CogSci books

I wouldn't recommend agreeing with him about a lot of things, but he's definitely worth paying attention to.

The gist of "The Mind Doesn't Work That Way," from what I can tell so far:

So partly sparked by his own work, modularity became an important idea in cognitive science; not all parts of your mind do the same jobs, or have access to the same information. For example, knowing the Müller-Lyer illusion is an illusion doesn't ruin the effect.

Some cognitive scientists of an evolutionary bent saw functional modularity, with the functions defined by the adaptive problems they were designed to solve, as the key to predicting and understanding the mind's entire functional architecture. If the modules are information-encapsulated, then massive modularity also offers a solution to the frame problem. A computational version of this is the picture that Pinker presents in How the Mind Works.

Fodor's position seems to be something like: there are modules; computation is a good way of thinking about modules; but they seem to be restricted to input (eg. perception) and output (eg. maintaining balance) processes (both in the sense of having clear functional success-criteria and in the sense of being informationally-encapsulated). The things cognitive scientists are most interested in - and have had the least success in studying - seem to be nonmodular; when you "believe a belief" or "think a thought", you seem to have at least potential access to most of the information you've ever had access to before. If belief and thought and other things he calls "global processes" are nonmodular, then computation may not be the right way to think about them, despite being the best hypothesis we've had so far.

Fodor's arguments for a "language of thought" make sense (see his book of the same name). In a nutshell, thought seems to be productive – out of given concepts, we can always construct new ones, e.g. arbitrary nestings of "the mother of the mother of ..." – systematic – knowing certain concepts automatically leads to the ability to construct other concepts, e.g. knowing the concept "child" and the concept "wild", I can also represent "wild child" – and compositional, e.g. the meaning of "wild child" is a function of the meaning of "wild" and "child".

Isn't the obvious answer to his pondering just "Natural selection selects for gene frequency"? And hasn't that been pretty well known for a while? If so, that's pretty bad.

And it doesn't help that everything I've read by him so far comes across as disconnected, unmotivated rambling. :-/ I'm gonna have to agree with you.

To be frank, the idea that one could ever get a thorough grounding in any serious, empirical scientific discipline by scouring the Internet is, at least at this time, laughable.

Is this because of the lack of lab work as well as the lack of textbook-level information?

Incidentally, Google is clearly aware of this, and willing to step into some hot (and unprofitable in the short term) waters to get to the book-stored knowledge. They also revived decent open-source OCR probably for this purpose.

Even with respect to computer science though, I think the point is dead wrong. For example, take an awesome introductory textbook like Sipser's Introduction to the Theory of Computation. You can work through that book doing all the exercises in a few weekends, and it is such a pleasure to read that it's hard to put it down. There's no way you could learn as much as efficiently by randomly jumping from topic to topic on Wikipedia and reading online resources.

A good textbook is still pretty much the greatest bargain in the known universe.

I assume this is only a matter of time, not anything fundamental - it happened earlier to computer science than to other disciplines, but it is probably more often than not true already, and will be nearly universally true in matter of years.

Yes, I have MSc in Computer Science.