Crocker / Siekmann Resource-Adaptive Cognitive Processes

1. Auflage 2010
ISBN: 978-3-540-89408-7
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 424 Seiten

Reihe: Cognitive Technologies

This book explores the adaptation of cognitive processes to limited resources. It deals with resource-bounded and resource-adaptive cognitive processes in human information processing and human-machine systems plus the related technology transfer issues.

Autoren/Hrsg.

Siekmann, Jörg

Crocker, Matthew W.

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Contributors;10
4;Resource-Adaptive Cognitive Processes;13
4.1;Jörg Siekmann and Matthew W. Crocker;13
4.1.1;1 Background;13
4.1.2;2 Resource-Adaptive Cognitive Processes;14
4.1.3;3 What Is a Resource-Adaptive Cognitive Process?;16
4.1.3.1;3.1 What Is a Resource-Limited Process?;16
4.1.3.2;3.2 How Can We Allocate the Given Resources to a Specific Task?;17
4.1.3.3;3.3 How Can We Allocate Resources That Are NotA Priori Defined?;17
4.1.3.4;3.4 Can We Postulate Indirectly Observable Resources That Help to Explain Experimental Data?;17
4.1.3.5;3.5 In a Multimodal Context, How Are Diverse Knowledge Resources Exploited?;18
4.1.4;4 Structure of This Volume;18
4.1.4.1;4.1 Part I: Resource-Bounded Cognitive Processes in Human Information Processing;18
4.1.4.2;4.2 Part II: Resource-Adaptive Processes in Human--Machine Interaction;20
4.1.4.3;4.3 Part III: Resource-Adaptive Rationality in Machines;21
4.1.5;References;21
5;Part I Resource-Bounded Cognitive Processes in Human Information Processing;23
5.1;Visuo-spatial Working Memory as a Limited Resource of Cognitive Processing;24
5.1.1;Hubert D. Zimmer, Stefan Münzer, and Katja Umla-Runge;24
5.1.1.1;1 The Concept of a Resource-Limited Working Memory;24
5.1.1.2;2 Components and Capacities of Visual Working Memory;27
5.1.1.3;3 Working Memory and Higher Cognitive Performances;32
5.1.1.4;4 Neural Structures Underlying Working Memory;34
5.1.1.5;5 Visual Working Memory in an Applied Context;37
5.1.1.6;References;40
5.2;From Resource-Adaptive Navigation Assistance to Augmented Cognition ;46
5.2.1;Hubert D. Zimmer, Stefan Münzer, and Jörg Baus;46
5.2.1.1;1 Introduction;46
5.2.1.2;2 Resources;46
5.2.1.2.1;2.1 User's Resources;47
5.2.1.2.2;2.2 System's Resources;52
5.2.1.3;3 Goals;54
5.2.1.4;4 Assistance Systems of the Future: Augmented Cognition;57
5.2.1.5;References;60
5.3;Error-Induced Learning as a Resource-Adaptive Process in Young and Elderly Individuals;65
5.3.1;Nicola K. Ferdinand, Anja Weiten, Axel Mecklinger, and Jutta Kray;65
5.3.1.1;1 Introduction;65
5.3.1.2;2 Error Monitoring, ERN/Ne, and Dopamine;66
5.3.1.3;3 The Relevance of Learning Intention;68
5.3.1.4;4 Error-Induced Learning in the Elderly;70
5.3.1.5;5 Methods and Procedure;71
5.3.1.6;6 Results;73
5.3.1.6.1;6.1 Reaction Times;73
5.3.1.6.2;6.2 Error Rates;75
5.3.1.6.3;6.3 Speed-Accuracy Trade-Off;76
5.3.1.6.4;6.4 ERPs for Committed Errors;78
