Practically every aspect of human activities have to the mind/brain: art, literature, aesthetics, ethics, history, politics, business, education, social organizations, etc. all are product of the lump north of the neck, so why aren’t all of these part of the COGS program?
That’s a very good question. In answering it, we can highlight the nature of cognitive science as a field, and the principles by which our COGS curriculum is constructed.
Cognitive science reflects the natural progression of the scientific method, which has found tremendous success in the natural sciences–and its application to the mind. Modern science is founded on the commitment that the world has a mechanistic basis that can be understood as such. Why do objects in the air fall back to the ground? The pre-scientific answer would be that the ground is the “natural” place for objects. There is nothing mechanical about this explanation, not to mention that it is wrong: just observe the dust particles bouncing around in the sunlight while suspended in the air, as noted by the ancient Greeks (who got a lot of things right).
The founders of modern science also wondered about the nature of the mind. Galileo, for instance, marveled at human language, which permits us to construct “from 25 or 30 sounds an infinite variety of expressions, which although not having any resemblance in themselves to that which passes through our minds, nevertheless do not fail to reveal all of the secrets of the mind, and to make intelligible to others who cannot penetrate into the mind all that we conceive and all of the diverse movements of our souls.” These observations are very much the core problem for modern linguistics and cognitive science of language. Descartes developed a theory of vision: Light rays reflected on objects are focused by the lenses on the retinas, and tools from trigonometry for the purpose of distance estimation based on the retinal images. While he got a lot of the details wrong, his general approach is very similar to the modern theory of perception, and is still of enduring interest to the historians and philosophers of science.
A mechanical view of science requires a precise formal “language” to formulate scientific hypotheses from which (non-trivial) consequences can be derived and tested, which may in turn lead to revisions of the hypotheses so we can make progress. This should be familiar to everyone who has taken high school science. In some cases, the formal language had to be invented in order to understand nature: for example, Newton and Leibniz invented calculus to formulate theories of mechanics. In other cases, the formal language had been around all along but the right scientist had to come along to put the pieces together. While Darwin famously proposed the theory of evolution by natural selection, it was not until the early half of the 20th century did mathematicians and biologists figure out a way–actually very simple mathematics–to lay a formal foundation of the theory of evolution. (The delay was also caused by the ignorance of the genetic basis of inheritance and transmission: Mendel did his work at roughly the same time as Darwin but it was forgotten and only rediscovered in the early 1900s.) Closer to home, the initiation and transmission of the action potentials of the neuron are described as mathematical equations like those in the theory of electrical conductance.
For the study of the mind to be a genuine science, we had to wait for the right formal language to come along. That turns out to be the modern theory of computation, which emerged as a branch of mathematics, logic, and philosophy. Computer Science as a field came into existence shortly afterwards, with Penn playing a leading role. It was no accident that linguistics and psychology underwent a revolution, the so-called Cognitive Revolution at roughly the same time, and Artificial Intelligence popped up in the intersection of these fields.
The computational theory of the mind is a research program in which the mechanical basis of the mind is understood and modeled as a computer program. Some objections notwithstanding, it has proven to be enormously successful, so much that we can define cognitive science as theories of the mind that can be in principle be implemented on a computer. The requirement of in-principle-implementation forces us to develop theories in a rigorous and, again, mechanistic, way: a computer tolerates no ambiguity. Indeed, the most successful theories in cognitive science have been implemented: language, vision, learning, memory, etc., and you use these implementations everyday on your smartphone. The topics and classes in our program reflect this general principle: If it can be implemented on a computer from which no trivial consequences follow, then it is Cognitive Science. With luck although without guarantee, other areas of the mind may someday reach the level of mechanistic explanations, and our program will expand.
It should also be noted that our program focuses on the descriptive aspect of the mind–how it works–and not the prescriptive aspect of mind–how it should work, so that people are healthier, happier, make better decisions, etc. These two aspects are obviously related but they are not the same and are in fact frequently out of sync. For instance, medicine may discover a drug that alleviates some symptoms of a mental disorder, which is obviously a good thing for the patient, without understanding the mechanistic basis of that mental disorder. Again, this is not unusual in the history of sciences, where practice is ahead of theory: someday theory may catch up.