The human mind remains a mystery. The brain science community — through no fault of their own — is lacking a clear, coherent, and agreed-upon understanding. Although much is known about the mind, everyone is in the dark regarding the most fundamental questions. What exactly IS the (conscious and unconscious) mind, or a mental process? What are its main components? How do these vary — across people, situations, learning, and measurement conditions? How are mental processes connected to one another — functionally and structurally? In what physical form are mental processes manifest inside the brain?
Overall, 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).
Stated another way, no one knows what the neural correlates of the mind are. This is inevitable given our current understanding. How can you identify the neural correlate of a state of mind that is poorly-defined in the first place?
The lack of mind understanding, as it operates within the brain, is I argue a major problem in applied neuroscience. On the one hand, applying neuroscience skills and knowledge to real-world problems holds great promise to help humanity. For example, brain computer interface technology, such as motor neuroprosthetics, has great potential to improve the lives of those suffering from movement disorders, such as paralysis. One the other hand, BCI devices are unreliable and scarcely used outside the lab (Chavarriaga et. al., 2016). Why? The main obstacle to high-functioning performance is not technological hurdles. Nor is it the brain. Rather it is the mind within the brain.
Consider the mind and motor neuroprosthetics. An intention to move in a particular way is a dominant aspect of the user’s mind during (natural or artificial) movement. Yet, the psychological mechanisms underlying motor intention are poorly understood (O’Shea & Moran, 2017). Therefore, its neural correlate will be poorly-defined as well. How could the neural networks that represent, or coincide with, a “pick up THAT glass” intention be defined, without listing the components involved? These components would have to include the perception, as well as the imagination, of “arm & hand, motion toward a glass, move hand to the left/right (error correction), fingers in grasp position, and grasp.”
Without these components you MAY inadvertently stumble upon and identify a particular set of neural networks during the performance of this task. Even so, you will not be able to define those neural networks! You are left with a set of vague, inaccurate, and incompletely-defined networks (such as task-defined networks i.e. “pick up a glass networks”).
The mind has become the major blind spot of the brain science community. The topic of the mind (or consciousness) is, currently, almost totally ignored. Answering the question “how is the mind expressed inside the brain?” is seen as a remote goal to be achieved in the distant future. Therefore neuroscience has put the issue on the back burner, and turned its attention to the brain. The decision to forego a serious investigation of the mind is entirely rational. Why pursue a problem where little if any progress is being made? Also, the current efforts to understand the brain ARE making (extremely slow but steady) progress. The hope is the ongoing collection of brain data and knowledge will some day add up to a brain theory.
But, as I argue above, this assumption is naive. The only way to understand the brain is to first understand the mind to which it connects. Understanding the mind allows you to understand the brain, and the good news is it can be done.
Bassett, D.S., & Gazzaniga, M.S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15, 200-209.
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.
The current lack of a brain theory, and lack of mind understanding, are closely intertwined. The (conscious and unconscious) mind is poorly-understood. And its connection to the brain is only vaguely understood. Since the brain’s main purpose is to create, represent, compute, or express the mind, the brain cannot be understood without understanding the mind.
As implausible as it may sound, Mind Brain Insights claims the solution to both problems already exists — in the form of the MA Method. The core idea is mind = a set of active general memories = a set of active functional neural network ranges.
The method is based on a more expansive view of the human mind. Cognitive neuroscience funnels and compresses the mind into a brain-based view. The brain is said to “compute” or “process” (external and internal) events, to create the mind. The MA Method broadens this view to include an obvious, yet mostly ignored, phenomenon — external experience. The conscious mind, as it is expressed within the brain, encompasses all of experience.
A person clearly understands their own state of mind includes the most salient, and attended-to, events in conscious experience. These occur within one’s visual field, including one’s body at its center. We experience our own bodies and all that surrounds it. This is how everyone throughout the world (who are not brain scientists) view the conscious mind.
The brain continually makes copies of this field (ex: that apple), which form and then shape general memories (ex: “an apple,” “apple taste,” “fruit” etc.). That general memories are built from copies of our “field of experience” is also common knowledge.
Throughout the day a person’s mind continually records their experience. This mental content shapes their general memory storehouse. Memories can be defined quite accurately. For example, whenever an apple is seen, a particular set of memories is triggered: “apple, apple taste, apple smell, food, grasp an apple, take a bite” etc. These memories can not only be listed, but connected, weighted, and labeled excitatory/inhibitory. At the same time the apple’s recognition, meaning, related thoughts, emotions, goals, attention, intention, etc. can also be defined — again as a general memory set. The mind during common, daily activity is filled with general memory (with an assist from the afferent signal & low-level perception).