5.3.1.6.5;6.5 ERPs for Perceived Errors;79
5.3.1.7;7 Discussion;80
5.3.1.8;References;84
5.4;An ERP-Approach to Study Age Differences in Cognitive Control Processes;87
5.4.1;Jutta Kray and Ben Eppinger;87
5.4.1.1;1 Introduction;87
5.4.1.2;2 Cognitive Flexibility and Inhibition Limitations in Older Adults;88
5.4.1.2.1;2.1 Age Differences in Task Switching;88
5.4.1.2.2;2.2 Age Differences in Interference Control;89
5.4.1.2.3;2.3 Interactions Between Cognitive Control Processes and Their Temporal Dynamics;90
5.4.1.3;3 Methods Applied;91
5.4.1.3.1;3.1 Participants;91
5.4.1.3.2;3.2 Procedure;91
5.4.1.3.3;3.3 Data Recordings;92
5.4.1.4;4 Results;92
5.4.1.4.1;4.1 Behavioural Results;92
5.4.1.4.2;4.2 ERP-Results;94
5.4.1.4.2.1;4.2.1 Age Differences in ERP Correlates of Task-Preparation Processes;94
5.4.1.4.2.2;4.2.2 Age Differences in ERP Correlates of Interference Control;96
5.4.1.5;5 Summary and Conclusions;99
5.4.1.6;References;100
5.5;Simulating Statistical Power in Latent Growth Curve Modeling: A Strategy for Evaluating Age-Based Changes in Cognitive Resources;104
5.5.1;Timo von Oertzen, Paolo Ghisletta, and Ulman Lindenberger;104
5.5.1.1;1 Introduction;104
5.5.1.2;2 The Latent Growth Curve Model;106
5.5.1.3;3 Least Squares and Minus Two Log Likelihood Fitting Functions;108
5.5.1.3.1;3.1 Minimization of the Fitting Functions;109
5.5.1.3.2;3.2 Inadmissable Estimation Areas;112
5.5.1.4;4 General Simulation Procedure;114
5.5.1.4.1;4.1 Data Generation;114
5.5.1.4.2;4.2 Data Selection;115
5.5.1.4.3;4.3 Evaluation Criteria;116
5.5.1.4.4;4.4 Summarizing the Simulation Procedure;118
5.5.1.5;5 An Illustration;119
5.5.1.5.1;5.1 Population Parameters;119
5.5.1.5.2;5.2 Data Selection;120
5.5.1.5.3;5.3 Parameters of Focus;121
5.5.1.5.4;5.4 Definition of Power;121
5.5.1.5.5;5.5 Results;121
5.5.1.6;6 Discussion and Outlook;123
5.5.1.7;References;124
5.6;Conflicting Constraints in Resource-Adaptive Language Comprehension;127
5.6.1;Andrea Weber, Matthew W. Crocker, and Pia Knoeferle;127
5.6.1.1;1 Introduction;127
5.6.1.1.1;1.1 Incrementality;128
5.6.1.1.2;1.2 Multiple Constraints;128
5.6.1.1.3;1.3 Anticipation in Situated Comprehension;130
5.6.1.2;2 Varying Constraints;130
5.6.1.2.1;2.1 Discourse Information and Structural Preferences;131
5.6.1.2.2;2.2 Prosodic Information and Structural Preferences;134
5.6.1.2.3;2.3 Semantic Information and Lexical Preferences;137
5.6.1.2.4;2.4 Semantic Information and Visual Context;140
5.6.1.2.5;2.5 The Influence of the Scene: Depicted Events and Their Priority;142
5.6.1.3;3 Conclusions;146
5.6.1.4;References;147
5.7;The Evolution of a Connectionist Model of Situated Human Language Understanding;150
5.7.1;Marshall R. Mayberry and Matthew W. Crocker;150
5.7.1.1;1 Introduction;150
5.7.1.2;2 Experimental Findings;151
5.7.1.2.1;2.1 Anticipation in Unambiguous Utterances;152
5.7.1.2.2;2.2 Anticipation in Ambiguous Utterances;153
5.7.1.2.3;2.3 Coordinated Interplay Account;156
