Norbert has brought out the main themes of my paper much more clearly than I could have (many thanks for that). This entry is something of a postscript triggered by the comments over the past few days.
The comments remind me of the early days in the Past Tense debate. What does it mean to be a connectionist model? Can't it pass the Wug test if we just get rid of those awful Wickelfeatures? If not backprop, maybe a recurrent net? Most commentators tread a similar terrain: What’s the distinction between a normative Bayesian model and a cognitive one? How essential is the claim of optimality? Is a model that uses non-Bayesian approximations Bayesian in name only? If not MAP, then how about a full posterior interpretation … 
These questions can never be fully resolved because they are questions about frameworks. As Norbert notes, frameworks can only be evaluated by the questions they raise and the answers they provide, not by whether it can or cannot do X because one can always patch things up. (Of course this holds for the Minimalist Framework as well.) A virtue of the Past Tense debate was that it grounded a largely conceptual/philosophical discussion in a well-defined empirical domain, and we have it to thank for a refined understanding of morphology and language acquisition. That represents progress, even if no minds were changed. So let’s focus on some concrete empirical cases, be it probability matching by rodents or Polish genitives by kids. Framework-level questions go nowhere, especially when the highest priests of Bayesianism disagree.
As I said in the paper, none of my criticisms is necessarily decisive but taken together, I hope they make it worthwhile to pursue alternatives : alternatives that linguists have always been good at (e.g., restricting hypothesis space), alternatives that take the psychological findings of language acquisition seriously, and alternatives that do not take forever to run. It’s disappointing to see all the hard lessons are forgotten. For instance, indirect negative evidence, which was always viewed with suspicion, is now freely invoked without actually working through its complications. The problem doesn't go away when the modeler peeks at the target grammar and rigs the machinery accordingly, even though the modeler is some kind of idealized observer.
Somewhere during the Second Act of the Past Tense debate, connectionist models that implicitly implemented the regular/irregular distinction started to appear. I remember it annoyed the heck out of a young Gary Marcus, but I suspect that an older and wiser Gary would take that as a compliment.
 A "true" Bayesian model does not necessarily do better. As I noted in the paper, one such model for morphological learning took a week to train on supervised data but only offers very marginal improvement over an online incremental and psychologically motivated unsupervised model, which processed almost a million words in under half an hour.
 The paper does offer an alternative, one embedded in a framework that insists on a transparent mapping between the Marrian levels. Like in the Past Tense debate, a critique is never enough, and one needs a positive counterproposal. So let's hear some counter-counter-proposals.