Inspired by PhD by haiku, vol 1: OnCirculation
For more than 17 syllables on this topic, see this chapter: How Languages Construct Time by Lera Boroditsky.
Where does cognition come from? The most common claim is probably that cognition resides in the brain. But that can’t be enough. The idea of a bodiless brain is pretty disturbing – could a brain in a jar produce true cognition? The externalist (embodiment) philosophy maintains that the interaction among brain, body, and world is crucial; these three components together give rise to the mind, and no one is sufficient on its own.
The brain is the most obvious contributor to cognition. Neuroscience research is based in the idea that a better understanding of the brain will bring about a better understanding of thinking and behavior. Fuster demonstrates his belief in the primacy of the brain by claiming “our memories are networks of interconnected cortical neurons, formed by association, that contain our experiences in their connectional structure” (451). His use of the verb “to be” instead of phrases like “are created by” or “result from” exemplifies his commitment to the preeminence of the brain. Fuster explains complex cognitive phenomena in terms of their underlying brain events. For example, he describes working memory as a temporary activation of a network of perceptual and motor memories, or, more simply, as the product of neural events.
A number of researchers opposed to this brain-centered view advocate for the importance of the body in cognition. After all, every brain is situated in a body. Ballard emphasizes the vitality of the body. Specifically, he points out the central role that they eyes can play in a number of tasks requiring working memory. In one experiment, he showed participants a structure made of a number of different blocks. They had a resource area containing the same blocks they would need to duplicate the model, and were asked to do so in a separate workspace area. As they moved blocks from the resource area to the workspace area, participants looked back at the model much more than they should have if they had stored a representation of the model in working memory. In fact, the most common strategy was to look from the model, to the pick-up area, back to the model, and then to the drop area for each block. Thus, Ballard concludes, subjects used fixation as a deictic pointing device presumably because the computational costs of storing the model in memory (a cognitive task) were greater than the on-line costs of shifting their eyes. In sum, the paper provided a unique example of the connectedness of the physical body and the brain’s processes.
Just as brains are situated in bodies, bodies are situated in the world, so it is unsurprising that the world is likewise crucial for cognition. Spivey uses eye-movement experiments to demonstrate the role of the external visual environment in numerous types of cognition. In one especially persuasive experiment, participants listened to descriptions of spatiotemporally dynamic scenes while facing a large white screen. For example, they might hear a description of events unfolding in a skyscraper. Narration might start talking about an occurrence on a lower floor and continue describing events higher up sequentially. Even though subjects were looking at a blank screen, their eye movements corresponded with the direction of the description (in the case of the example, their eyes would shift upward with the description). Spivey argues that the physical eye movements are an integral part of language processing, and more broadly that physical movements enable many types of cognition.
Kirsh also presents numerous real-world examples of people’s use of their physical environments. Drawing from instances of people cooking, packing groceries at the supermarket, personal workshops and playrooms, and playing Tetris, he shows that the spatial arrangements we create in our own environments help simplify choices, perception, and computation. We simplify choices, for example, when we lay vegetables that need to be washed next to the sink because the proximity of the items makes the desired action (washing) more salient and undesired actions (like chopping) less salient. One way we simplify perception is by symbolically marking an object. He gives the example of a woman who, after measuring an amount of butter and cutting the stick in two, laid her knife on the measured piece to act as a sort of reminder. Finally, a Tetris game demonstrates our ability to use our environment to simplify computation. Approximately 800 to 1800ms after a zoid enters the screen, people display a burst of rotations, presumably because actually rotating the zoid takes less time than mentally rotating it. Thus, he concludes, humans use a variety of strategies to optimize their environments for cognition in many tasks.
Mind can emerge only when brain, body, and world come together. In this sense, “mind is best measured by its capabilities, not by its capacities” (Spivey, p. 183). Although the brain is undeniably an important contributor to thought and behavior, the physical presence of neurons and their connections does not alone constitute cognition. The bodies in which brains are found and the worlds in which the bodies are found are also crucial components of the human mind and cognition. Evidence from a variety of contexts has shown the importance of embodiment, or as Spivey concisely noted, “it might just be that your mind is bigger than your brain” (p. 162).
But what if mind just IS matter???
A recent assignment had me revisit Searle’s thought experiment referred to as “the Chinese room” and the debate of whether machines can understand as humans do. He puts forward this scenario: He doesn’t know any Chinese, but is sitting in a room with reference books that allow him to produce coherent written responses to any message he’s given in Chinese. Thus, the person outside the room receiving his responses will believe that the person understands Chinese, when in reality, there is no understanding going on. Searle uses this as an analogy for computer programs- even if a computer program can produce logical or correct or sophisticated outputs, it doesn’t truly understand what it’s computing. Computers simulate intelligence but simulation is not sufficient for true consciousness or to be considered a “mind”.
