Connectionism and the Mind - Abrahamsen a., A.

. .

Auteur(s) : Abrahamsen a., A. - Bechtel

Editeur : Blackwell Publishers

Langue : Anglais

Parution : 01/12/2001

Format : Moyen, de 350g à 1kg

Nombre de pages : 424

Expédition : 728

Dimensions : 24.4 x 17.0 x 23.0

ISBN : 9780631207139

Résumé :
Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience.

Read two of the sample chapters on line:

Connectionism and the Dynamical Approach to Cognition:
http://www.blackwellpublishing.com/pdf/bechtel.pdf

Networks, Robots, and Artificial Life:
http://www.blackwellpublishing.com/pdf/bechtel2.pdf

...

Biographie:
William Bechtel is Professor of Philosophy at the University of California, San Diego and Editor of the journal Philosophical Psychology. His publications include Philosophy of Mind (1988), Philosophy of Science (1988), and Discovering Complexity (1993, with Robert Richardson), A Companion to Cognitive Science (with George Graham, Blackwell 1999), Philosophy and the Neurosciences (with Pete Mandik, Jennefer Mundale and Robert S. Stufflebeam, Blackwell 2001).

Adele Abrahamsen is Associate Professor of Psychology and Undergraduate Director of the Philosophy-Neuroscience-Psychology and Linguistics Programs at Washington University in St. Louis. She is the author of Child Language (1977).

Sommaire:

Preface xiii

1 Networks Versus Symbol Systems: Two Approaches To Modeling Cognition 1

1.1 A Revolution in the Making? 1

1.2 Forerunners of Connectionism: Pandemonium and Perceptrons 2

1.3 The Allure of Symbol Manipulation 7

1.3.1 From logic to artificial intelligence 7

1.3.2 From linguistics to information processing 10

1.3.3 Using artificial intelligence to simulate human information processing 11?????????

1.4 The Decline and Re-emergence of Network Models 12

1.4.1 Problems with perceptrons 12

1.4.2 Re-emergence: The new connectionism 13

1.5 New Alliances and Unfinished Business 15

Notes 17

Sources and Suggested Readings 17

2 Connectionist Architectures 19

2.1 The Flavor of Connectionist Processing: A Simulation of Memory Retrieval 19

2.1.1 Components of the model 20

2.1.2 Dynamics of the model 22
2.1.2.1 Memory retrieval in the Jets and Sharks network 22
2.1.2.2 The equations 23

2.1.3 Illustrations of the dynamics of the model 24
2.1.3.1 Retrieving properties from a name 24
2.1.3.2 Retrieving a name from other properties 26
2.1.3.3 Categorization and prototype formation 26
2.1.3.4 Utilizing regularities 28

2.2 The Design Features of a Connectionist Architecture 29

2.2.1 Patterns of connectivity 29
2.2.1.1 Feedforward networks 29
2.2.1.2 Interactive networks 31

2.2.2 Activation rules for units 32
2.2.2.1 Feedforward networks 32
2.2.2.2 Interactive networks: Hopfield networks and Boltzmann machines 34
2.2.2.3 Spreading activation vs. interactive connectionist models 37

2.2.3 Learning principles 38

2.2.4 Semantic interpretation of connectionist systems 40
2.2.4.1 Localist networks 41
2.2.4.2 Distributed networks 41

2.3 The Allure of the Connectionist Approach 45

2.3.1 Neural plausibility 45

2.3.2 Satisfaction of soft constraints 46

2.3.3 Graceful degradation 48

2.3.4 Content-addressable memory 49

2.3.5 Capacity to learn from experience and generalize 51

2.4 Challenges Facing Connectionist Networks 51

2.5 Summary 52

Notes 52

Sources and Recommended Readings 53

3 Learning 54

3.1 Traditional and Contemporary Approaches to Learning 54

3.1.1 Empiricism 54

3.1.2 Rationalism 55

3.1.3 Contemporary cognitive science 56

3.2 Connectionist Models of Learning 57

3.2.1 Learning procedures for two-layer feedforward networks 58
3.2.1.1 Training and testing a network 58
3.2.1.2 The Hebbian rule 58
3.2.1.3 The delta rule 60
3.2.1.4 Comparing the Hebbian and delta rules 67
3.2.1.5 Limitations of the delta rule: The XOR problem 67

3.2.2 The backpropagation learning procedure for multi-layered networks 69
3.2.2.1 Introducing hidden units and backpropagation learning 69
3.2.2.2 Using backpropagation to solve the XOR problem 74
3.2.2.3 Using backpropagation to train a network to pronounce words 77
3.2.2.4 Some drawbacks of using backpropagation 78

3.2.3 Boltzmann learning procedures for non-layered networks 79

3.2.4 Competitive learning 80

3.2.5 Reinforcement learning 81

3.3 Some Issues Regarding Learning 82

3.3.1 Are connectionist systems associationist? 82

3.3.2 Possible roles for innate knowledge 84
3.3.2.1 Networks and the rationalist-empiricist continuum 84
3.3.2.2 Rethinking innateness: Connectionism and emergence 85

Notes 87

Sources and Suggested Readings 88

4 Pattern Recognition and Cognition 89

4.1 Networks as Pattern Recognition Devices 90