Do We Need To Study The Brain To Understand The Mind?
The brain is the most complex object in the known universe. Some 100 billion neurons release hundreds of neurotransmitters and peptides in a dynamic spanning timescales from the microsecond to the lifetime. Given this complexity, neurobiologists can spend productive careers studying a single receptor. Might psychologists more productively understand the mind by ignoring the brain altogether?
Marr (1977) suggested that mental processes may be studied at three levels of analysis: computational (the goals of the process), algorithmic (the method), and implementation (the hardware). The separation implies that the same computational goals and algorithms may be accomplished by a human brain or a computer, and the physical medium—neuron or silicon—is irrelevant. This concept was fundamental to the cognitive science movement and has given its practitioners permission to comfortably ignore the brain. But it has been seriously challenged: A high-level computation (e.g., deciding the next move in a chess game) can be accomplished in a virtually infinite number of ways. Building a computer model that accomplishes the computational goal says little about whether it does so in the same way that a human would. The hardware provides critical constraints on the space of possible models.
The debate about whether we need to study the brain to understand the mind is now being conducted among a network of thousands of scientists and scholars worldwide. The emerging consensus appears to be that implementation is important. Interestingly, the inverse question is also being asked by neurobiologists—do we need consider the mind to understand the brain?—and answered largely and increasingly in the affirmative.
We can learn much about the mind without knowing a neuron from an astrocyte. As I often repeat to myself and occasionally to others, “If you want to understand human performance, study human performance.” But brain data provide information about the mind that cannot be gleaned from even the most careful studies of behavior. In short, brain data provide a physical grounding that constrains the myriad otherwise-plausible models of cognition. They give us a direct window into which mental processes involve similar and different neurobiological processes, allowing us to use biology to ‘carve nature at its joints’ and understand the structure of mental processes (Kosslyn, 1994). Brain function also provides a common language for directly comparing and contrasting processes that are otherwise ‘apples and oranges,’ such as attention and emotion. This common language is a basis for the integration of knowledge across different types of research—basic and clinical, human and nonhuman.
As the general uses of neuroimaging have been eloquently discussed elsewhere, I focus here on a few examples of how functional magnetic resonance imaging (fMRI) has been useful in my work (see Jonides, Nee, & Berman, 2006). Also, as every method has its limitations, I discuss some of the pitfalls of making psychological inferences from neuroimaging data.
One use for me has been in understanding the structure of emotion and executive control processes, and the ways in which cognitive control operates in emotional and nonemotional situations. My colleagues and I have asked: Is pain different from negative emotions such as sadness and anger, or are they variants on a common theme? In meta-analyses we have found that pain and negative emotions activate distinct brain networks, but share features such as anterior cingulate and frontal cortex activity with a broader class of processes, including attention (Wager & Barrett, 2004; Wager, Reading & Jonides, 2004). In contrast, different varieties of negative emotion engage largely overlapping networks. Thus, pain appears to be distinct from negative emotion, but commonalities suggest ways in which they may share underlying processes such as heightened attention.
Questions about the similarity and distinctiveness of mental processes have been at the heart of psychology since its inception, but definitive answers have been elusive. Inferences have been based largely on correlations in performance across tasks (or in physiological responses, for emotion). But performance data are relatively information-poor: the fact that two tasks take about as long to complete says little about whether processes involved in selecting the response were the same. Physiological responses suffer from similar problems of specificity. Neuroimaging provides a much richer source of information: if two tasks activate the same brain regions to the same degree, they are likely to involve similar processes. This logic provides a way to assess the structure of mental processes based on the similarity of their brain activation patterns. In a study based on these principles, we asked whether diverse ‘executive control’ tasks involve a common brain substrate (Wager, et al., 2005). Substantial overlapping activation suggested a common network for controlled response selection.
Though questions about mechanism are more difficult to address, neuroimaging can be informative here as well. In an fMRI study of pain, my colleagues and I found that expectation of pain relief induced by a placebo engages the frontal cortex and midbrain pain-relieving mechanisms (Wager et al., 2004). Frontal activation suggests a common substrate for maintaining cognitive context that shapes both perceptual/motor and affective processes, and midbrain activation suggests engagement of opioid analgesic systems. Such direct evidence on the mechanisms by which expectations affect pain would be hard to come by without studying the brain.
The study also points to an additional benefit of neuroimaging: In cases where self-report may be inaccurate, imaging can provide converging direct measures of central processing of a stimulus. Whereas expectations might affect pain reports for uninteresting reasons related to cognitive reporting bias, the evidence that expectations affect ongoing pain processing provides converging evidence that they shape pain experience.
Yes, there are many ways in which neuroimaging data can be misused or misinterpreted. Gross levels of regional brain activity might in some cases be uninformative about the similarity of psychological tasks: Two dissimilar tasks may involve the same regions but use different populations of neurons or involve different patterns of connectivity between regions. Two similar tasks might involve different regions but involve the same type of computation. Neural activity may be missed, as observed imaging signal only indirectly reflects neural activity, and observed imaging activation may not be essential for the task.