Searle first wrote about the Chinese Room in 1980, but when I just recently read about the deep learning, the most current advance in artificial intelligence, I couldn’t help think that the Chinese Room is still quite pertinent. The process of deep learning is accomplished by a network in which connections among concepts. The smallest concepts form one layer, and above them are slightly larger ones, and so on, so that to retrieve information, the network doesn’t have to search a massive pool of data, but instead has to find the right associations between data (a much less onerous computational task). The name “Deep Learning” alone is telling to me because typically only humans, or agents with human cognitive capacities, can truly learn. If it helps make my Facebook newsfeed more interesting (i.e., excludes status updates from those random people from elementary school I’ve just never gotten around to unfriending), that’s great. But have technological innovations like this one brought us closer to a feeling that true artificial understanding is achievable (or already achieved?)
I just read an interesting post by Christian Jarrett called “‘My Brain Did It!’ Neuro Talk in Everyday Conversation.” He comments on how with the increase of scientific information regarding our brains, people use more and more language that suggests their brain is an autonomous, computer-like agent, separate from their bodies (things like “this menu confuses my brain” or “I can feel my brain whirring”). Perhaps, one researcher has suggested, language like this reflects people’s materialistic beliefs that the mind is reducible to the brain. In other words, they think that their thoughts, feelings, and emotions are equivalent to the physiological processes occurring in their brains. There’s also the possibility that an increase of language that suggests that mind=brain will influence people’s beliefs about their minds and their selves.
This is interesting, but I’m not convinced (about either). The possibility was also mentioned that we talk about our thoughts and feelings by referencing the brain for pragmatic reasons- it can facilitate getting our point across in certain circumstances. This seems more likely to me, first because I think most people actually hold a dualist view of the mind, one that contrasts pretty starkly with the mind=brain viewpoint. People who believe in an afterlife or those who believe in paranormal activity have to believe that there is more to our minds, or our souls, than just the physical brain.
Another reason that I don’t think language about the brain reflects speakers’ beliefs about their minds is that we talk about other body parts in similar ways, but we certainly don’t believe that they operate in isolation. We often say that our heart is breaking or that our stomach wants food, but we know that these organs don’t actually break or have desires.
It does seem like there could be some cool research opportunities here. Do some people use materialist-sounding language more often than others? And does their language use correlate with their beliefs about the mind? Actually, does the average person (who might say something like “my brain said, ‘you can do it,’ but my body said, ‘no, you can’t’) even have beliefs about whether his mind is reducible to the brain?
And the idea that we might be able to understand what’s going on when we have certain thoughts and emotions and even to induce them is really seductive.
But it leaves out context, the most crucial ingredient in understanding the mind. fMRI scans are necessarily done in a lab, specifically in a really noisy, claustrophobic machine. Personally, most of the thoughts and feelings I have in life don’t occur in that environment. They occur in real-life situations, with other people, and in situations in which I’m not aware that I’m being scrutinized. Without a doubt, fMRI data teach us a lot about the human brain and some correlates of thoughts and emotions, but it’s not the single explanation for all that goes on in our minds, as many people wish and expect it to be. Satel writes that “mechanism is not meaning. The brain creates the mind through the actions of neurons and circuits, yes, but it cannot reveal its nuanced contents.”
If we want to truly understand the thoughts and feelings that make us human, we have to look beyond the pervasive pretty rainbow pictures of “brain porn” that may at first seem enticing, but in the long run won’t bring the satisfaction we’re looking for.
Put concisely, I loved it. Mario Beauregard challenges the dominant materialistic view among scientists that mind = brain. He presents a number of phenomena that can’t be explained by materialism, such as placebos, neurofeedback, meditation, psi, hypnosis, and out of body/near death experiences. All of these are examples of ways that thoughts, beliefs, and emotions can influence what happens in our brains and bodies and affect our heath and well-being. In other words, our physical bodies don’t seem to be wholly determined by our physical brains.
“Mental activity is not the same as brain activity, and we are not “meat puppets,” totally controlled by our brains, our genes, and our environments. Indeed, our minds and our consciousness can significantly affect events occurring in the brain and body, and outside the body. We do have these immensely important capacities, and it is time for science to begin taking them seriously.”
For a while I’ve felt torn regarding the mind-brain relationship. If scientists didn’t believe that studying the brain would inform them about the mind and consciousness, why would they study it? But at the same time, people are drawn towards the idea that there is more to the mind than simply the physical brain, or else they would not practice religions geared towards life after death, when the mind transcends the physical. It’s always felt like a bit of an impasse.
Beauregard’s claim is that the mind and the physical world aren’t actually separated, but instead they just appear to be. They are distinct but complementary aspects of one reality.
