One thing I’ve been discovering in the last couple weeks, co-teaching Intro to Cognitive Science with a philosopher and cognitive scientist, is that we use the class readings differently, and expect students to get different things out of them.
- Philosophy readings tend to be original texts from the philosophers themselves – Rene Descartes, David Marr, Daniel Dennett, and so on. The focus is for students to understand exactly what the authors are saying, the new ideas that they are trying to get across. So students need at least some historical perspective – one might say this is true of most philosophy – then in class we use it to frame other topics of the course in philosophical terms.
- Cognitive science readings tend to be “second-hand”, in the sense that while they may be written by the original researchers, we tend not to read research papers, but more layman’s summaries that cover multiple studies and experiments – Oliver Sacks being a good example. For students, we want them to understand not just the phenomena that is being studied, but also the experiments that allowed researchers to isolate causes, which they must later use to justify their explanations.
- My own readings vary between original research papers and interactive explanations of computer science concepts. Since students in the course are not programmers, the readings tend to be older papers, and even then ones that are closer to surveys than to technical advances. My desire for the students to is skim over the details while getting the gist of what is going on, to gain just a little understanding of why computers and AI is important in cognitive science.
It would never have occurred to me before this class, but these differences make sense given their respective disciplines. That is, the way we choose readings is based on how we were given readings when we were students, which in turn reflects the tradition of philosophy, cognitive science, and computer science respectively. Philosophy is (at least partially) about schools of thought, and so it makes sense to study the thinker and the context in which that thinker lived. Cognitive science, as a semi-physical science, is about creating theories that describe the world, and so it’s necessary to know the experiments that separate the good hypotheses from the bad. And finally, since (for me) computer science is about building and doing things, I choose papers that demonstrate new representations and algorithms, but where the details may not matter as much.
And I love this. About three years ago, I started a personal project to understand what constitutes progress in various academic disciplines. As part of that project, every time I met a graduate student, I asked them what counted as “evidence” in their field and how contradictory theories are resolved. It’s fascinating to learn what counts as progress in philosophy – something that is often still debated by philosophers. (A philosophy friend suggested that although philosophical theories for, say, what constitutes “knowledge” doesn’t have direct supporting experimental evidence, we can still prefer theories that have fewer “exceptions” – much as we use Ockham’s Razor to choose between scientific theories.) One of the more interesting things I learned from the project is that computer science and statistics are unique in that we generate our own data to test our methods – think recovering the parameters of a distribution.
I wonder if it would be useful for students to understand these differences between disciplines, and if so, what would be the best way to guide them towards discovering it.