Buch, Englisch, 348 Seiten, Format (B × H): 161 mm x 239 mm, Gewicht: 726 g
Essays in the Cognitive Science of Human Understanding
Buch, Englisch, 348 Seiten, Format (B × H): 161 mm x 239 mm, Gewicht: 726 g
ISBN: 978-0-86377-534-5
Verlag: Taylor & Francis
Oaksford and Chater's argument draws on research in computer science, artificial intelligence and philosophy of science, in addition to experimental psychology. The authors propose that probability theory, the calculus of uncertain inference, provides a more appropriate model for human thought. They show how a probabilistic account can provide detailed explanations of experimental data on Wason's selection task, which many have viewed as providing a paradigmatic demonstration of human irrationality. Oaksford and Chater show that people's behaviour appears irrational only from a logical point of view, whereas it is entirely rational from a probabilistic perspective. The shift to a probabilistic framework for human inference has significant implications for the psychology of reasoning, cognitive science more generally, and forour picture of ourselves as rational agents.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Part I: problems with logicism; autonomy, implementation and cognitive architecture - a reply to Fodor and Pylyshyn; connectionism, classical cognitive science, and experimental psychology; against logicist cognitive science I - the core argument; against logicist cognitive science II - objections and replies; reasoning theories and bounded rationality; bounded rationlity in taking risks and drawing inferences; logicism and everyday reasoning - mental methods and mental logic; the falsity of folk theories - implications for psychology and philosophy. Part II: the probablistic approach; a rational analysis of the selection task I - optimal data selection; a rational analysis of the selection task II - abstract materials; a rational analysis of the selection task III - thematic materials; a rational analysis of the selection task IV - implications; rational explanation of the selection task; information gain explains relevance, which explains the selection; current developments and future directions.