Luiz Pessoa Professor of Psychology at the University of Maryland has recently published The Cognitive-Emotional Brain: From Interactions to Integration a book that lends neuroscientific support to one of the major tenets of CogZest and Cognitive Productivity. I haven’t read the book yet; but I’ve heard Pessoa interviewed by Ginger Campbell on one of my favourite podcasts, the Brain Science Podcast. In this compelling episode, which I highly recommend, they focus mainly on the amygdala and a region of the thalamus, debunking several myths while conveying very deep ideas about the brain, not the least of which is the importance of embracing complexity.
Pessoa shows, for example, that there are several problems with the widely held belief that the amygdala is responsible for fear and anxiety. One problem is that such psychological functions do not tend to be fully located in specific areas. Intelligence —and affect is a contributor to intelligence — is the result of the interaction between several different parts of the brain. There is a social analogy to this: No individual, even Einstein, could make great contributions to science if there is not a network of other people contributing and communicating knowledge.
Another problem with the simplistic view of the amygdala is that this structure does not merely contribute to a single psychological function. It is a very complex structure with many links to different parts of the brain. This has long been known by neuroscientists. As an undergraduate student in the 1980’s, I worked in a brain stimulation reward lab. I would implant electrodes in the lateral hypothalamus of rats. These rats would press a lever for electrical stimulation in this part of the medial forebrain bundle. (We were interested in the underpinnings of the “priming effect”, how one reward makes one want more.) To be sure, rats would be terrified by stimulation in some parts of the amygdala, but they would work for stimulation elsewhere in the amygdala, showing that the amygdala was also concerned with positive evaluations. Pessoa lists several other functions in which the amygdala participates. Yet the dominant function to which the amygdala contributes, fear, is the one retained not only by the media, and those in other area of psychology/neuroscience, but even many neuroscientists. That reminds me of the phenomenon of blocking in classical conditioning: when an animal is trained to associate a conditioned stimulus (CS–1) with an unconditioned stimulus (US), and CS–1 is highly predictive of the US, it can interfere with (block) the development of a conditioned response to CS–2, even if CS–2 is also quite predictive of the US.
Yes, affective processes and experience (emotion, motivation, moods and attitudes) are caused by brain structures, but they are nevertheless information processes. This truth is denied by a very prominent affective neuroscientist, Jaak Panksepp. He and Lucy Biven, in their 2012 book, The Archeology of Mind: Neuroevolutionary History of Human Emotion say “[Emotion] is not a mental state that is created by information processing”. This misses a basic fact, that was first clearly explained by McCulloch and Pitts, that the brain is essentially a computing device. I do not just have a semantic issue with Panksepp’s position. If one were to take Panksepp’s rigid position seriously, it would be impossible to computationally model, and therefore to explain, affective processes. Yet Pessoa and many others, including myself, have provided computational models of motivation and emotion. Attempting to shunt the concept of information processing from the realm of affect is like trying to deny the role of energy in physics; it will delay science but it is doomed to fail.
As I argue in Cognitive Productivity, the idea that cognition, affect and executive functions are separate functions is literally an ancient error, one that comes from Aristotle. I’m not blaming Aristotle, however. The distinctions between these three concepts are useful as a starting point, both in the history of science and in the student’s progression in science. However, we need to see the limitations of these ancient distinctions. In Cognitive Productivity, I give several other reasons why cognition and affect are intertwined and often inseparable.
Now let’s get to some practical implications of this. A major challenge we all face as learners is to program our minds such that we learn to see and understand the world in terms of the knowledge we are acquiring. (I call this mindware development.) In order to apply what we know, we need to appraise events, objects, people and situations in terms of the new knowledge. Consider a person who aced her college mechanical physics exams. If given sufficient relevant information, she will be able to predict the minimum time it will take for one car to avoid hitting a car ahead that is going in the same direction but that suddenly decreases its speed by 50%. However, this same knowledgeable woman, behind the wheel, might fail to realize (or value) that she is not keeping a safe distance from the car ahead of her. Her bookish knowledge has not made her a better driver. It has not sufficiently changed how she perceives dangers and opportunities. She is of course very likely to forget her bookish knowledge—how many people remember differential calculus or undergraduate material? But if she had compiled her knowledge into affectively laden ways of seeing the world, the essence would have lasted her a lifetime. (Well, I suppose with careless driving it might still last her a short lifetime.)
Even very theoretical and remote knowledge may be potentially helpful. To be helpful, it must be able to impact how we perceive and value. The research presented by Professor Pessoa implies that this kind of learning affects not just the cortex but a distributed collection of brain areas, including the amygdala, that work together to add layers of value to our perception, something I call valenced perception. (See Definition 5 in this French dictionary and valency in the O.E.D. Pessoa aptly calls it affective perception.)
In Cognitive Productivity, I discuss criteria that we can use to determine how helpful a knowledge resource is, whether it is a book, a podcast or any other source. That can help us decide what information to process — a very important decision, given how much information there is to choose from, how important knowledge is, and how limited our time is.
All of this is why, as I explained in Cognitive Productivity, when I speak of cognitive science, I generally mean broad cognitive science. This includes classical cognition, affect and executive functions. In fact, it’s all the brain’s information processing. The term “cognitive science”, is therefore unfortunate. It has had the unfortunate effect of limiting the kinds of questions students and professional scientists ask, and problems the public itself expects to be addressed under this banner. But then the expressions “affective science” , “Artificial Intelligence” (AI) and “Psychology” are also problematic. Many people feel that AI is only concerned with understanding non-human minds, whereas it is actually in some ways more general than psychology, for it involves the exploration of the space of possible minds. Perhaps we should all unite under the banner of mental science. The key is to approach problems of interest in a manner that is informed by multiple disciplines, and multiple approaches of solving these problems—the design-based approach, empirical approaches and semantic approaches. In this way, we can continue to enjoy research — our own, and each other’s — knowing A rose, by any other name, would smell as sweet…