I think it's mostly about interpretation. The matching law basically is a GLM, and diffusion models are also applied in finance and physics, and many RL models are used in settings that have nothing to do with cognition. The RW mechanism is basically online gradient descent.
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Replying to @macstrelioff @djnavarro and
Are you saying that because gray exists that black and white are "just shades of grey"? We can take each model on a case-by-case basis and situate it as being more one thing over another, because most models are.
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Replying to @o_guest @macstrelioff and
I think the point about continuity/discontinuity in this space is important b/c it all comes in the context of people prescribing best practices based on misconceptions around modellers aims and scopes...
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Replying to @tom_hartley @o_guest and
... while the distinction is a very useful one, with overuse (or misuse) the classification/labelling of approaches brings potential assumptions about right/wrong methods and tends to inhibit diversity.
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Replying to @tom_hartley @o_guest and
I enjoyed the
@KordingLab preprint shared by@IrisVanRooij recently (https://osf.io/3vy69/ ) and the dimensional rather than dichotomous approach they took. Strikes me as reflecting the reality, and as stated elsewhere I think the diversity is a feature not a bug.1 reply 1 retweet 6 likes -
Replying to @tom_hartley @macstrelioff and
Importantly for me, the implementation details of a model and a stats method can be literally identical but one is a model and the another is not (due to other details above the implementation layer). Are we on the same page on this issue?
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Replying to @o_guest @macstrelioff and
Yes. Some good points in this thread.
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Ah, OK, good! Because these convos are so high-level that I struggle to always correctly parse everybody due to time and since I think we sometimes use similar/same words in slightly different ways. 
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