The problem is that most MRI exp. design is based on behav. psych, which is a poor framework to begin with.
I disagreed on grounds that psychology provides at least one part of a theory that constrains MRI research.
Here is another of Jack's Tweets:
In theory, theory is great. In practice currently, only data-driven models accurately predict brain activity under naturalistic conditions.
To which, I responded:
What's your endgame, Jack? Is predicting brain activity all you want to do? Or do u want to understand how brain computes mind?Jack's answer:
Science's end game is always an elegant, predictive theory. But complex systems often require a data-driven middle game.The discussion between me and Jack then went offline. After a few exchanges it seemed to me we were converging a little bit and certainly clarifying our positions. I felt the discussion would be interesting to others and asked Jack if I could post it here. He agreed and so here you go! Comments welcome!
I look at it this way: which would you rather have, (1) a computational model that predicts well but you don't know why, or (2) a model that you understand but it doesn't predict anything? I would say obviously (1) is preferred. If you have an in silico computational model that predicts but you don't understand it, you can study the model instead of the brain. And of course that will be much easier, because you are not limited in the number of experiments that you can do to the model. In contrast, if you have (2) you could be stuck in an irrelevant local minimum, and be wasting your time completely.
Understanding (i.e., a low-dimensional explanation that accurately predicts) is obviously the ultimate goal of science, but you may not get there in the most straightforward path (i.e. through theory-driven approaches).
Hi Jack, yes a nice predictive model is great. My point though is about what we are trying to predict. Your statement makes it sound like all we are trying to predict is physiology. That's fine for a physiologist, but the point about studying the brain is that it is a system for controlling behavior. We therefore need good data and good theories of brain, behavior and their relation. I think you agree but many of your tweets give an anti theory anti cog sci impression.
Hey Greg believe it or not I think that we agree on everything except priorities. So let me summarize where I think the problem lies and you can correct me. We both think that the brain is some sort of meaty computer that controls behavior. And we both think that behavior is, ultimately, the most interesting thing. However, you seem to think that theory is really useful and important AT THIS TIME for studying the brain and the brain-behavior relationship. And I do not. My reasons for this are that (1) our understanding about how a system like the brain might compute are really poor, because we don't really understand distributed nonlinear dynamical systems like the brain, (2) we are severely data-limited because our brain measurement technology is pretty poor, and (3) other attempts to use theory to predict computational principles of brain function beyond the most peripheral stages of sensory processing have largely failed. Just take vision as an example. There are no good theories of visual function beyond V1 and MT. All the models that work well beyond those areas are data-driven, not theoretical. And note that the SAME PROBLEM arises in computer vision, and in NLP. The models that actually WORK in computer vision are neural nets, which are basically a data-driven universal mapping function. And the models that actually WORK in NLP are neural nets. That is why all of the engineering people have (temporarily) abandoned theory in favor of nets. Now ultimately of course we're going to have to take these data driven models and extract their principles of computation. But that is a very different problem from starting with overly-strong theory and then ending up in a local minimum.
Thanks for this. Your view is more clear now. We are talking about slightly different things or at least we are taking different approaches. You seem to be taking an engineering approach with a next-step goal of trying to figure out what predicts neural activity and you hope that once that is done, we can derive some "principles of computation." My concern is that there is no easy way to get from the engineering approach to the principles without doing some serious theoretical work that can inform the data-driven results. So while I like the engineering approach and think it is worthwhile pursuing, I don't think it is going to be able to answer our questions without doing theoretical work in parallel.