Connectionism in context
著者
書誌事項
Connectionism in context
（Artificial intelligence and society）
Springer-Verlag, c1992
- : uk
- : gw
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注記
Includes bibliographical references
内容説明・目次
内容説明
Connectionism in Context aims to broaden and extend the debate concerning the significance of connectionist models. The volume collects together a variety of perspectives by experimental and developmental psychologists, philosophers and active AI researchers. These contributions relate con- nectionist ideas to historical psychlogical debates, e.g., over behaviourism and associationism, to develop- mental and philosophical issues. The result is a volume which addresses both familiar, but central, topics such as the relation between connectionism and classical AI, and less familiar, but highly challenging topics, such as connectionism,associationism and behaviourism, the dis- tinction between perception and cognition, the role of en- vironmental structure, and the potential value ofconnec- tionism as a means of "symbol grounding". The nine essays have been written with an interdisciplinary audience in mind and avoid both technical jargon and heavy mathematics.
目次
1. Introduction.- Architecture and Properties l.- A Copernican Revolution.- Distributed Representations and Context Dependence.- The Nature of Thought.- 2. Action, Connectionism and Enaction: A Developmental Perspective.- Background.- Symbols, Connectionism and Innate Knowledge.- System Scale and the Control of Action.- Development, Emergence and Enaction.- Conclusion.- 3. Connectionism and Why Fodor and Pylyshyn Are Wrong.- The Case Against Connectionism.- Fodor and Pylyshyn's Four Conditions.- Systems that Satisfy these Conditions.- Fodor and Pylyshyn's Argument.- What's Wrong with this Argument.- What's Wrong with Premise (T3)?.- What's Wrong with Premise (T2)?.- What's Wrong with Premise (T1).- What's Wrong with this Defence?.- The Turing Machine Paradigm.- Minds as Semiotic Systems.- Cognitive Architecture.- On Behalf of Neural Networks.- Dispositions and Predispositions.- The Proper Comparison.- 4. Connectionism, Classical Cognitive Science and Experimental Psychology.- Classicism Versus Connectionism.- The Psychological Data.- Memory.- Inference.- Theory.- Memory.- Inference.- Modelling.- Memory.- Inference.- Conclusions.- 5. Connecting Object to Symbol in Modelling Cognition.- Symbol Systems.- The Symbolic Theory of Mind.- The Symbol Grounding Problem.- Neural Nets.- Transducers and Analogue Transformations.- Robotic Capacities: Discrimination and Identification.- Philosophical Objections to Bottom-Up Grounding of Concrete and Abstract Categories.- Categorical Perception and Category-Learning.- Neural Net and CP.- Analogue Constraints on Symbols.- 6 Active Symbols and Internal Models: Towards a Cognitive Connectionism.- Criticisms of Connectionism.- Connectionism Equals Behaviourism.- Are FFPA Models Behaviourist?.- Connectionism Equals Associationism.- The Active Symbol.- Active Symbols and Control Mechanisms.- Symbol Formation and Properties.- Higher-Level Processes.- First-Order Knowledge Structures.- Second-Order Knowledge Structures.- Toward Structure-Sensitive Operations.- Summary and Concluding Remarks.- The Continuum of Cognitive Models.- Logic, Difficulty and Adaptation.- 7. Thinking Persons and Cognitive Science.- Extending Content.- The Credentials of Cognition.- Consciousness and What It Is Like.- Conceptualized Content and the Structure of Thinking.- Inference and Causal Systernaticity.- Reconstructing the Mind.- 8. A Brief History of Connectionism and Its Psychological Implications.- Connectionist Assumptions in Earlier Psychologies.- Spencer's Principles of Psychology (1855/1899).- William James' Principles of Psychology.- Thorndike's Connectionism.- Pavlov's Theory of the Cerebral Cortex.- Watsonian and Skinnerian Behaviourism.- Hullian Stimulus-Response Theory.- Comparisons of Old and New Connectionism.- Neural Plausibility.- Thought and the Thinking Self.- Empiricism.- Practical Implications of Connectionism.- Conclusions.- 9. Connectionism and Artificial Intelligence as Cognitive Models.- Artificial Intelligence.- Level of Explanation.- Processing Style.- Representational Structure.- Connectionism.- Classification of Neural Networks.- Level of Explanation.- Processing Style.- Representational Structure.- Classical AI and Connectionism.- Segregation.- Compilation.- Hybridization.- Subsumption.- 10. The Neural Dynamics of Conversational Coherence.- Previous Research.- Conversation Analysis: Sequencing Rules Approach.- Speech Acts.- Computational Models.- A Neurally Inspired Model of Coherence.- Some Experimental Results.- How Associative Is Conversation?.- Final on the Purpose of Conversation.
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