- ホーム
- > 洋書
- > 英文書
- > Psychology
基本説明
A follow-up to the influential and highly cited Rational Models of Cognition (OUP, 1998).
Full Description
The rational analysis method, first proposed by John R. Anderson, has been enormously influential in helping us understand high-level cognitive processes.
'The Probabilistic Mind' is a follow-up to the influential and highly cited 'Rational Models of Cognition' (OUP, 1998). It brings together developments in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods. It synthesizes and evaluates the progress in the past decade, taking into account developments in Bayesian statistics, statistical analysis of the cognitive 'environment' and a variety of theoretical and experimental lines of research. The scope of the book is broad, covering important recent work in reasoning, decision making, categorization, and memory. Including chapters from many of the leading figures in this field,
'The Probabilistic Mind' will be valuable for psychologists and philosophers interested in cognition.
Contents
PART I - FOUNDATIONS ; 1. The probabilistic mind: prospects for a Bayesian cognitive science ; 2. Technical introduction: a primer on probabilistic inference ; 3. Rational analyses, instrumentalism, and implementations ; PART II - INFERENCE AND ARGUMENT ; 4. Framing effects and rationality ; 5. Probability logic and the 'Modus Ponens - Modus Tollens' asymmetry ; 6. Inference from absence in language and thought ; 7. Towards a rational theory of human information acquisition ; 8. Pseudocontingencies: a key paradigm for understanding adaptive cognition ; PART III - JUDGEMENT AND DECISION-MAKING ; 9. Probabilistic minds, Bayesian brains, and cognitive mechanisms: harmony or dissonance ; 10. The game of life: how small samples render choice simple ; 11. The naive intuitive statistician: organism-environment relations from yet another angle ; 12. A decision-by-sampling account of decision under risk ; 13. The neurodynamics of choice, value-based decisions and preference reversal ; PART IV - CATEGORIZATION AND MEMORY ; 14. Categorization as nonparametric Bayesian density estimation ; 15. Rational analysis as a link between human memory and information retrieval ; 16. Causality in time: explaining away the future and the past ; 17. Compositionality in rational analysis: grammar-based induction for concept learning ; PART V - LEARNING ABOUT CONTINGENCY AND CAUSALITY ; 18. Through the looking-glass: a dynamic lens model approach to learning in MCPL tasks ; 19. Semi-rational models of conditioning: the case of trial order ; 20. Causal learning in rats and humans: a minimal rational model ; 21. The value of rational analysis: an assessment of causal reasoning and learning ; 22. Conclusion: where next?
