This is a fundamental distinction that, unfortunately, is too often disregarded and one critical reason why, in my view, many psychologists misunderstand the complexities of building computational models
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Nope. You're right. Sticking with Bayes, implementation would also include sampler etc.
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Is sampler not seen as part of theory? I thought sometimes sampling is part of the algorithmic level hypothesis. Coincidentally, just saw this come by on the
#iccmpsyched hashtag:https://twitter.com/flosense/status/1021071829623091200?s=21 …
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Interesting. I wouldn't think so though. If I implement my model in stan, and then switch to jags, I wouldn't think I would change anything about my theory.
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Oh, I see my ambiguity. I just meant Bayesian inference, not Bayesian theory of mind.
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Ah, you meant Bayesian inference by the scientist? Then understood!
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Here's something that helps I think for those who haven't been exposed before. I'm not equivocating between theory and spec but they can serve a similar role. https://figshare.com/articles/Varieties_of_Reproducibility_in_Empirical_and_Computational_Domains/6818018 …pic.twitter.com/3GTTzV3gIH
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Brain still melting here but I need to take a transatlantic flight so gotta be strong.


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Sorry yo hear

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I agree that the implementation vs theory distinction is important. As Grace brought up Marr: I notice that students in my comp & formal modeling class often confuse implementation of theory for purposes of computer simulation with (Marr’s) ‘implementational-level theory’.
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Once that comfusion is cleared up they often still find it hard to distinguish which part of the code corresponds to computational level and which to algorithmic, as they all look like algorithms. But often algorithms are only a way to derive/simulate comp-level predictions.
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I promise to return when I get some headspace/spoons back!
