This is a very welcome introductory text that fills a gaping gap in the literature. In the past 10 years, computational modeling has moved from a niche area of psychology (and other fields) into the main stream. This book covers most of the essentials, from the philosophy of computational modeling, to maximum-likelihood estimation, to model comparison. Throughout the book, the authors show the reader how to balance theoretical integration and model parsimony with statistical fit. Indeed, even if you never go through the steps of creating and testing models, this lesson will serve you well in all areas of scientific reasoning.
Armed with this book, a free copy of the statistics programming language R (and of course some data), you'll be off and running in no time. I highly recommend this book for motivated undergraduate students and all graduate students studying computational cognitive modeling.
One more thing - the authors are great, if you have questions while working through the book I'm sure they would be happy to answer them.
