AI increasingly shapes how we think about the human mind, yet our brains may be far more complex than the technology industry assumes. A century ago, the cyberneticist Norbert Wiener observed that “the thought of every age is reflected in its technique.” For the past hundred years, that thought has been reflected in our computers, a framing readily adopted by those building artificial intelligence today.
Google’s Demis Hassabis has described the brain as “a biological approximation to a Turing machine,” while Elon Musk has stated that “people should just think of the brain as a biological computer.” Yet humans are considerably more intricate than any straightforward comparison with machines allows. As AI products push deeper into culture, this gap helps explain some of the difficulties they create. AI functions rather like a cognitive version of a hot dog: appealing in the moment, but potentially undermining the health and sustainability of cognitive systems that have evolved over several millennia.
The Computational Model of Thought
The notion that brains are computers traces back to Alan Turing. It frames thought as a three-step process that functions much like an algorithm. The mind takes in information from the world as input, manipulates it in some fashion through computation, and then enacts behaviour as output. In this view, perception leads to cognition, which in turn leads to action.
In many respects, this model has proven productive. By imagining brains as computers, technologists have advanced actual computing from simple adding machines to artificial neural networks and generative AI, seeking ever more detailed mirrors of the human mind. Yet that reflection, while not entirely wrong, is both warped and limited. John von Neumann, a seminal figure in the development of computers and computer science, doubted that the computational model could ever capture the “exceptional complexity of the human nervous system.” Our nervous systems evolved to help us navigate the vast ecological system we call “the world,” and we act upon that world to exercise what control over it we can.
A Biological Alternative
The computational approach examines the end product of the mind and attempts to reverse engineer how it functions. An alternative is to examine the long arc of evolutionary history and build a model from the ground up. This is the approach favoured by Paul Cisek, a neuroscientist at the University of Montreal, who has developed a biological model of brain and nervous system development spanning millions of years.
Cisek contends that, rather than information processors, our brains are better understood as feedback-control systems. Our bodies do not simply receive input; they take action to adjust what that input is, contingent on the options available. As Cisek is quick to note, this is not a new idea. Writing at the turn of the twentieth century, the philosopher John Dewey described the mind as a circuit, “more truly termed organic than reflex, because the motor response determines the stimulus, just as truly as sensory stimulus determines movement.”
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