One of the biggest pitfalls is the temptation to observe brain activity and make inferences about the psychological state—for example, to infer episodic memory retrieval from hippocampal activity, fear from amygdala activity, or visual processing from activity in the ‘visual cortex’ (Barrett & Wager, 2006; Poldrack, 2006; Wager et al., in press). These inferences ignore the scope of processes which may activate each of these areas and involve a fallacy in reasoning: “if memory then hippocampus” is not the same thing as “if hippocampus then memory.” The fact that few brain areas, including the ‘visual cortex,’ are dedicated to one process means that self-report is still the gold standard for assessing emotional experience and the contents of thought (Shuler & Bear, 2006). This is a serious challenge for those who would like, for example, to assess your brand preferences or your political affiliation from a brain scan. (And isn’t it easier just to ask?)
These problems are significant, but there is no perfect method—an understanding of the mind must emerge from a coordinated effort using converging evidence from all the tools at our disposal. Many of the issues above are being addressed by advances in data acquisition and analysis methods, the accumulation of more data on the mapping between brain structure and psychological function, and more nuanced views of what kinds of inferences are plausible. I believe that as the field matures, the exuberance of youth will give way to a more level-headed view of when and how neuroimaging can inform us about the mind. What we have learned already is considerable, and the accelerated integration across fields is leading to ever more and sophisticated and veridical models of the mind.
Barrett, L.F. and Wager, T.D. (2006). The structure of emotion: Evidence from neuroimaging studies. Current Directions in Psychological Science, 15, 79-83.
Jonides, J., Nee, D.E., Berman, M.G. (2006). What has functional neuroimaging told us about the mind? So many examples, so little space. Cortex, 42, 414-427.
Kosslyn, S. M. (1994). Carving a system at its joints. In image and brain: The resolution of the mental imagery debate. Cambridge, MA: MIT Press.
Marr, D. and Poggio, T. (1977). From understanding computation to understanding neural circuitry. Neurosciences Res Prog Bull, 15, 470-488.
Poldrack, R.A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10, 59-63.
Shuler, M.G., Bear, M.F. (2006). Reward timing in the primary visual cortex. Science, 311, 1606-1609.
Wager, T.D. and Barrett, L.F. (2004). From affect to control: Functional specialization of the insula in motivation and regulation.
Wager, T.D., Reading S., Jonides, J. (2004). Neuroimaging studies of shifting attention: A meta-analysis. Neuroimage, 22, 1679-1693.
Wager, T.D., et al. (2005). Common and unique components of response inhibition revealed by fMRI. Neuroimage, 27, 323-340.
Wager, T.D. et al. (in press). Elements of functional neuroimaging. In J. Cacioppo and R.J. Davidson (Ed.), Handbook of Psychophysiology. Cambridge, MA: Cambridge University Press.
Wager, T.D., et al. (2004). Placebo-induce changes in fMRI in the anticipation and experience of pain. Science, 303, 1162-1167.
suppose a triangle, one angle at the top, the most abstract parts of a theme are in the top angle. thus vertically increasing absctract-level.
You are trying to study at the baselinelevel, real particular data etc. This is irrelevant in understanding. Understanding is abstraction in the first place, so modelling using surrounding knowledge.
I want to show you how I see this.
Brains hardware is dominant in software possibilities. Hardware modelling helps understanding the software, the mind.
Suppose your car engine brakes down and you do not really understand how such an engine works. You can try to think, to theoretisize on the cause of the breakdown, but without engine knowledge the outcome will be poor. Also, when other unprofessionals gather around your car and try to help, the outcome, again is hopeless.
The same is true for psychology, depression, learning, personality, schisofrenia etc, all symptom hunting without basic knowing.
There is no alternative to generalized modelling of human mental functioning and that is what you will have to do.
In order to start, I want to give you some ideas to progress on, or to modify in your way.
Before so, some words on science in general.
Science is to condense the vast amount of data in a field.
A / theories, laws etc., as used e.g. in physics
B / categorization, as used in biology e.g. families of plants, animals
C/ statistics, condensing large amounts of simple date, and simple relations (without direction) as done by a national bureau of statistics.
D/ Modelling as used for predicting the economy of a country
The psychology community use mostly statistics, (zero help in the direction of general mental understanding) or symptom theories mostly based on schools like behavior, or cognitive (cognitive here as open, empty, do it yourself contents).
I want to use modelling (brains hardware), and categorization (brains software) .
Part 1, a model of the hardware function
Part 2, a model of the attitudes system
Part 3, a model of the capabilities (learned how to do)
The signal processing in the brains is extremely slow, not gigaherz like in computers, but about 40 Herz, (so 40 steps per second) because of the nature of the conductors and switches (electrochemical, salt-ions).
Yet the response speed is very fast. If a say car, you immediately know , that this item exists and what it is etc. This speed can only be performed by a specific model of brains organization.
