Bayesian Rationality: The Probabilistic Approach to Human Reasoning
Are people rational? This question was central to Greek thought; and has been at the heart of psychology, philosophy, rational choice in social sciences, and probabilistic approaches to artificial intelligence. This book provides a radical re-appraisal of conventional wisdom in the psychology of reasoning.
For almost two and a half thousand years, the Western conception of what it is to be a human being has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. From Aristotle to the present day, rationality has been explained by comparison to systems of logic, which distinguish valid (i.e., rationally justified) from invalid arguments. Within psychology and cognitive science, such a logicist conception of the mind was adopted wholeheartedly from Piaget onwards. Simultaneous with the construction of the logicist program in cognition, other researchers found that people appeared surprisingly and systematically illogical in some experiments. Proposals within the logicist paradigm suggested that these were mere performance errors, although in some reasoning tasks only as few as 5% of people's reasoning was logically correct.
In this book a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward: the Western conception of the mind as a logical system is flawed at the very outset. The human mind is primarily concerned with practical action in the face of a profoundly complex and uncertain world. Oaksford and Chater argue that cognition should be understood in terms of probability theory, the calculus of uncertain reasoning, rather than in terms of logic, the calculus of certain reasoning. Thus, the logical mind should be replaced by the probabilistic mind - people may possess not logical rationality, but Bayesian rationality.
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1 Logic and the Western concept of mind
2 Rationality and rational analysis
how much deduction is there?
4 The probabilistic turn
5 Does the exception prove the rule? How people reason with conditionals
collecting data and testing hypotheses
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algorithms antecedent argued argument artificial intelligence assumption Bayesian behaviour bird calculations car starts categorical premise causal Chater and Oaksford cognitive science cognitive system computational conditional inference conditional probability Consequently counter-examples deductive reasoning degrees of belief effects empirical end term endorse epistemology Evans everyday rationality everyday reasoning example experiment explain formal rationality goals heuristics human reasoning hypothesis information-gain model interpretation intuitions Johnson-Laird logicist manipulation mathematical mental logic mental models theory meta-analysis min-heuristic Moreover MT inference natural language non-monotonic non-monotonic logic normative Oaksford and Chater Oaksford et al.’s Oberauer optimal p-entailments p-valid parameter participants people’s possible predictions prior probability probabilistic approach probabilistic model probability theory problem psychology of reasoning q card quantifiers Ramsey test rational analysis rational explanation reasoning tasks relevant representation response rule Schroyens selection task semantics specific syllogisms syllogistic reasoning tion truth table Tweety Wason selection task