Semantic Cognition: A Parallel Distributed Processing ApproachMIT Press, 2004 - 425 Seiten This groundbreaking monograph offers a mechanistic theory of the representation and use of semantic knowledge, integrating the strengths and overcoming many of the weaknesses of hierarchical, categorisation-based approaches, similarity-based approaches, and the approach often called theory theory. Building on earlier models by Geoff Hinton in the 1980s and David Rumelhart in the early 1990s, the authors propose that performance in semantic tasks arises through the propagation of graded signals in a system of interconnected processing units. The representations used in performing these tasks are patterns of activation across units, governed by weighted connections among them. Semantic knowledge is acquired through the gradual adjustment of the strengths of these connections in the course of day-to-day experience. The authors show how a simple computational model proposed by Rumelhart exhibits a progressive differentiation of conceptual knowledge, paralleling aspects of cognitive development seen in the work of Frank Keil and Jean Mandler. The authors extend the model to address aspects of conceptual knowledge acquisition in infancy, basic-level effects and their interaction with |
Inhalt
Categories Hierarchies and Theories | 1 |
CategorizationBased Models | 3 |
The TheoryTheory | 28 |
Toward a Mechanistic Model of Semantic Knowledge | 42 |
Summary | 46 |
A POP Theory of Semantic Cognition | 49 |
The Rumelhart FeedForward Network | 55 |
Interpretation and Storage of New Information | 63 |
Category Coherence | 231 |
Category Coherence | 236 |
Category Coherence | 240 |
Illusory Correlations | 250 |
CategoryDependent Attribute Weighting | 258 |
Summary of Findings | 263 |
Inductive Projection and Conceptual Reorganization | 265 |
Inductive Projection | 269 |
Inferences Based on New Semantic Information | 66 |
Discussion of the Rumelhart Network | 69 |
Latent Hierarchies in Distributed Representations | 83 |
Progressive Differentiation of Concept Representations | 84 |
Simulating Loss of Differentiation in Dementia | 104 |
Summary of Basic Simulations | 113 |
Extended Training Corpus for Use in Subsequent Simulations | 114 |
Emergence of Category Structure in Infancy | 121 |
A Brief Overview of the Literature | 123 |
Simulating Preverbal Conceptual Development in the Rumelhart Model | 138 |
Capturing Conceptual Differentiation in Infancy | 144 |
Discussion | 168 |
Naming Things Privileged Categories Familiarity Typicality and Expertise | 175 |
A PDF Account of BasicLevel Effects | 182 |
Learning with Basic Names Most Frequent | 189 |
Learning with All Names Equally Frequent | 197 |
Effects of the Attribute Structure of the Training Corpus | 204 |
Conclusions from Simulations 5153 | 209 |
Familiarity and Expertise Effects | 212 |
DomainSpecific Expertise | 219 |
Different Kinds of Expertise in the Same Domain | 225 |
Generality of the Observed Simulation Results | 229 |
Inductive Projection and Its Differentiation in Development | 270 |
Differential Projection of Different Kinds of Properties | 276 |
Reorganization and Coalescence | 282 |
Coalescence | 283 |
Discussion | 292 |
The Role of Causal Knowledge in Semantic Task Performance | 297 |
The Role of Causal Properties of Objects in Semantic Cognition | 302 |
Toward a POP Account of Causal Knowledge and Causal Inference | 309 |
The Basis of Explanations | 327 |
Comparison of the PDP Approach with TheoryBased Approaches | 338 |
Core Principles General Issues and Future Directions | 347 |
Perspectives on General Issues in the Study of Cognition | 367 |
Semantic Cognition in the Brain | 376 |
Conclusion | 380 |
Simulation Details | 381 |
Training Patterns | 393 |
Individuating Specific Items in the Input | 399 |
Notes | 403 |
References | 407 |
423 | |
Andere Ausgaben - Alle anzeigen
Semantic Cognition: A Parallel Distributed Processing Approach Timothy T. Rogers,James L. McClelland Keine Leseprobe verfügbar - 2006 |
Häufige Begriffe und Wortgruppen
animals assigned attributes backpropagation basic name basis behavior bird brontosaurus canary capture Carey causal knowledge causal properties chapter coherent covariation color concept connection weights connectionist constraints differentiation distributed representations domain domain theories effects eigenvector environment epochs of training erties Eucalyptol example experience explanations feed-forward figure fish flower frequency Gelman Gopnik hidden units hierarchy illusory correlations image schemas induction infants inferences input units internal representations Keil labels layer learning localist mammals Mandler maple McClelland mechanisms Miikkulainen name units novel objects observed output units parallel distributed processing particular pattern of activity pdp++ penguin perceptual phenomena pine plants prop propositions repre Representation units represented robin Rosch Rumelhart model Rumelhart network semantic cognition semantic dementia semantic knowledge semantic memory semantic task performance sensitive sentations share simulation sparrow specific names suggest sunfish superordinate target taxonomic test items theory-theory tion training corpus trees triceratops
Verweise auf dieses Buch
Building Object Categories in Developmental Time Lisa Gershkoff-Stowe,David H. Rakison Keine Leseprobe verfügbar - 2005 |