Tom Griffiths

And so that's really trying to ask a question about the function of the system and the goals of the system, and then what's an ideal solution to execute that particular function.

And then below that, we have what he called the level of representation and algorithm or the algorithmic level, which is more about what are the actual cognitive processes that you could engage in in order to produce something which is maybe an approximation to that ideal solution.

And then below that is the level of implementation, which is how is that realized in a physical system, right?

And so, you know, for humans, that's in brains, for computers, it's in silicon and so on.

And so when we talk about identifying laws of thought, I think the most natural level to think about that is that most abstract computational level.

And there are things like logic and probability theory stand out as the principles that we can use for saying how it is that you should be solving the kinds of problems that minds have to solve.

So it's a great question.

And I think it's something where when I started out writing the book, I wrote a sort of introduction that said what I normally say in my cognitive science classes, which is we've done a lot of work here trying to understand how minds work.

And in many ways, what cognitive scientists have done is kind of figure out better ways of asking the questions that we want to ask without necessarily giving us answers to those questions.

But then having spent a few years working on the book and writing it, and having things in the external world having changed a little bit as well, by the time I got to the end of it, I ended up feeling like actually, we've done a pretty good job of characterizing what some of those abstract laws might look like.

In particular, at that computational level,

I think it's pretty clear that the things that we should be thinking about are things like logic and probability theory.

And then some additional things that I don't really talk about in detail in the book, but which are more about how that translates into action.

So things like decision theory associated with that reinforcement learning and some of these kinds of more practical applications of those abstract principles.

And then there's another level, which is how does this translate into something which can be actually realized in a physical system in our world, where the principles that come from studying things like artificial neural networks and correspondingly human brains are just as important to understanding intelligence, but understanding it in a different way.

It's less the...

why do we do the things that we do and more the how do we do those things and what what's the the way that that can be instantiated inside a physical system and then there are lots of open questions that we have that kind of lie in the territory between those two things right so you know how is it that you can make systems that are able to do something like what we think abstractly thought should be like uh using things like neural networks and so on

Where is it that they fall short?

How is it that they differ from the kinds of solutions that human minds find?

So there's plenty for us to do as cognitive scientists, but I think those most abstract questions are ones that we have some resolution on.