The Mind/Brain Connection
It’s well-known mind and brain are very closely related. After all the main purpose of having a brain (along with regulating the body) is to create our mental states and processes. These includes perception (sight, sound, somatosensation…), recognition, meaning, thought, intellectual activity (understanding, comparison, synthesis, analysis…), emotion, the self, goals, attention, intention, motor control, and learning. In fact the brain enables all aspects of mind, conscious or unconscious. If not the brain, what else would be able to functionally connect one mental component (ex: hand) to another (grasp, cup, fingers..), or initiate & influence movement — via the signal sent down the spine?
A strong mind-brain connection has been well-established experimentally as well, by cognitive neuroscience. The main task of this field is to further elucidate this connection. This is great news — if one is interested in a brain theory. It means that defining and labeling the former, as it functions through space and time in the brain, becomes a viable path toward brain understanding. Once defined accurately, the mind’s states and processes can be mapped to corresponding brain activity. This yields functional maps: showing what the brain is enabling, when and where. This is a mind-centered way to understand (encode and decode) brain activity and signal.
There’s a big hurdle to overcome, however. The mind is poorly understood. There’s no consensus on what consciousness, or the unconscious mind, even is. What are its “parts” i.e. its cognitive ontology? How do these act and interact through space and time? And how does this activation vary: across tasks, people, environments, situations, time, learning, recent life events etc.?
A second, equally daunting problem is the so-called “easy problem of consciousness.” How do mind and brain connect? How is the mind represented inside this physical organ? How do mental states and processes connect to neural activity? What is the neural correlate of a given instance of perception, recognition, motivation, goal formation, imagination, intention, prediction and so on?
The easy problem of consciousness remains unsolved also. The exact nature of the mind’s relationship to the brain is poorly understood (Bassett & Gazzaniga, 2011). There are a number of very difficult problems standing in the way of defining the mind, and mapping it to the brain (Poldrack & Yarkoni, 2016).
The good news is there are many clues we can use to solve these two problems and understand the system. First, most agree the human mind does in fact exist. We all see, hear, feel, think, recognize, understand and so on. Attention, experience and mental activity occurs continuously, through every moment of the day.
Second, any aspect of the mind are connected to brain activity. This is obvious given the mind’s location. How could a mental state or process arise from, or be manifest within, the brain — without being connected to it? Indeed cognitive neuroscience has found neural correlates of everything they have looked for: including sensation and perception, recognition, emotion, motivation, thought, inner speech, executive control, imagination, goals, working memory, pain & pleasure, belief in God and thousands of other mental phenomena.
That the mind’s expression is represented, computed, mediated or otherwise enabled by the brain is demonstrated clearly by movement. Any aspect of mind can instantly trigger a particular movement, or affect how it occurs, via the efferent (motor control) signal. What a person perceives, recognizes, derives meaning from, thinks, feels, attends to and so on is reflected by our body language and movement, continually. The movement of one’s eyes, facial muscles, arms, hands and the rest of the body is a continual reflection of one’s state of mind. And it is clear that voluntary movement mirrors thought and intention. Human movement demonstrates the mind/brain connection in a very real (physical) way.
If the mind does have a neural correlate, and the evidence for this seems overwhelming, this is great news. It means the former, once defined with accuracy and precision, can be used to construct functional maps of the latter. The easy problem of consciousness can, in theory at least, be solved — by using a mind model as the basis for understanding, defining and labeling its neural counterpart.
Brain Signal Decoding
The (subjective and non-subjective) mind is critical to brain signal decoding. To decode a brain signal is to ascribe meaning to it, in response to a particular task. Meaning is a subjective mental phenomena. First, it’s based on subjective human opinion. It is what a group of people agree to. Second, it’s represented by mental categories and labels. These labels are asserted to represent the subject’s aspects of mind — his or her mental states and processes assumed active at that time. The more accurate and precise the labels, and the meaning they represent, the more accurate and precise the decoding can be.
For example, imagine a given set of neural activity is labeled as the mental command “remember what a ‘pear’ is.” Is it enough to label this activity as such? It’s a start. But a number of sub-labels are needed for an accurate and precise accounting of this command. These might include “I or me,” “recall from (long term) memory,” “pear shape/color/taste/texture,” “fruit,” “juicy,” “desire,” “bite into,” “nutrition,” “grows on trees” “a favorite food of mine” and so on. The more accurately the labels represent the mind during this mental command, the more accurate the decoding of that neural activity can be.
A variety of rich subjective content — perceptual, intellectual, and emotional — is manifest continually during any task or activity. Recognizing this fact is the first step toward optimal brain activity & signal decoding.
Beyond Materialism, to a Mind Model
If the subjective mind is critical to decode the brain signal, and understand the brain generally, what should be done? The first step is to acknowledge the mind/brain problem is real. Strangely enough, a hard core materialist might beg to differ. They may try to ignore the mind, minimize it by labeling it a hallucination or epiphenomenon, or “reduce it” to some aspect of the brain (such as coordinated patterns of neural activity or their “computation”). And materialism is quite popular among brain scientists.
