Nominator's statement

This is a very good book even if you don't agree with the authors. It has raised many discussions in cognitive science recently. It must be on this list!

comments

• Although this book has a very good goal: trying to bridge the gap between neurophysiology, developmental psychology and modeling using neural networks, it does not really succeed in this endevour. Nevertheless it provides for a good reading in each of the three subdomains, but is probably is not strong enough for the top 100.

• This book is well-written, interesting, and probes issues that most cognitive scientists find important. It is an important work and should be on the top 100 list.

• Rethinking Innateness discusses a number of big issues, but I found their discussion to be poorly thought out. They repeatedly referred to the power of domain- general algorithms, but presented domain-specific examples of algorithms in which the input representation, the output representation, and correct performance are built-in to the model. It sounds more like MIT modularity than the emergent empiricism espoused at UCSD. In addition, the authors swept the poverty of the stimulus problem under the rug when it is one of the strongest nativist arguments. RI was really disappointing.