Tim Scarfe: evaluation

Transcript context

…It was quite a few years in the making. I think officially I started writing it maybe 2018 and it came out in 2024. But it was really based on ideas that I'd been working on. Maybe since 2014, I started publishing some philosophy of science papers about computational explanation in neuroscience. And then going back beyond that, some of my own experiences when I was doing training in neuroscience on the visual system and I was using computational models of the era before there was deep learning or anything that fancy, thinking about really what does understanding the brain through this lens of computation by saying that we have models which not only simulate the brain as biological simulation using computers and all kinds of things or weather simulation such and so forth, but actually kind of alleged to duplicate the function of cells in the brain, which is this kind of additional claim which is made of about computational modeling when it's applied to the brain as this unique unique structure which is not only a biological organ, but also a kind of computer itself. The arc of your book is we have this problem with simplification. Yeah. Because as scientists, we want to build legible theories about how the world works. A lot of philosophy of science in recent years has picked up this topic of abstraction and idealization. So abstraction is sort of quite a general word which can just mean sort of ignoring details which are there in concrete real life situations. So it would be, you know, familiar to you from doing sort of Newtonian problems in physics where your teacher tells you, well, there's always friction in real life, but we'll pretend that the friction isn't there. So you're leaving out a detail which is known to be there in the concrete concrete system. Idealization means, sort of attributing properties to the system that you're modeling in science which are known to be false. So for example in genetics modeling, the assumptions made of infinite populations, these kinds of idealizations often make the calculations more tractable but of course there's no such thing as an infinite population in real life. In some way, an abstraction is also always a false representation, always an idealization. So sometimes the difference between the 2 can be subtle. How I put this in the book is that an idealization kind of points us to the thought that when we have a scientific representation, we're kind of presenting something which is kind of cleaner and better than the thing in real life. When we talk about some someone being idealistic, it's like they have a view of how things should be and unfortunately reality does not live up to that. So idealization in science is often to do with sort of representing things mathematically in a way which is kind of cleaner and neater than could be possible in real life.…

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