The basic argument of materialism starts with the obvious fact that everything in the physical universe takes a physical form. Therefore the mind, since it’s inside the brain, can be “reduced” to it. Once reduced, the mind essentially disappears. The mind is the brain. After all the mental can’t actually exist within, or as part of, a physical entity.
Materialism I argue is partly correct. The human mind does in fact depend on a working brain. However it’s only half the story. Subjective awareness exists as well. It occurs every moment of the waking day. It seems obvious to me that mind and neural activity both exist — inside the brain.
Once the subjective is acknowledged to exist, the second step is work toward (or be open to learning) an accurate definition of it. In other words, work toward an accurate mind model. This may involve a rethinking of the mind from a conceptual basis, from the ground up. This may sound distasteful or a waste of time to a brain scientist who leans toward strict materialism. But the current brain-centered paradigm has yet to yield a mind model — or anything approaching one.
To be clear, great work is being done without a mind model. Valuable experimental data and knowledge continues to accumulate. Tremendous progress toward understanding the brain has been made. Yet seeing the brain as physical only — and minimizing or ignoring the mental — is a very unbalanced approach.
The Value of a Mind/Brain Model
A way to define the meaning of and categorize brain function accurately, in real time, would be of great value. The stakes are high. A mind/brain model would enhance not only academia but applied neuroscience. This includes CNS biomarkers, neurotech, AGI, knowledge representation, NLP and many other fields. The potential to help humanity is great. For example, BCI technology has the potential to greatly enhance communication and movement for those suffering from paralysis.
Yet with BCI, as with most of applied neuroscience, real-world applications to date have been minimal. BCI devices remain unreliable and scarcely used outside the lab (Chavarriaga et. al., 2016). BCI’s are not yet a viable commercial technology (Chaudhary et. al., 2021)
I argue the main problem is not technological. Though engineering challenges remain, the major obstacle to reliable, robust BCI performance is not lack of technology, engineering, brain data or knowledge. It’s the inability to define the user’s mind during device operation, and then decode and classify the corresponding brain signal. This is needed for one’s thoughts to control an external device accurately, reliably and robustly.
Here a mind/brain model has great potential. Consider neuroprosthetics As the user attempts to move an artificial limb, an intention to move in a particular way enters her mind. Motor intention (MI) thus becomes the dominant force in the user’s mind, and brain.
However, the psychological mechanisms underlying motor intention are poorly understood (O’Shea & Moran, 2017). Therefore the brain signals corresponding to this, or any, intention will be poorly-understood as well. This means the signal will be decoded and classified sub-optimally.
A mind/brain model is extremely useful. It allows defining of the user’s mind in real time, and prediction of it’s activation in the brain. Neuroimaging of this (labeled) expression yields mind, and corresponding brain signal, “signatures.” These can be used as classifiers, to decode & classify future brain activity & signals. Classifiers of aspects of min (perception, emotion, prediction…) associated with MI can also be developed. A classifier could accurately represent a MI + mind context via its signal characteristics: (ranges of) frequency, location, amplitude, band power etc.
For instance, defining the neural activity involved in the intention “reach forward to pick up that glass” rests on labeled subjective components. These include the perception, imagination, and prediction of “(artificial) arm & hand, motion toward a glass, left/right/up/down error correction, fingers in grasp position, and grasp.” Also includes are associated fatigue, frustration, impatience or any other emotion, positive or negative. Arguably dozens of components of mind are active during this (or any) intention.
The Mind is a Blind Spot
Despite its obvious importance, the subjective mind has become the single largest blind spot within the brain science community. Answering the question “what is the mind?” let alone “how does it connect, exactly, to the brain?” seems a remote goal to be achieved in the distant future. Therefore neuroscience has put the issue on the back burner, and turned its focus to the brain almost exclusively.
On one hand the decision to forego a serious investigation of the mind makes perfect sense. Why pursue a problem which seems to have no resolution, or clear path toward one? Also, current efforts to understand the brain are making (very slow, but steady) progress. The hope is the continued accumulation of brain data and knowledge will some day add up to a brain theory.
However pursuing a brain theory, i.e. a true understanding of how it enables the mind, using brain study alone I argue is a lost cause. The only way to accurately decoding brain activity is to first define the mind to which it connects. The good news is it can in fact be done, right now.
References
Bassett, D.S., & Gazzaniga, M.S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15, 200-209.
Chaudhary, U., Chander, B. S., Ohry, A., Jaramillo-Gonzalez, A., Lule, D., Birbaumer, N. (2021). Brain Computer Interfaces for Assisted Communication in Paralysis and Quality of Life. International Journal of Neural Systems v. 31. https://doi.org/10.1142/S0129065721300035
Chavarriaga, R., Fried, O., Kleih, S., Lotte, F., Scherer, R. (2016). Heading for new shores! Overcoming pitfalls in bci design. Brain-Computer Interfaces, 4, 60.
O’Shea, H., & Moran, A. (2017). Does motor simulation theory explain the cognitive mechanisms underlying motor imagery? Frontiers in Human Neuroscience, 17, 1.
Poldrack, R.A., Yarkoni, T. (2016). From brain maps to cognitive ontologies: informatics and the search for mental structure. Annual Review of Psychology, 67, 587.