How do our brains make our minds?
In his new magnus opus, Conscious Mind, Resonant Brain: How Each Brain Makes a Mind, Stephen Grossberg answers some of the biggest queries in the field, such as: How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it?
Even knowing how to begin this quest is difficult, because our brains look so different from the mental phenomena that they support. How does one make the link between the small lump of meat that we call a brain and the world of vivid percepts, thoughts, feelings, hopes, plans, and actions that we consciously experience every day? How can a visual percept like a brilliantly colored autumn scene seem so different from the sound of beautiful music, or from an intense experience of pleasure or pain? How do such diverse experiences get combined into unified moments of conscious awareness that all seem to belong to an integrated sense of self? What, after all, is consciousness and how does it work in each brain? What happens in each of our brains when we consciously see, hear, feel, or know something? And why, from a deep theoretical perspective, was evolution driven to discover consciousness in the first place?
You might immediately wonder: If these discoveries are so simple that they can be turned into stories, then why has it taken so long for them to be made? After all, in one sense, the answer that we are seeking is simple: Our minds emerge from the operations of our brains. Such an answer is, however, profoundly unsatisfying, because our conscious awareness seems so different from the brain’s anatomy, physiology, and biochemistry. In particular, the brain contains a very large number of cells, called neurons, that interact with one another in complex circuits. That is why many people in Artificial Intelligence, or AI, thought for a while that the brain is designed like a digital computer. Some of the greatest pioneers of digital computer design, such as John von Neumann, drew inspiration from what people knew about the brain in the 1940s. Very few people today, however, believe that the brain operates like a digital computer. It is quite a different type of system.
Knowing that your brain is not like the computer on your desk, or more recently in your hand, is a comfort. There seems to be more to our mental lives, after all, than just a morass of operating systems and programs. But what we are not does not teach us what we are. It does not, in particular, help us at all to understand how the brain’s networks of neurons give rise to learned behaviors and introspective experience as we know it. How can such different levels of description ever be linked?
Knowing that your brain is not like the computer on your desk, or more recently in your hand, is a comfort.
I would argue that it has taken so long to begin to understand how a brain gives rise to a mind in a theoretically satisfying way because, to achieve this, one needed to first create a new scientific paradigm. This paradigm concerns how autonomous adaptive intelligence is achieved. As I will discuss throughout the book, this is a topic that is just as important for understanding our own minds as it is for the design of intelligent devices in multiple areas of computer science, engineering, and technology, including AI.
The discoveries that contribute to this paradigm have required new design principles that unify multiple disciplines, new mathematical concepts and methods, major computer resources, and multiple experimental techniques. I will write more about what this paradigm is below, when it began, and why it has taken so long to develop. In brief, this paradigm has to do with properties of our lives that we take for granted, like your ability to continue learning at a remarkably fast rate throughout life, without your new learning washing away memories of important information that you learned before. I have called this fundamental property the stability- plasticity dilemma. Many gifted colleagues and I have been vigorously developing the theoretical and mathematical foundations of this new paradigm since I began in 1957, as summarized in my YouTube lecture for SEP.
However, if a brain were just a bag of tricks, then it would be difficult, if not impossible, to discover unifying theories of how brains make mind.
The difficulty of solving the mind-body problem, which ranks with the greatest problems ever considered by scientists and philosophers, has led many distinguished thinkers to despair of ever being able to explain how a mind emerges from a brain, despite overwhelming experimental evidence that it does. Some distinguished scientists have suggested that the brain is a “bag of tricks” that has been discovered during many cycles of trial and error during millions of years of natural selection (Buckner, 2013; Ramachandran, 1985). Natural selection has indeed been understood to be the dominant force in shaping the evolution of all living things since the epochal work of Charles Darwin (1859) on the origin of species. However, if a brain were just a bag of tricks, then it would be difficult, if not impossible, to discover unifying theories of how brains make mind. The work that my colleagues and I have done contributes to a growing understanding that, in addition to opportunistic evolutionary adaptations in response to changing environments, there is also a deeper level of unifying organizational principles and mechanisms upon which coherent theories of brain and mind can securely build.
Stephen Grossberg is the world’s foremost pioneer and current researcher who introduces and develops neural network models and mathematical methods for both biological and artificial intelligence. More generally, Grossberg is the world’s leading scientist/engineer who discovers and models neural design principles and mechanisms that enable the behavior of individuals, or machines, to adapt autonomously in real time to unexpected environmental challenges. To acknowledge these contributions, Grossberg received the 2015 Norman Anderson Lifetime Achievement Award of the Society of Experimental Psychologists, the 2017 Frank Rosenblatt Award of the IEEE Computational Intelligence Society, and the 2019 Donald O. Hebb Award of the International Neural Network Society.
