The Cambridge Handbook of Computational PsychologyRon Sun 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. |
Contents
Part II Cognitive Modeling Paradigms | 21 |
Part III Computational Modeling of Various Cognitive Functionalitiesand Domains | 187 |
Part IV Concluding Remarks | 665 |
Author Index | 711 |
| 735 | |
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Common terms and phrases
action activation analysis Anderson applied approach architecture associative attention Bayesian behavior brain Cambridge causal chapter cognitive cognitive modeling Cognitive Science complex computational computational models connectionist connections context decision depends described direction discussed distribution dynamics effects error et al example experience Experimental explain field Figure function given goal human important individual inference input interaction Journal knowledge language layer learning logical match meaning mechanisms memory natural neural node objects observed output parameter particular pattern performance possible predictions presented Press prior probability problem processing produce properties Psychology reasoning representations represented response Review rule Science semantic shows similarity simple simulation social specific stimulus structure task theory tion tive understanding units University visual weights