The Cambridge Handbook of Computational Psychology

Cambridge Handbooks in Psychology

by Ron Sun (Editor)

This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Given the complexity of the human mind and its manifestation in behavioral flexibility, process-based computational models may be necessary to explicate and elucidate the intricate details of the mind. The key to understanding cognitive processes is often in fine details. Computational models provide algorithmic specificity: detailed, exactly specified, and carefully thought-out steps, arranged in precise yet flexible sequences. These models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.

Genres: Psychology, Computers, Computation

768 pages, Kindle Edition

First published January 1, 2008

Reviews

[Muhammad al-Khwarizmi · Rating 3 out of 5]

Interesting, sometimes pioneering material but marred by seriously illucid writing throughout. I understand that handbooks like these are technical and dry—I've read quite a lot of material like this—but you have to wonder whether the authors are even trying. I threw in the towel in the chapter on decision making when its authors described rather vaguely the subjective probability function π(x) of prospect theory—I had forgotten what it looks like given that its counterpart v(x) of course gets far more attention—with no figures or anything. π(x) is simply described as an inverse S. At first I imagined a sigmoid flipped about the line y = some constant and was pretty confused but when I went and checked on Google Images I saw that they had meant a rather shallow S flipped about the line y = x. The description given can admit a number of different interpretations and thinks like this made the reading painful. Here I am going on and on about one sentence but there are plenty of other examples of why this book is pedagogically terrible. The way differentiation among progressively finer categories in Rumelhart and McClelland's semantic network is described is also pretty bad, for instance. You'd have to read it to see. I've come across lots of perfectly readable literature on neural networks and what's in here just doesn't rate. You probably get the picture now anyway. There are stumbling blocks everywhere and if my copy were physical I would have felt tempted to chuck it out of a window. Only Springer texts top this for bad academic writing.

Best used as reference material, and with auxiliary material for comprehension at that.

[Ricardo Acuña · Rating 5 out of 5]

A great reference book on the emerging field of computational psychology (or cognitive modeling). I only have read the first models. Nevertheless, I will continue reading for my on going research.