Brownian thought space

Cognitive science, mostly, but more a sometimes structured random walk about things.

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Tuesday, May 23, 2006

Categories and causality

Nice paper: Waldmann, MR & Hagmayer, Y (2006) Categories & causality: The neglected direction, Cognitive Psychology, 53, 27-58. Specially nice is the introductory part, in which they discuss some of the ideas about categories and causality. Here is the example: imagine tokens, A, B , C & D. If you see that A & C are always followed by some effect E. Then the regularities can be summarized depending on how you cut up the world. If you see A and C as tokens of the same category C1, then you will say that C1 causes E. But if you cut up the world so that A and B are the same category C1 (while C & D are category C2), then both C1 and C2 predict E with equal likelihood, so there is no information, and no reason to believe any kind of causal stories between categories and events. Notice that if there are (statistical) regularities that group A & C together (say both are brightly coloured, while B & D are dull coloured), then this is not a problem anymore. {Sidetrack: For a paper which discusses this in a sort of ok way but then goes horribly wrong, see the BBS article: Clark, A & Thornton, C (1997). Trading spaces: Computation, representation and the limit of uninformed learning. Behav. Brain Sci., 20, 57-90} One way of looking at the results of this paper is to say that if there are such dimensions, and if you have learnt to classify based on the brightness dimension, then you might use these brightness-based groups to draw causal inferences (the bright ones do well at college, for example ;). But now, imagine that A & B are large objects, while C & D are tiny objects. And now someone comes along and says that A & B cause problems. How readily would you conclude that large objects cause problems? Not very easily, find the authors. Looks like you get stuck with the original categories, and so cannot quite 'see' the link between a different dimension of categorization and its causal inference. Turns out that if you believe that the original dimension of classification was a 'natural kind'; so something that existed out-there, so to say (like colours and brightnesses and sizes), as opposed to something someone made up; then you are much less willing to give up the natural categorization than the made-up one. What strikes me here is that nowhere is there the mention of the Wisconsin Card Sorting task. Essentially, card sorting in the Wisconsin task requires users to sort according to a certain rule (say colour), and then, at some point, to switch over to, say, shape. In 'normal' adults, this is supposed to be do-able. So, we can set-shift along different dimensions. Could it be that this is due to the artificial nature of the categrizaton schemes? So, does the adult simply think that these are not natural categories, so they can switch easily? Would it be harder with natural categories? And here is something else. What about patients who do sort correctly, but then persevere. Could it be that atleast in some cases, this is not due to the executive dysfunctioning, but due to the fact that the patient cannot separate natural from artificial categories? And why is the whole thing interesting anyway? Hmm. I think that would need a separate post. Starting with Gary.

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