The MA Method extends beyond the cognitive neuroscience conception of the mind. The mind during everyday tasks is not limited to cognitive neuroscience categories such as executive control, reward system, working memory etc. In fact the mind encompasses ALL human experience: all aspects of perception, recognition, meaning, thought, emotion, executive function, goals, intention, language, motor control, etc. and combinations thereof.
An obvious candidate for the neural correlate of an (active) general memory is a functional neural network. A FNN is a local or global, population-level neural firing event. Groups of neurons in distributed regions of the brain act in concert, via neural synchrony, to produce coordinated firing activity. Active state general memories and FNNs share the same basic characteristics. Both are highly associative. As a memory activates, component and associated memories spring to mind. As a FNN is activated it supports associated FNN activity (Bressler, 2007). Also, both memories and FNNs act, simultaneously, as a receiver of similar signals and as a transmitter of its own signal. And each is arguably the dominant feature of their respective (mind/brain) systems.
Similar FNN activity (ex: “the taste of an apple”), repeated over time, will form a range of expression. As a FNN range forms, so does a corresponding structural neural network (SNN). The latter stores the former when dormant, and works (along with a matching FNN) to recall & support it when active.
In short, once mental processes are defined as a set of general memories, they can be mapped to the brain’s functional and structural neural networks. This is mind/brain mapping based on the MIND first, as it really exists during daily living, inside and outside the lab.
Bressler, S.L. (2007). The formation of global neurocognitive state. Neurodynamics of Cognition and Consciousness. Understanding Complex Systems, 61. https://doi.org/10.1007/978-3-540-73267-9_4
BCI is widely understood to be unreliable and generally ineffective outside the lab. One obvious reason for this is the brain signal controlling the device is not optimally understood or classified. This is far from just a technological hurdle. It’s true that as neuroimaging and other capabilities to gather, and process, information from the brain improve, signal classification will be enhanced. So will gathering more brain data and its ability to add to our store of brain knowledge.
However the biggest potential improvement by far, I argue, involves the mind. There is great untapped potential here. The human mind – our consciousness, and unconscious processes — is currently neither well-understood nor well-defined. And its relationship or connection to the brain is equally murky. But what if the mind could be clearly and accurately defined, and mapped to the brain?
Consider someone working on an applied neuroscience project, such as a BCI and motor neuroprosthetic research group. Imagine they were to classify a brain signal of the user’s intention “reach forward to grasp a cup.” To begin, there is no consensus on what aspects of mind are best to try to classify. Should it be the intention “(me) reach forward? or “my arm reach forward?” Should associated motor imagery be the main target of classification? Should a prediction (I will soon reach forward?) be included? What about the accompanying perceptual experience (visual and somatosensory) and recognition — of “my arm,” its motion, and what it is reaching toward?
Or, maybe an overall brain signal that (typically) occurs during this task should be the target of classification. In other words, maybe the best thing to do is to ignore the mind altogether and classify the signal based purely on the type of task being performed (task-based signal classification).
Seeing the mind as generating the brain signal, and defining the mind accurately are, I argue, two overlooked yet crucial components of classifying the brain signal. After all, no two states of mind during a movement type (or any other task) are alike. Each will have its own set of associations: of perception, emotion, imagination, intention, etc.
The human mind is currently poorly-defined; which is a major shortcoming giving its tremendous variability. However, there is some great news: it CAN be defined accurately, given a correct mind/brain theory! This will greatly enhance brain signal classification, given the brain signal is a direct reflection of the mind.
Despite significant progress, it’s well-accepted the reliability and overall effectiveness of BCI technology remains limited, especially in the real world. How might an improved understanding of mind, and brain, help solve this problem?
The brain is understood to be a central component of BCI control. The user’s brain signal is analyzed, processed, decoded, and/or classified. The end result is a user’s intended movement, or other external action.
But, what controls the user’s brain signal? Some might say task determines the signal: its location, frequency, amplitude and other characteristics.
I argue task is a secondary variable. The primary, and most direct, variable affecting the brain is the user’s state of mind: their perception, meaning, emotion, imagination, attitude, motivation, intention etc.
The mind is influenced — often strongly — by the task in which it is engaged. But the mind, or mental processes, are also affected by much more that task — including environment, measurement situation, recent life events (good and bad) and other context. This context includes not just external factors but the mind itself: excitement, reward, frustration, fatigue, hunger etc. A plethora of external and internal variables affect the user’s mind..
