I think one of the main things that struck me is the type of iterated rationality models (IRMs) that F&K2020 discuss -- those IRMs don’t seem like any of the ones I’ve seen in the cognitively-oriented literature that connects with human behavioral or developmental data. That is, in footnote 3, F&K2020 note that there’s an IRM approach that assumes grammatical derivation of alternatives, and then uses probabilistic reasoning to disambiguate those alternatives in context. They don’t have a problem with this IRM approach, and think it’s compatible with the grammatical approach they favor. So, if we’re using this IRM approach, then the worries F&K2020 that highlight don’t apply? In my own collaborative work for instance, I’m pretty sure we always talk about our IRM (i.e., RSA) models as ambiguity resolution among grammatical options that were already derived, though we can assign priors to them and so include how expensive it is to access those options.
Other thoughts:
(1) My take on footnote 4 and related text: there’s a conceptual separation between the creation of alternatives (syntactic/semantic computation) and how we choose between those alternatives (which typically involves probabilities). I know there’s a big debate about whether this conceptual separation is cognitively real, and I think that’s what’s being alluded to here.
(2) The comparison “grammatical approach": I’m curious about the evaluation metrics being used for theory comparison here -- in terms of acquisition, the grammatical approach requires language-specific knowledge (presumably innate?) in the form of the Exh operator, the “innocent inclusion”, and “innocent exclusion” operations. From this perspective, it’s putting a lot of explanatory work onto the development of this language-specific knowledge, compared with domain-general probabilistic reasoning mechanisms. I guess F&K2020 are focused more on issues of empirical coverage, with the multiplier conjunctive reading example not being handled by Franke’s approach.
(3) In section 6 on probabilities and modularity, F&K2020 discuss how probabilities could be part of the initial computations of SIs. I think I’m now starting to blur between this and the version of IRMs that F&R2020 were okay with, which is when IRMs have possibilities that are calculated from the grammar (e.g., with the semantics) and then the IRM uses recursive social reasoning to choose among those possibilities in context. It seems like the “SI calculation” part is about navigating the possibilities (here: the options on the scale that come from the semantics). So, RSA models that purport to capture SIs (even if relying on scale options that come from the grammar) would be exactly the IRMs that F&R2020 would be unhappy with.
(4) In 6.3, F&K2020 mention that priors could be “formal constructs defined internally to the system.” This is clearly an option the F&K2020 think is viable (even if they don’t favor it), so it seems important to understand what this means. But I’m unclear myself on how to interpret that phrase. Would this mean that there are probabilities available beforehand (therefore making them priors), but they’re not tied to anything external (like beliefs about the world, or how often a particular interpretation has occurred)? They’re just...probabilities that get generated somehow for possible interpretations?
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