Tim Scarfe: evaluation

Transcript context

…the notion of patterns and real patterns, to invoke Dannett's term there, is a helpful 1. So 1 thing that you could say is going on here is that, yes, there's lots of complexity there in the natural world, it's apparent in the data, but like if you just denoise the data a bit, underlying there there's a real pattern and we should we don't have to be like Platonist and weird about it, but there's just regularity that is sometimes masked by noise. That doesn't seem like too metaphysically problematic, but 1 of the questions that I sort of posed to that as a challenge to that, you know, very moderate view, and I and I say this frequently in the book, is when you're saying that some of the apparent dysregularity in the data is irrelevant, that's your decision as a scientist. It's not relevant to you at the moment, but that it could be relevant to someone else. It could be really important to how that system works in the natural world for reasons that you're not aware of. So when we sort of classify the signal versus noise in our datasets, we shouldn't ignore the the fact that those are decisions that we're bringing to bear on our investigation. We shouldn't assume that we're just reading off the signal, the real pattern that is there in reality, and that there aren't very many other significant real patterns there. And to the extent that we're probably also kind of creating pattern through the through the very denoising process that we bring about. Interesting. Physicists aren't under any illusion, so they know that Newton is an idealization. And just to contrast, you cited reflex theory. Of course Pavlov and the dogs, folks at home will know about that. And Newton is still around, we still use that, but we don't use reflex theory anymore. Yeah. Yeah. So this is a chapter that I present in the book as a case study of how oversimplification can get scientists on the wrong track. So the history of science is, like, 2020. We're looking at a theory about how the brain worked, was really dominant for a few decades at the end of the nineteenth century, beginning of the twentieth century. It's, yeah, familiar to us in the with Pavlov, with this idea that we can explain behavior in terms of reflexes which get conditioned and there's obviously learning involved with that. The most ambitious version of the theory said that all of the functions in the brain are basically versions of, reflex arcs, so sensory motor loops. So, very prestigious and sort of well regarded physiologist like Charles Sherrington was heavily invested in the reflex theory. But he admitted in his his book, The Integrated Action of the Nervous System, that this notion of a simple reflex is an idealization. It probably doesn't exist in real life, and yet this is the key that's going to kind of unlock, neurophysiology. It's gonna help us decompose and make sense of all of these different interactions that could be observed experimentally. So what seems to be going on there is that scientists were sort of taking that age old method, which is that it's a good heuristic to seek parsimonious explanations, to use Occam's razor, and the obvious thing to do was like, let's assume there's this thing that's there's a simple reflex, and then running with it way too far, actually never being able to explain the amount of data that they had initially thought that they would with it. And it's not clear how long the reflex theory could have gone on for if it hadn't been for the computational theory, sort of coming in during the second world war era, and basically providing an alternative explanatory framework which was also quite neat, and I would say provides its own kind of idealization toolbox.…

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