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This is also how I tend to think about it! As Olivia says though ANNs can simulate pretty much anything. So if an ANN succeeds through training, that doesn't say much about whether the same happens in humans. It's more of a theoretical limit as you say

But ANNs give us a chance to think in terms of architecture vs training which is much more concrete than the hand wavy nature vs nurture dichotomy. We can think of what architectures and curricula are more plausible (though at a very abstract level) and in that direction things get more interesting

Sure, and also it depends which type of connectionism...

Another, related aspect, that I wanted to highlight in this work is the difference in stances — some much more damaging than others — that appears to have occurred around 2010. These are summarised in Table 1, here: doi.org/10.1037/rev0...; e.g. goals seem to shift from theory to prediction. 3/n

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I think it's full M but the only thing that throws me off is that they still argue for treating architecture, training data, objective and learning rule as manipulable hypotheses. But I'm probably misunderstanding something.. Or is it that they say it but in practice they don't do it enough?

I needed a different M because there's a lot of symbols, other Ms (for model). At least the classic and modern are next to each C and M in the table. To go back to @francescopoli.bsky.social's point however you cannot pick out mechanisms, architecture and so-called training sets in M (modern).

I cannot boil down all the paper into posts without just copy-pasting too much of it (doi.org/10.1037/rev0... & OA osf.io/eaf2z/downlo...). But the main gists are captured in this cartoon and the formalisms in section "Argumentation"; C connectionism doesn't have correlationism as a cornerstone. 7/n