In the conclusion, he talks about what the brain sciences can learn from Quantum Mechanics:
“The work of QM has effectively dematerialized the classical universe by showing that it is not made of minuscule billiard balls, as drawing of atoms and molecules would lead us to believe. QM has shown that atoms and subatomic particles are not really objects- they do not exist with certainty at definite spatial locations and definite times. Rather they show ‘tendencies to exist,’ forming a world of potentialities within the quantum domain.”
In the future, he argues, we won’t make progress in understanding the relationship between the brain and mind until scientists can shed their insistence on materialism, which causes them to “neglect the subjective dimension of human experience and downplay the importance of mind and consciousness.” When we have firm expectations about what we’ll find when approaching a research problem, it comes as no surprise that those expectations are met. If we want to understand the human mind as fully as possible, on the other hand, we have to be more open to different possibilities.
With my college graduation just days away, it’s only natural that I’ve been doing quite a bit of introspecting: In what ways am I different from the 17-year old my parents dropped off at Vassar in 2009? How do my current beliefs and thoughts differ from those I had as I began my freshman year, and what aspects of my education have contributed to those developments? I think back to many of the classes I’ve taken over the four years: French, Latin, and Chinese, computer science, physiological psychology, the history of the English language, and anthropological linguistics come to mind. I feel that cumulatively, regardless of whether they counted towards the Cognitive Science major in the eyes of the Registrar, they have all contributed to my current understanding of the human mind.
In the fall, I’ll begin working on a PhD in cognitive science, so it seems just to expect myself to have a clear definition of the field. “It’s like psychology, right?” asks almost every curious relative, family friend, and dental hygienist I’ve encountered in the past. Others with more understanding of what cognitive science entails may see it as a lofty field, thinking about thinking, without practical applications. The conventional understanding of cognitive science, as articulated by Wikipedia, the hub of collective intelligence, is “the interdisciplinary scientific study of the mind and its processes.” While I certainly can’t disagree with this, such a pithy statement falls short for me.
The world is messy. I’ve always been tempted to impose order on it, applying logic to circumstances in which it may not belong, and I feel confident that I’m not alone in the propensity to reduce the world around me to causes and effects. However, causes and effects are meaningless in the absence of context, the world in which anything- and everything- occurs. Because this world is dynamic and constantly changing, explanatory reductions may be misguided; instead, context may be the only acceptable explanation for the perceptions and actions that we seek to understand. Cognitive science is, to me, the study of the mind- of any agent that perceives and acts in its world- that takes context as its starting point. In order to truly take context into account, the discipline necessarily draws from a number of fields, including psychology, philosophy, linguistics, anthropology, computer science and artificial intelligence, and neuroscience. Each field is simply one piece of the larger puzzle: alone, it has awkward edges and indiscernible shapes, but the amalgamation reveals a whole image that’s greater than the sum of all its parts.
On the first day of Introduction to Cognitive Science freshman year, I had no idea what cog sci was, except that “cognitive” meant something along the lines of “brain.” I created a Turing machine that could determine whether any string of x’s and y’s was a palindrome. All it needed was a set of rules, and the machine was infallible. But as soon as I added a z into the input string, it broke down completely: No Rule Defined, it told me. Because my human brain does not break down and halt in the middle of problem solving, it was evident to me that there aren’t Turing machines in our heads, but instead something else, something more complex than states and rules, that must shape how we think, sense, and act in the world.
Lessons on Chinese grammar, cultures of South American tribes, and programming a for-loop also triggered mind-related thoughts and curiosities in my foreign language, anthropology, and computer science classes. In Perception & Action, I learned more about ants than almost any human would desire to know. An ant colony is a miraculously intelligent system, another example of a product much greater than the sum of its parts. Context alone determines an individual ant’s role and how and when he will carry it out. The ant lives in a constantly changing world, but instead of causing a break down, as such a world would for a Turing machine, it encourages various behaviors that contribute to the colony’s overall success.
What does this mean for the study of human minds? It means that our perceptions, thoughts, and actions are inseparable from the contexts in which they occur. We are situated in the world, and numerous aspects of our world, like prior experiences, culture, and other people, play a prominent role in shaping what we may intuitively believe occurs only or primarily in our heads.
As I prepare to begin a new chapter in my Cognitive Science career, I expect (and hope) that my appreciation of context will color the ways in which I move forward. My devotion to the importance of context has taught me to question everything. It is important to question whether studies done under different circumstances (i.e., outside a lab) and with different subpopulations (i.e., not westernized college undergrads) may have resulted in different conclusions. It is important to question whether there may be ways of viewing the world that differ from my own view (i.e., as cyclical as opposed to linear, or correlational as opposed to causal) that may shape the research questions posed, methods employed, and findings extracted. I hope that by doing this, my mind will remain open to new possibilities, continually working toward achieving the most comprehensive understanding of the mind possible.