5.7.1.3;3 Connectionist Models;157
5.7.1.3.1;3.1 Multimodal Integration Using Event Layers;158
5.7.1.3.1.1;3.1.1 Input Data, Training, and Testing;159
5.7.1.3.1.2;3.1.2 Results;160
5.7.1.3.2;3.2 Multimodal Integration Using Attention;161
5.7.1.3.2.1;3.2.1 Input Data, Training, and Testing;162
5.7.1.3.2.2;3.2.2 Results;163
5.7.1.4;4 Conclusion;172
5.7.1.5;References;173
6;Part II Resource-Adaptive Processes in Human--Machine Interaction;175
6.1;Assessment of a User's Time Pressure and Cognitive Load on the Basis of Features of Speech;176
6.1.1;Anthony Jameson, Juergen Kiefer, Christian Müller, Barbara Großmann-Hutter, Frank Wittig, and Ralf Rummer;176
6.1.1.1;1 Introduction;176
6.1.1.1.1;1.1 Reasons for Variation in Cognitive Load and Time Pressure;176
6.1.1.1.2;1.2 Why Automatic Adaptation?;177
6.1.1.2;2 Possible Forms of Adaptation;178
6.1.1.2.1;2.1 Interruption of Communication;178
6.1.1.2.2;2.2 Timing and Form of Notifications;179
6.1.1.2.3;2.3 Dialog Strategy;179
6.1.1.2.4;2.4 Other Forms of Adaptation to Resource Limitations;180
6.1.1.3;3 Ways of Recognizing Resource Limitations ;181
6.1.1.3.1;3.1 Recognizing Likely Causes of Resource Limitations ;181
6.1.1.3.2;3.2 Physiological Indicators;181
6.1.1.3.2.1;3.2.1 Heart Rate Variability;182
6.1.1.3.2.2;3.2.2 Pupil Diameter;182
6.1.1.3.2.3;3.2.3 Other Indices;183
6.1.1.3.2.4;3.2.4 Comments;183
6.1.1.3.3;3.3 Evidence in the User's Behavior with the System;183
6.1.1.3.3.1;3.3.1 Evidence in the User's Motor Behavior ;183
6.1.1.3.3.2;3.3.2 Evidence in the User's Speech ;184
6.1.1.4;4 Experiments: Introduction;184
6.1.1.4.1;4.1 Earlier Research on Speech Indicators;184
6.1.1.4.1.1;4.1.1 Distinction from Other Topics;184
6.1.1.4.1.2;4.1.2 Effects of Cognitive Load;185
6.1.1.4.1.3;4.1.3 Effects of Time Pressure ;185
6.1.1.5;5 Experimental Method;186
6.1.1.5.1;5.1 Purpose of Experiments;186
6.1.1.5.2;5.2 Method for Experiment 1;186
6.1.1.5.2.1;5.2.1 Materials;186
6.1.1.5.2.2;5.2.2 Design;186
6.1.1.5.2.3;5.2.3 Procedure;188
6.1.1.5.2.4;5.2.4 Subjects;188
6.1.1.5.2.5;5.2.5 Coding and Rating of Speech;188
6.1.1.5.3;5.3 Method for Experiment 2 ;189
6.1.1.6;6 Experimental Results ;190
6.1.1.6.1;6.1 Statistical Analyses;190
6.1.1.6.2;6.2 Number of Syllables;190
6.1.1.6.3;6.3 Articulation Rate;191
6.1.1.6.4;6.4 Silent Pauses;192
6.1.1.6.5;6.5 Filled Pauses;193
6.1.1.6.6;6.6 Hesitations;194
6.1.1.6.7;6.7 Onset Latency;195
6.1.1.6.8;6.8 Disfluencies;195
6.1.1.6.9;6.9 Discussion;196
6.1.1.7;7 Learning of User Models;196
6.1.1.7.1;7.1 Bayesian Network Structure;197
6.1.1.7.2;7.2 Quantitative Parameters;199
6.1.1.7.3;7.3 Learning the Quantitative Parameters;200
6.1.1.8;8 Evaluation of the User Models;200
6.1.1.8.1;8.1 Procedure;200
6.1.1.8.2;8.2 Results;201
6.1.1.8.2.1;8.2.1 Recognizing Time Pressure;201
6.1.1.8.2.2;8.2.2 Recognizing Navigation;203
6.1.1.8.2.3;8.2.3 Dispensing with Individual Indicators;203
6.1.1.8.3;8.3 Discussion ;204
