Cognitive Models and Architectures

Languages differ from one another in bewildering and seemingly arbitrary ways. For example, in English, the verb precedes the direct object (understand the proof), but in Japanese, the direct object comes first. In some languages, such as Mohawk, it is not even possible to establish a basic word order. Nonetheless, languages do share certain regularities in how they are structured and used. The exact nature and extent of these “language universals” has been the focus of much research and is one of the central explanatory goals in the language sciences. During the past fifty years, there has been tremendous progress, a few major conceptual revolutions, and even the emergence of entirely new fields. The wealth of findings and theories offered by the various language-science disciplines has made it more important than ever to work toward an integrated understanding of the nature of human language universals. This book examines language universals from a cross-disciplinary perspective. It provides insights into long standing questions such as: What exactly defines the human capacity for language? Are there universal properties of human languages and, if so, what are they? Can all language universals be explained in the same way, or do some universals require different kinds of explanations from others?

This book attempts to resolve the Great Rationality Debate in cognitive science—the debate about how much irrationality to ascribe to human cognition. It shows how the insights of dual-process theory and evolutionary psychology can be combined to explain why humans are sometimes irrational even though they possess remarkably adaptive cognitive machinery. The book argues that to characterize fully differences in rational thinking, we need to replace dual-process theories with tripartite models of cognition. Using a unique individual differences approach, it shows that the traditional second system (System 2) of dual-process theory must be further divided into the reflective mind and the algorithmic mind. Distinguishing them gives a better appreciation of the significant differences in their key functions: the key function of the reflective mind is to detect the need to interrupt autonomous processing and to begin simulation activities, whereas that of the algorithmic mind is to sustain the processing of decoupled secondary representations in cognitive simulation. The book then uses this algorithmic/reflective distinction to develop a taxonomy of cognitive errors made on tasks in the heuristics and biases literature. It presents the empirical data to show that the tendency to make these thinking errors is not highly related to intelligence. Using a tripartite model of cognition, the book shows how, when both are properly defined, rationality is a more encompassing construct than intelligence, and that IQ tests fail to assess individual differences in rational thought. It then goes on to discuss the types of thinking processes that would be measured if rational thinking were to be assessed as IQ has been.