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Brain, Meaning, and Computation

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Brain, Meaning, and Computation Brain, Meaning, and Computation Von der Philosophisch- Historischen Fakultaet der Universitaet Stuttgart zur Erlangung der Wuerde eines Doktors der Philosophie (Dr. phil.) genehmigte Abhandlung Vorgelegt von Michael Klein aus Leonberg Hauptberichter: Prof Dr. h.c. Hans Kamp PhD Mitberichter: Prof Dr. Guenther Palm Tag der muendlichen Pruefung: 1. Februar 2007 Institut fuer maschinelle Sprachverarbeitung Universitaet Stuttgart 2007 Acknowledgment This work was supported by the German Research Foundation (DAG), the German Aca- demic Exchange Service (DAAD), and the National Institute of Information and Commu- nications Technology of Japan (NICT). I am very grateful to Hans Kamp for all his time, patience, good advice, many discus- sions and for teaching me the essentials of semantics, logic and philosophy of language. His criticism shaped my writing abilities and finally made me able to write scientific text. I would like to thank Guenther Palm for his support and feedback, and for his advice in questions related to neural modelling. His outstanding knowledge about the modeling of unsupervised associative learning and the simulation of binding by synchronization gave this thesis its first main theoretical backbone. It was Michael Arbib’s Brain Simulation Laboratory where I leared how neural models can be designed on the basis of neuroscientific data. He also gave me encouraging feedback, as well as many critical comments on my ideas. Kenji Doya invitated me to Japan, and there I learned a lot about internal models and reinforcement learning, the second theoretical backbone of this thesis. He also teached me how to write scientific articles. With Peter Indefrey I had a very stimulating discussion about the modelling of indi- vidual concepts and how my theory could be tested experimentally. I also had stimulating discussions about how the brain computes with Christof von der Malsburg. Almut Schuez and Valentino Braitenberg shaped my way of thinking about the brain by discussing the biological evidence for specific learning mechanisms with me. Aude Billard taught me the methods of synthetic brain imaging and helped me to program my very first neural network. She also introduced me to C programming. Laurent Itti introduced me to C++ programming. He always had helpful and stimulating answers to my questions concerning visual processing and attention in the brain. Dirk Wildgruber taught me the methods of functional brain imaging. With him I shared many exciting discussions about philosophy and language processing in the brain. Finally, I would like to thanks Cristina Rosazza and Majken Hulstijn for comments on the manuscript. i ii Contents 1 Introduction 1 1.1 Background Problem . 1 1.2 Massive Integration . 4 1.3 Learning . 6 1.4 The Question of Innateness . 8 1.5 Goal-Directedness . 12 1.6 Scope of this Thesis . 13 2 Learning, Representation, and Processing in the Brain 17 2.1 Rethinking Modularity . 17 2.1.1 A Short Historical Overview . 17 2.1.2 Current Issues . 18 2.2 Brain Structures and Learning Algorithms . 21 2.3 Real Neurons and Artificial Neurons . 25 2.3.1 Neurons . 25 2.3.2 Synapses and Learning . 26 2.4 Cerebellum . 26 2.4.1 Supervised Learning . 26 2.4.2 Anatomy . 27 2.4.3 Physiology . 28 2.4.4 Theoretical Model . 28 2.5 Basal Ganglia . 29 2.5.1 Reinforcement Learning . 29 2.5.2 Anatomy . 29 2.5.3 Physiology . 29 2.5.4 Theoretical Models . 29 2.6 Cerebral Cortex . 30 2.6.1 Unsupervised Learning . 30 2.6.2 Anatomy . 30 2.6.3 Physiology . 30 2.6.4 Theoretical Models . 34 iii iv CONTENTS 3 A Goal-Directed Communication System 37 3.1 The Importance of Considering Goals . 37 3.2 The Overall Architecture . 38 3.2.1 Essential Cognitive Functions . 38 3.2.2 A Formal Description of the Architecture . 38 3.3 Cognitive Components in More Details . 39 3.3.1 The (Conceptualized) State Representations . 39 3.3.2 Evaluation . 40 3.3.3 Internal Models . 40 3.3.4 Other Essential Functions . 41 3.4 Remarks on Utterance Comprehension . 42 4 Basic Units of Meaning 43 4.1 Form and Meaning . 43 4.2 Representation of Word Forms . 44 4.2.1 Functional Neuranatomy . 44 4.2.2 Learning of Words . 47 4.2.3 Computational Aspects . 47 4.2.4 Abstract Word Form Units . 48 4.3 Representations of Concepts . 48 4.3.1 The Basic Unit of Meaning . 48 4.3.2 A Neural Theory of Predication . 49 4.4 Individual and Categorical Concepts . 54 4.4.1 Categorization . 54 4.4.2 Distinguishing Individuals and Categories . 59 4.4.3 Types of Predication . 61 4.4.4 Meager Individual Concepts . 65 4.5 Coding of Events . 68 4.5.1 State of the Art . 68 4.5.2 Extending the Formalism . 71 4.6 Lexicalization . 73 5 Expressing Desires 75 5.1 Simulating Goal-Directed Utterance Selection . 75 5.2 Theoretical Framework . 77 5.2.1 Value Function and Forward Model . 77 5.3 The Acquisition Environment . 80 5.4 Simulations . 84 5.4.1 Value Function . 84 5.4.2 Language Learning . 84 5.4.3 Learning without Observation . 86 5.4.4 Muted Agents . 86 5.5 Discussion . 86 CONTENTS v 5.5.1 Summary of Simulation Results . 86 5.5.2 Basal Ganglia and the Value Function . 87 5.5.3 Cerebellum and the Forward model . 88 5.5.4 Involvement of Other Brain Areas . 89 5.5.5 Alternative Models of Language Use . 89 5.6 Conclusion . 91 5.7 Technical Details . 91 5.7.1 Rules . 91 5.7.2 Parameters of the Simulations . 92 6 Speech Acts 99 6.1 Increasing the Speech Act Variety . 99 6.2 Questions . 100 6.2.1 Knowledge . 100 6.2.2 Learning the Value of Knowledge . 101 6.2.3 Predicting the Effect of Questions . 105 6.2.4 A Probabilistic Internal Model . 106 6.2.5 Learning to ask Questions . 107 6.3 Assertions . 111 6.3.1 Complex Desires . 111 6.3.2 Communicative and other Intentions . 114 6.3.3 Reasons to Change Knowledge . 116 6.3.4 The Social Value of Declaratives . 116 6.4 Selecting Among Different Speech Acts . 118 6.4.1 The Environment . 118 6.4.2 The Social Score . 119 6.4.3 Verbal Actions . 120 6.4.4 The Mathematical Framework . 121 6.5 Speech Acts and Reference . 124 7 Compositionality 125 7.1 Definition . 125 7.2 A Simple Extension of the Architecture . 125 7.2.1 Overview . 125 7.2.2 The Simulation . 126 7.2.3 A Test of Compositionality . 127 7.2.4 Application of the Test . 128 7.3 Using Conceptual Binding . 129 7.3.1 Simulation . 129 7.3.2 Results and Discussion . ..
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