The good news is the mind can be controlled to a large degree. If a user’s mind is activated with conscious intent, matches the classifiers being used, and the brain signal processed and classified accordingly, the desired action will occur. Mind Target “Hit” → Functional Neural Network Activity → Brain Signal → Processing & Classification → (Desired) External Action.
For example, imagine a user’s mind were dominated by an imagining and intention to “move the cursor to the left.” In this case the corresponding functional neural network activity, and brain signal, would then be classified as “move cursor left.” This in turn triggers the desired action.
What if the mind could be defined with greater accuracy, precision, and completeness than the current cognitive neuroscience paradigm allows? This would enable more accurate, precise, and comprehensive classification of these signals.
More importantly, a group could use improved mind/brain signatures as “targets.” The entire team — BCI designer, tester, trainer, and user — could use these to improve their performance. For example, for the user, the targets coiuld be more clear, precise, and personalized; desirable for the user to strive toward: easier to hit (strongly and consistently); and adjusted for specific movements, situations, and other context.
The contents of the conscious mind, contrary to what many brain scientists think, are not mysterious or difficult to understand. They are the same as our experience of life. All of our awareness, moment by moment, is encompassed by our mind, as it changes (or stays the same) inside the brain. The good news is these contents can be defined — or at least summarized – quite accurately.
The MA Method defines the mind as a set of active general memories, based on past experience. For example, when a user tries to moves a cursor to the left, a “me moving a cursor left” memory will activate. This memory is comprised predominately of (visual & somatosensory) perception: of “my arm & hand moving to the left,” a computer screen, a cursor (a range of cursor size & shapes), and cursor moving left (a range of cursor speeds and directions). These mental contents will activate as part of the larger memory “me moving a cursor left” (itself a general memory).
A memory range = a general memory. For example the general memory “an apple” includes a range of apple characteristics: shapes, sizes, colors, tastes, etc.
The neural correlate of a general memory, I argue, is obvious: a functional neural network (FNN) range. Both are highly associative, function — simultaneously — as signal transmitters and receivers, and dominate their respective (mind/brain) systems.
Overall, a state of mind = (mostly) an active general memory set = (mostly) an active set of FNN ranges = (most of) the corresponding brain signal.
Why label the brain signal based on task when it can be more accurately labeled based on the mind? In fact, I argue it’s possible to define, connect, and weight a memory set — its components and associations — for any common movement & its context. Movement context would include the person, occupation, lifestyle, movement situation (environmental, social, work/home/lab…), and stage of learning (beginner/intermediate/advanced).
Overall, brain signal targets — based on a new understanding of the mind, not just the task — have the potential to greatly enhance BCI design, testing, learning, and use.
Imagine cognitive neuroscience has progressed to the point where the mind i.e. mental processes could not only be defined, but mapped to the brain — with accuracy and precision. In other words, imaging the existence of a workable mind/brain theory. How might this empower a BCI user?
The primary advantage of accurate mind-to-brain mapping, for a BCI device user, is it would enable construction of better mind/brain “targets.” These targets are the states of mind the user tries to achieve i.e. “hit” while commanding a BCI device.
Obviously the user can control (within limits — of task, environment, situation etc.) her state of mind during BCI operation. Because of this control she can also define, beforehand, her target states of mind. She can research and ultimately arrive at personalized targets: ones that fit her lifestyle, occupation, social habits, and personal goals. She could define her most desired, and readily-achieved, states of mind.
Self-chosen states of mind make the easiest targets to hit. Why? Because they are defined by someone who knows the user best — the user herself! For example, if the user is a Zen practitioner, “being mindful and focused on the task, moment-by-moment, during (BCI) movement” could be a target. If the user was a professional researcher, “moving while thinking and staying calm” might be a target.
Personalized mind/brain targets could be bolstered by the user’s own research efforts. For example, the user might learn it’s difficult to maintain a constant level of excitement, and dopamine-based brain signal, when one is both “failing” and “succeeding” at a task (such as device control). Social situations would magnify this emotional rollercoaster. To allow for this emotional noise, the user might define a target state of mind that includes “emotional variability.”
On the other hand, the user might be a devoted Buddhist who is (or strives to be) unattached to results. In this case, she could define her emotional target as “an even level of excitement while moving.”
Overall, optimal mind/brain targets can be researched, considered, discussed, and defined by the user and her clinical and professional team. Targets could be personalized to account for occupation, lifestyle, and other variables. They could also be activated flexibly according to the demands of the situation. These targets would be hit with much greater strength and consistency because (1) they are easier to hit, (2) the user is motivated to achieve these states of mind, and (3) she has a clear idea of what the targets are in the first place!