Distinction between 'a theory being testable in principle" versus "being able to think of a *concrete* experiment to test the theory' is important, because too often if exp psychologists can't think of an experiment right away they think the theory is at fault. (cc @annemscheel)https://twitter.com/maxcoltheart/status/1049607054162182144 …
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Personally, I see a role for (computational?) modeling in theory and a role for (statistical?) modeling in data analyses, and those two also need to be kept distinct (yet, often are confused).
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I think that's exactly why theory people need to have real experimental expertise, so that they know how to reach out effectively to those folks, and how to frame their models (e.g. minimize equations in pubs) so that the exp. folk want to read them.
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I've work with data gathered by others. I've gathered data myself but only once and for an online thing (http://rescience.science ), so I don't really count it TBH. I have been involved in the design of experiments but never very very closely.
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But I have no idea if you consider me (I consider myself a modeller) a theory person and if you think I have "real experimental expertise"?
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Most people have experimental experience in the way I use these words due to the way cogsci and psych are taught. Modellers seem to emerge despite not because of most programmes — because the focus seems to be almost always empirical.
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So unless my experiences are somehow outlier ones (which they may be I realise I'm in a very small minority of people within cog/psych) I believe it's already the case that experimental experience is a baseline experience all PhDs have.
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Sometimes exp training can even hinder theoretical development. Not saying no empirical expertise is useful for theoretician, but also see how drilling of exp training can kill all conceptual creativity & make people conflate statistical hypothesis with substantive theory.
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If you're analyzing data, I think that's essentially empirical experience. I think the important aspect is just to know that data can be messy so that you don't get too attached to a particular interpretation of it.
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This is a good point, e.g. was at meeting with logicians & a cognitive scientist showed a plot of human data, with 3 noisy lines. I and other empirically trained ppl understood the general pattern. A logician asked "why do the lines zig-zag & how does your theory explain that?"
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