6.1.1.9;9 Summary of Contributions and Remaining Work;206
6.1.1.10;References;206
6.2;The Shopping Experience of Tomorrow: Human-Centered and Resource-Adaptive;210
6.2.1;Wolfgang Wahlster, Michael Feld, Patrick Gebhard, Dominikus Heckmann, Ralf Jung, Michael Kruppa, Michael Schmitz, Lübomira Spassova, and Rainer Wasinger;210
6.2.1.1;1 Introduction;210
6.2.1.1.1;1.1 Overview Described Within a Motivating Scenario;211
6.2.1.2;2 Dialogue Shell of Talking Products;212
6.2.1.2.1;2.1 Modelling Personality in Voices;213
6.2.1.2.2;2.2 Expressing Personality in Dialogues;213
6.2.1.3;3 Mobile ShopAssist;215
6.2.1.4;4 Product Associated Displays;217
6.2.1.5;5 Personalized Ambient Soundscape Notification;218
6.2.1.5.1;5.1 Introduction to Ambient Audio Notification;219
6.2.1.5.2;5.2 Ambient Soundscapes and Audio Notification Cues;219
6.2.1.5.3;5.3 Applications and Shopping Scenario;221
6.2.1.6;6 Virtual Room Inhabitant;223
6.2.1.7;7 Live Acquisition of User Profile Data from Speech;227
6.2.1.8;8 Ubiquitous User Modeling with UbisWorld;231
6.2.1.9;9 Modeling Affect;234
6.2.1.9.1;9.1 Affect Taxonomy;234
6.2.1.9.2;9.2 Affect Computation;234
6.2.1.9.2.1;9.2.1 Mood Changes;236
6.2.1.9.2.2;9.2.2 Appraisal Based Affect Computation;238
6.2.1.10;10 Conclusions;238
6.2.1.11;References;239
6.3;Seamless Resource-Adaptive Navigation ;243
6.3.1;Tim Schwartz, Christoph Stahl, Jörg Baus, and Wolfgang Wahlster;243
6.3.1.1;1 Introduction;243
6.3.1.2;2 REAL and BPN as the Basis of our Extensions;244
6.3.1.3;3 Overall System Architecture of the New Navigation Framework;245
6.3.1.4;4 Providing Map Material for Pedestrian Navigation;246
6.3.1.5;5 The Always Best Positioned Paradigm;251
6.3.1.5.1;5.1 Exocentric and Egocentric Localization;251
6.3.1.5.2;5.2 LORIOT;252
6.3.1.5.2.1;5.2.1 Estimation of the User Position;253
6.3.1.5.2.2;5.2.2 Orientation Estimation;256
6.3.1.5.2.3;5.2.3 Orientation Information Through Infrared Beacons;256
6.3.1.5.2.4;5.2.4 Orientation Information Through Active RFID Tags;257
6.3.1.5.2.5;5.2.5 Fusion of Orientation Information Through Bayesian Networks;257
6.3.1.5.2.6;5.2.6 Decomposition of a Direction Vector into Evidence Values;258
6.3.1.5.2.7;5.2.7 Composition of a Direction Vector out of Evidence Values;259
6.3.1.5.2.8;5.2.8 Example Calculation;259
6.3.1.6;6 Implementing a Seamless, Proactive User Interface;260
6.3.1.6.1;6.1 Hybrid Navigation Visualization;260
6.3.1.6.2;6.2 VISTO: Videos for Spatial Orientation;262
6.3.1.6.2.1;6.2.1 The Ubiquitous To-Do Organizer UBIDOO;263
6.3.1.6.2.2;6.2.2 The User Interface of VISTO;265
6.3.1.7;7 Summary;267
6.3.1.8;References;267
6.4;Linguistic Processing in a Mathematics Tutoring System: Cooperative Input Interpretation and Dialogue Modelling;270
6.4.1;Magdalena Wolska, Mark Buckley, Helmut Horacek, Ivana Kruijff-Korbayová, and Manfred Pinkal;270
6.4.1.1;1 Introduction;270
6.4.1.2;2 Research Setting;272
6.4.1.3;3 The Language of Informal Proofs;273
