tech industry.

The growing recognition of these limitations, particularly within fields like 4E cognitive science (embodied, embedded, enactive, and extended cognition), is leading to the development of more nuanced and holistic models of cognition that transcend simplistic mechanical analogies.

Do we have the energy to spare our brain so we may better understand it?


The 4E framework in cognitive science highlights the brain's integrated and dynamic nature. Advances in neuroscience have shown that the brain is not a static organ with fixed functions but is highly malleable, capable of reorganizing itself in response to learning and experience. This plasticity allows for adaptability and resilience necessary for its survival, characteristics not accounted for in the rigid structure of a CPU.

The brain's structure is composed of complex, interconnected networks that support a wide range of functions, from basic sensory processing to higher-order cognitive tasks. These networks do not operate in isolation but are dynamically interacting and reconfiguring based on internal and external demands.

The brain's function is also modulated by a variety of neurotransmitters that influence mood, cognition, and behavior. This biochemical layer adds a level of complexity to brain function that is absent in computer CPUs today. This means the brain is intimately connected to the biology of the body, receiving continuous sensory inputs and sending commands to our organs and limbs. This sensory-motor coupling is foundational to cognition, emphasizing the role of bodily interactions with the world and how our brain processes it.

Research supports this concept. Cognition is something that is embodied in us, where cognitive processes are grounded in sensory and motor systems. For example, studies on mirror neurons suggest that understanding others' actions involves simulating these actions in our own sensory-motor systems.

For instance, when a child observes an adult using a tool, such as a hammer, the mirror neurons associated with the motion of hammering may fire in the child's brain, despite the child not physically performing the action. This neural activity can help the child understand the action and later replicate it, contributing to the learning process.

Another example is in the understanding of emotions. When we see someone smiling or frowning, our mirror neuron system may activate the same facial muscles involved in smiling or frowning, contributing to an empathetic response. This internal mimicry can help us to 'feel' what the other person is feeling and develop a better understanding of their emotional state.

The brain is in continuous interaction with the complex environment in which we exist. It is embedded in an environment that it continuously interacts with, influencing and being influenced by it. This interaction is not merely passive; the brain actively constructs perceptions and meanings based on environmental inputs.

This enactive perspective posits cognition arises through a dynamic interplay between an organism and its environment. Cognitive processes such as perception and action are therefore inseparable and co-determined. In the example of a child learning to use a hammer, they learn to grasp the handle not just by observing but through a process of trial and error. This involves actively engaging with the object and learning from the outcomes of these interactions thus enacting cognition through interactive processes.

These dynamic interactions are extended beyond the brain and body to include tools, like a hammer, but also computers, mobile phones, and automobiles. These tools become part of the mind's cognitive architecture. This perspective challenges traditional notions of cognition as being confined within the boundaries of the individual, proposing instead that objects and devices in our environment can function as extensions of our cognitive system when they are deeply integrated into our mental activities.

A diagram showing the scope of embodied cognition and the intertwined relationship that arise between the sciences. Source: Wikipedia

As the brains of neuroscientists interact with each other, their embedded and embodied brains are synthesizing an ever-evolving understanding of cognition that is more integral than dichotomous, more holistic than dualistic. Even as the brain employs cost-cutting simplification strategies, a rich emergent complexity emerges that further defines our cognitive reality.

The old metaphor of the brain as a CPU, once a middle 20th-century marvel, is gradually yielding to a perspective that sees the brain not as a solitary processor but as part of a dynamic, integrated system of organism and environment. As techno-optimists laud AI and digital immortality, praying to dualistic gods, the minds of some neuroscientists are extended by imaging tools powered by CPUs, presenting a model of cognition far from the mechanistic. Instead, they argue our brains are enmeshed in a dynamic and fluid biological existence.

It is here, in the flowing network of neurons and scientific narratives, that the future of understanding the human mind is taking shape. Even as I write this and you read it, we are moving our brain from the rigidly digital dualistic understanding to the fluidly enactive. In doing so, our brains are redefining our place within this emergent organism-environment system we call life using as little energy as necessary.



Knyazev, G. G. (2023). A Paradigm Shift in Cognitive Sciences. Neuroscience and Behavioral Physiology. DOI: 10.1007/s11055-023-01483-9

Newen, A., De Bruin, L., & Gallagher, S. (Eds.). (2018). The Oxford handbook of 4E cognition. Oxford University Press.

Wikipedia contributors. (n.d.). Enactivism. In Wikipedia. Retrieved February 2024, from

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