1. “My understanding is that we can do things like remember a word by putting it on loop using speech motor control circuits”—this is called phonological loop in psycholinguistics (psychology) and is NOT THE SAME as working memory—in fact, tests for working memory usually include reading something aloud precisely to occupy the circuits and not let the test subject take advantage of their phonological loop. What I mean by working memory is the number of things one can hold in their mind simultaneously captured by “5+-2″ work and Daneman’s tests—whatever the explanation is.
2. Fodorian modules are, by definition, barely compatible with CCA. And the Zeitgeist of theoretical linguistics leads me to think that when you use RNN to explain something you’re cheating your way to performance instead of explaining what goes on (i.e. to think that brain ISN’T an RNN or a combination thereof—at least not in an obvious sense). Thus we don’t quite share neurological assumptions—though bridging to a common point may well be possible.
To be clear, I am using the term “recurrent” as a kinda generic term meaning “having a connectivity graph in which there are cycles”. That’s what I think is ubiquitous in the neocortex. I absolutely do not think that “the kind of RNN that ML practitioners frequently use today” is similar to how the neocortex works. Indeed, I think very few ML practitioners are using algorithms that are fundamentally similar to brain algorithms. (I think Dileep George is one of the exceptions.)
Fodorian modules are, by definition, barely compatible with CCA
...unless the Fodorian modules are each running the same algorithm on different input data, right?
Well, no. In particular, if you feed the same sound input to linguistic module (PF) and to the module of (say, initially visual) perception, the very intuition behind Fodorian modules is that they will *not* do the same—PF will try to find linguistic expressions similar to the input whereas the perception module will try to, well, tell where the sound comes from, how loud it is and things like that.
1. “My understanding is that we can do things like remember a word by putting it on loop using speech motor control circuits”—this is called phonological loop in psycholinguistics (psychology) and is NOT THE SAME as working memory—in fact, tests for working memory usually include reading something aloud precisely to occupy the circuits and not let the test subject take advantage of their phonological loop. What I mean by working memory is the number of things one can hold in their mind simultaneously captured by “5+-2″ work and Daneman’s tests—whatever the explanation is.
2. Fodorian modules are, by definition, barely compatible with CCA. And the Zeitgeist of theoretical linguistics leads me to think that when you use RNN to explain something you’re cheating your way to performance instead of explaining what goes on (i.e. to think that brain ISN’T an RNN or a combination thereof—at least not in an obvious sense). Thus we don’t quite share neurological assumptions—though bridging to a common point may well be possible.
Thank you for clearing up my confusion! :-)
To be clear, I am using the term “recurrent” as a kinda generic term meaning “having a connectivity graph in which there are cycles”. That’s what I think is ubiquitous in the neocortex. I absolutely do not think that “the kind of RNN that ML practitioners frequently use today” is similar to how the neocortex works. Indeed, I think very few ML practitioners are using algorithms that are fundamentally similar to brain algorithms. (I think Dileep George is one of the exceptions.)
...unless the Fodorian modules are each running the same algorithm on different input data, right?
Oh, then sorry about the RNN attack ;)
Well, no. In particular, if you feed the same sound input to linguistic module (PF) and to the module of (say, initially visual) perception, the very intuition behind Fodorian modules is that they will *not* do the same—PF will try to find linguistic expressions similar to the input whereas the perception module will try to, well, tell where the sound comes from, how loud it is and things like that.