6.4.1.4;4 Baseline Processing;277
6.4.1.5;5 Aspects of Cooperative Interpretation;279
6.4.1.5.1;5.1 Parsing;280
6.4.1.5.2;5.2 Domain Modelling;281
6.4.1.5.3;5.3 Domain-Specific Anaphora;282
6.4.1.5.4;5.4 Flexible Formula Analysis and Disambiguation;283
6.4.1.6;6 Modelling Dialogue for Mathematics Tutoring;284
6.4.1.7;7 Related Work;287
6.4.1.8;8 Conclusions;288
6.4.1.9;References;289
6.5;Resource-Bounded Modelling and Analysis of Human-Level Interactive Proofs;293
6.5.1;Christoph Benzmüller, Marvin Schiller, and Jörg Siekmann;293
6.5.1.1;1 Introduction;293
6.5.1.2;2 The Need for Experiments and Corpora;295
6.5.1.3;3 Main Challenges and Resources for Proof Tutoring;298
6.5.1.3.1;3.1 B: Mathural Processing and Mathural Generation;298
6.5.1.3.2;3.2 C: Dialogue State and Proof Management;300
6.5.1.3.3;3.3 D: Proof Step Evaluation;300
6.5.1.3.4;3.4 E: Tutorial Context;301
6.5.1.3.5;3.5 F: Failure Analysis;302
6.5.1.3.6;3.6 G: Didactic Strategies, Feedback Generation and Hinting;302
6.5.1.3.7;3.7 H: Flexible Dialogue Modelling;302
6.5.1.4;4 Dynamic Proof Step Evaluation with MEGA;303
6.5.1.4.1;4.1 Proof Management, Correctness Analysis and Content Underspecification;303
6.5.1.4.2;4.2 Granularity Analysis;305
6.5.1.4.3;4.3 Learning Granularity Evaluation;305
6.5.1.4.4;4.4 Student Modelling;308
6.5.1.4.5;4.5 Further Work;309
6.5.1.5;5 Didactic Strategies and Dialogue Modelling;309
6.5.1.5.1;5.1 Didactic Strategies and Hinting;309
6.5.1.5.2;5.2 Dialog Modelling;310
6.5.1.6;6 Related Work and Conclusion;310
6.5.1.7;References;311
7;Part III Resource-Adaptive Rationality in Machines;314
7.1;Comparison of Machine Learning Techniques for Bayesian Networks for User-Adaptive Systems;315
7.1.1;Frank Wittig;315
7.1.1.1;1 Introduction: Bayesian Networks in User-Adaptive Systems;315
7.1.1.2;2 A Framework for Learning Bayesian Networks for User-Adaptive Systems;316
7.1.1.2.1;2.1 Machine Learning in User-Adaptive Systems;316
7.1.1.2.2;2.2 Learning Bayesian Networks for User-Adaptive Systems;317
7.1.1.2.2.1;2.2.1 Learning Offline (Batch) and Learning Online (Adaptation);317
7.1.1.2.2.2;2.2.2 Exploiting Experimental and Usage Data for Learning;318
7.1.1.2.2.3;2.2.3 Learning Probabilities and Structure;318
7.1.1.2.2.4;2.2.4 Learning Interpretable Bayesian Network User Models;319
7.1.1.2.3;2.3 Learning Bayesian Network User Models in the READY Project;319
7.1.1.3;3 The Structural View: An Evaluation of Bayesian Networks User Model Learning ;321
7.1.1.3.1;3.1 Combined Learning Approaches;321
7.1.1.3.2;3.2 Evaluation Procedure;323
7.1.1.3.3;3.3 Results;325
7.1.1.3.4;3.4 Discussion;328
7.1.1.4;4 Conclusion;329
7.1.1.5;References;334
7.2;Scope Underspecification with Tree Descriptions: Theory and Practice;337
7.2.1;Alexander Koller, Stefan Thater, and Manfred Pinkal;337
7.2.1.1;1 Introduction;337
7.2.1.2;2 Dominance-Based Scope Underspecification;338
7.2.1.2.1;2.1 Dominance Constraints;340
7.2.1.2.2;2.2 Dominance Graphs;341
