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.