Suppose a playground of a large school, with 20 million children.
A teacher wants to speak to Tommy 30.000.
A computer would transport children to the teacher, the teacher would see if this child was Tommy 30.000 and so on, so extremely many steps.
1 /In the human case, the teacher shouts :Tommy 30.000 and Tommy 30.000 would respond, Yes, here I am. So 2 steps. This is the first principle, distributed intelligence.
2/It can only be done if the data in the human brain are organized in a fixed way.
If a question is put on the ask bus, it can only be meaningful if all listeners use the same format.
3/ All data, e.g. on language, moving, thinking, etc. are organized in the same way.
The conscious system uses a small amount of broad busses for communication, this can only be efficient if all mental jobs use the same busorganisation.
4/ more databusses are in use, ask and response.
5/ the conscious system is organized in the same way, using a small group of taskmanagers, each with busses. The control of these multitasking environment can block, maybe by stress (too long blocking on one task). This I consider schisofrenia. So maybe early schisofrenia could best be treated by organizing constant taskrotation programs.
6/ Brain data is organized in a hierarchical way.
Think of moving e.g. dancing. If you know some basics, moving your right leg sideways etc, you can combine these basic units to sequences.
If you know words, you can combine these units to phrases
If you know intelligent stories, you can combine these units to other intelligent outcomes.
The conscious system is however to slow to really construct complete new things. If you talk it gets the phrases form your phrasecollection etc. If you move it gets your moveinstructions from your move-elements-collection etc.
The hierarchical part is the place in the row, like money on the bank. 1 million and 1 dollar on the bank means 2 times a 1, but the one 1 stands for a million and the other for a single 1.
The same must be true in human mind .
7/ Information is modelled before businteraction takes place. Eg a tilted picture is first put upright, separated in relevant parts and then bussed.
8/ learning is filling the memory, the idea that students should only understand, or would be able to find (on the internet) is nonsense. Information can only be used if it has its place in memory, the power of conscious thinking (= using comparable stories out of memory) without memory filling is very limited.
9/ finding and newfinding by the conscious system uses only the most important part of the bus (like the 1 in 1 million), the group of hits is automatically evaluated before consciousness.
Part 2 The attitude system.
The software and the hardware system of the brains must be compatible, so using similar organization. ( = hierarchical)
I regard the attitudesystem as another word for personality.
Consider a triangle, with one angle on top. In the top, the most aggregate level, the most generalized level, the most abstract level, you will find ideals, beliefs , masterknowledge etc. It directs all lower abstraction levels.
The attitudesystem is organized in a number of areas, with some crossinterference.
Eg 1/ politics Most people, if politically interested, can be statistically divided in a small number (less than 10) political streams, like liberal, social, green etc.
Once belonging to one such stream, everything else is of little importance in politically relevant themes. Intelligence, amount of information, etc.
The top level ideal is top important regarding attitude.
Eg 2/ the marriage relation
The triangle as introduced above, gives input, driving forces towards attitudes, behavior etc.,. One could mirror this triangle along the baseline, creating a second, downward triangle. This triangle contains the emotional output, also organized in more and less aggregate levels.
Again, like in the former theme, one could divide people at first relation beginning in a small number of statistically idealgroups.
Over time, things happen and these may severely damage the upper levels of beliefs, hope, and ideals. The problem of relationmanagement on lower abstraction levels is that it is more and more trending towards a relation, not being special but normal , average, not valuable .
Without the source, life organization as you would like, is a problem. And how much ideals and hope are left for a next relation, how much damage.
Of course such damage is reflected in the emotional results part.
A crosstheme interference example can be seen when eg the woman has strong justice feelings in the woman-man rights environment. Being militant here must rain in the marriage relation, sometimes it may rain hard.
Eg 3 religion. Clear enough.
Eg 4 justice in the personal environment
Eg 5 life perspective and life planning
Eg 6 sexuality
When in a number of theme hopes, beliefs ideals are damaged, or failed to build up in the first place, life gets less valuable, less organized (religion gives organization, politics school gives organization, marriage hope`s and ideals give organization). I consider this depression.
Most therapist´s work around the baseline, what feelings what attitudes, what coping capabilities. In my opinion no solution.
Rebuilding the values system , maybe not easy, but the powerway.
Part 3 Capabilities system
Simply enough , so I only give a small example.
Small talk, simple conversations to a wide range of people.
As stated in the hardware part, we do not construct the complete dialogues during the conversation, we are way too slow for such thing. No, we get conversation subparts from our conversation database and mix such parts as needed.
Suppose now, this database is poorly filled in the optimal times (eg youth) for this.
Than you have a problem, so avoiding, feeling not to good etc.
Trying to comfort such person won`t help, the problem is not an anxiety problem, but a databasehole.
So filling , using learning with a number of times the same prewritten texts dialogues.
It has been argued that we will never be able to fully understand how the brain operates because doing this involves using the brain to study itself. What do you think of this argument?
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