7.2.1.2.3;2.3 Configurations and Solved Forms;342
7.2.1.3;3 Solving Dominance Constraints and Graphs;343
7.2.1.3.1;3.1 A Saturation Algorithm;345
7.2.1.3.2;3.2 Reduction to Set Constraints;346
7.2.1.3.3;3.3 A Graph-Based Solver;348
7.2.1.3.4;3.4 The Chart Solver;349
7.2.1.4;4 Practical Scope Underspecification;351
7.2.1.4.1;4.1 Minimal Recursion Semantics as Dominance Constraints;351
7.2.1.4.2;4.2 Experiments with the English Resource Grammar;353
7.2.1.4.2.1;4.2.1 Evaluating the Net Hypothesis;354
7.2.1.4.2.2;4.2.2 Grammar Verification;354
7.2.1.4.3;4.3 Redundancy Elimination;355
7.2.1.5;5 Annotating Scope;357
7.2.1.6;6 Conclusion;359
7.2.1.7;References;362
7.3;Dependency Grammar:Classification and Exploration;365
7.3.1;Ralph Debusmann and Marco Kuhlmann;365
7.3.1.1;1 Introduction;365
7.3.1.2;2 Dependency Structures;366
7.3.1.3;3 Dependency Structures and Lexicalized Grammars;368
7.3.1.3.1;3.1 Lexicalized Grammars Induce Dependency Structures;368
7.3.1.3.2;3.2 The Algebraic View on Dependency Structures;370
7.3.1.3.3;3.3 Regular Dependency Grammars;371
7.3.1.4;4 Extensible Dependency Grammar;373
7.3.1.4.1;4.1 Dependency Multigraphs;373
7.3.1.4.2;4.2 Grammars;374
7.3.1.5;5 Modeling Complex Word Order Phenomena;375
7.3.1.5.1;5.1 Scrambling;376
7.3.1.5.2;5.2 A Topological Model of Scrambling;376
7.3.1.6;6 A Relational Syntax--Semantics Interface;377
7.3.1.6.1;6.1 Dominance Constraints;378
7.3.1.6.2;6.2 The Interface;379
7.3.1.7;7 Modeling Regular Dependency Grammars;380
7.3.1.8;8 Grammar Development Environment;382
7.3.1.8.1;8.1 Parser;382
7.3.1.8.2;8.2 Large-Scale Parsing;383
7.3.1.9;9 Conclusion;385
7.3.1.10;References;385
7.4;OMEGA: Resource-Adaptive Processes in an Automated Reasoning System;389
7.4.1;Serge Autexier, Christoph Benzmüller, Dominik Dietrich, and Jörg Siekmann;389
7.4.1.1;1 Motivation and Historical Background;389
7.4.1.1.1;1.1 The OMEGA Initiative;392
7.4.1.2;2 Resource-Adaptive Proof Search;394
7.4.1.2.1;2.1 Human-Oriented High-Level Proofs;394
7.4.1.2.1.1;2.1.1 Inferences;395
7.4.1.2.1.2;2.1.2 Application Direction of an Inference;397
7.4.1.2.1.3;2.1.3 Representation of Proof;398
7.4.1.2.2;2.2 Searching for a Proof;400
7.4.1.2.2.1;2.2.1 Knowledge-Based Proof Search;400
7.4.1.2.2.2;2.2.2 Reactive Proof Search;403
7.4.1.3;3 Knowledge as a Resource;405
7.4.1.3.1;3.1 Managing Mathematical Knowledge;405
7.4.1.3.2;3.2 Formalising Mathematical Knowledge;406
7.4.1.3.3;3.3 From Assertions to Inferences;406
7.4.1.3.4;3.4 From Inferences to Planner Methods;408
7.4.1.3.5;3.5 From Inferences to Agents;409
7.4.1.4;4 Specialised Computing and Reasoning Resources;409
7.4.1.5;5 mega as an Adaptive Resource;411
7.4.1.5.1;5.1 Adaptation to Users with Different Skills;412
7.4.1.5.2;5.2 Adaptation to Different Software Systems;414
7.4.1.5.2.1;5.2.1 Checking the Correctness;415
7.4.1.5.2.2;5.2.2 Cognitive Proof States;416
7.4.1.6;6 Future Research;417
7.4.1.7;References;418