Handbook of Categorization in Cognitive Science (eBook)

Cover 1
Handbook of Categorization in Cognitive Science 4
Preface 6
Table of Contents 8
List of Contributors 30
Bridging the Category Divide 38
Introduction 39
Organization of the book 39
Major common themes 44
The notions of category and categorization 44
The nature of categories: Discrete, vague, or other 46
Are there modality effects on categories? 47
Are there universal categories? Are there innate categories? 48
Bridging the category divide 50
References 52
Part 1 – Categorization in Cognitive Science 54
To Cognize is to Categorize: Cognition is Categorization 56
Abstract 57
Sensorimotor systems 58
Invariant sensorimotor features (“affordances”) 58
Categorization 58
Learning 59
Innate categories 60
Learned categories 60
Supervised learning 61
Instrumental (operant) learning 61
Color categories 62
Categorical perception 62
Learning algorithms 63
Unsupervised learning 64
Supervised learning 64
Vanishing intersections? 65
Direct sensorimotor invariants 66
Abstraction and hearsay 67
Abstraction and amnesia 67
Invariance and recurrence 68
Feature selection and weighting 69
Discrimination versus categorization 69
Recoding and feature selection 70
Learned categorical perception and the Whorf hypothesis 71
Uncertainty reduction 72
Explicit learning 73
Categorization is abstraction 73
Sensorimotor grounding: direct and derivative 73
The adaptive advantage of language: hearsay 74
Absolute discriminables and affordances 76
Cognitive science is not ontology 76
Cognition is categorization 77
Appendix A. There is nothing wrong with the “classical theory” of categorization 77
Appendix B. Associationism begs the question of categorization 78
References 79
A Modular Approach to Grammatical Categories Evidence from Language Diversity and Contact 82
Abstract 83
Introduction 84
Modularity and mismatch 85
Grammaticalization: The case of pe in Sranan (Suriname) 89
Mismatches in complexity of representations: The case of ku in Cuzco Quechua (Peru) 91
Lexical nondistinctness 92
The case of timap in Palikur 92
‘For’ prepositions to become complementizers 93
Adjectives versus adverbs 94
Lexical overspecification: Dutch gender and definiteness 94
The status of null elements: Subjects in Sranan and Papiamentu 95
Partially overlapping categories: predicate adjectives in Creoles 96
Differences in lexical richness 97
Evidence from language contact 98
Otomanguean-Spanish language contact 99
Borrowing of content words in Salishan languages 102
Concluding remarks 104
Appendix 104
References 105
Philosophical Analysis as Cognitive Psychology: The Case of Empty Concepts* 108
Abstract 109
Introduction 110
Misadventures of the Classical View 110
Terminological issues 112
Existential vs. (purely) intentional usage 112
Concepts as between representations and referents 114
The inadequacies of Externalism 114
The need for internal roles 119
The Quinean challenge 119
The analytic data 120
Rivals to the Analytic Explanation 122
Quine 122
Fodor 123
Conclusion 124
References 125
Categories and Cognitive Anthropology* 128
Abstract 129
Introduction 130
Cognition and culture, universalism and relativism 130
Paradigms and taxonomies 131
Kinship terminologies 138
Color classification 142
Ethnobiology 146
Towards a science of the stimulus 151
References 153
Categorization in Neuroscience: Brain Response to Objects and Events 156
Abstract 156
Introduction 157
Representing object categories in the brain 158
Category-specific representation 159
Feature-specific representation 160
Process-specific representation 162
Summary 162
Acquiring category knowledge 163
Summary 167
Categorizing actions and events 167
The nature of event knowledge 167
When categorization of action fails 169
The perception of events 170
Summary 173
Conclusion 173
References 174
Categorization in Cognitive Computer Science 178
Abstract 178
Computation in cognitive science 179
The great categorization debates 181
From local features to global structures 185
Categorization and reasoning 190
Levels of cognition 195
References 198
Part 2 – Semantic Categories 202
Semantic Categorization 204
Abstract 204
Introduction 205
The notional approach to lexical categories 206
The notional approach to lexical subcategories 207
Structural approach to semantic categories 209
Coordinators and subordinators 212
English nouns 218
Conclusion 221
Acknowledgments 221
References 221
Emotion Categories across Languages 224
Abstract 225
Introduction 226
Methods of assessing cultural emotion systems 228
The Method of Translation 228
The Method of Mapping 235
Theories of emotion 247
Cross-cultural scenarios as a tool to compare emotion categories 250
Conclusion 255
References 257
The World Color Survey Database 260
Abstract 261
Introduction 262
The WCS: History and methodology 262
Data processing and analysis 265
Cleaning the data 269
Original format of the data and creation of the WCS Online Data Archive 269
Uses of the WCS archive 271
Universals of color naming 271
Variation in color naming 274
Conclusion 277
References 277
Atoms, Categorization and Conceptual Change 280
Abstract 280
Introduction 281
Theories of concepts 282
The ancient concept of an atom 283
Revival of the concept of the atom 285
Modern development of the concept of an atom 286
Theories and meaning 289
Conclusion 290
References 290
Relations between Language and Thought: Individuation and the Count/Mass Distinction* 292
Abstract 293
Introduction 294
Strong discontinuity proposals 297
Quine 297
Abstract individuation in language and thought 299
Weak discontinuity proposals 300
Crosslinguistic studies 300
Language-on-language effects 303
Material and shape cues in labeling and categorization 305
Conclusion 308
References 308
Definitions in Categorization and Similarity Judgments 314
Abstract 315
Introduction 316
Importance rating and property selection 321
Method 321
Results 323
Categorization judgments 325
Method 325
Results 327
Discussion 333
Similarity judgments 334
Method 334
Results 335
General discussion 337
References 339
Why (Most) Concepts aren’t Categories 342
Abstract 342
Introduction 343
Species are not categories 343
Three kinds of (Aristotelian) “substances” 344
Historical kinds 344
Eternal kinds 345
Individuals 346
Concepts of individuals 347
Concepts of substances more generally 348
Substances encountered through language 349
References 352
Part 3 – Syntactic Categories 354
Lexical, Functional, Crossover, and Multifunctional Categories* 356
Abstract 357
Introduction 358
Categories as feature bundles 358
The system 358
Natural classes 359
Unnatural classes 361
Categories and phrase structure 362
Lexical and functional categories 363
Articulation of functional categories 364
Articulation below N and V 366
Crossover and multifunctionality 367
Crossover projections 367
Multifunctional categories 372
Where do categorial distinctions reside? 373
Conclusions 381
References 382
Isolating-Monocategorial-Associational Language 384
Abstract 385
Introduction 386
What IMA Language is Like 386
Isolating 387
Monocategorial 387
Associational 388
Where IMA Language Is Found 391
Semiotics 391
Phylogeny 393
Ontogeny 395
Typology 396
Riau Indonesian: overview 397
Riau Indonesian: analysis 401
Riau Indonesian: A Relative IMA Language 411
Cognition 412
Acknowledgments 414
References 414
Categories in Quebec Sign Language: Reflections on Categorization across Modalities 418
Abstract 418
The categories of lexical items 419
Traditional categorization applied to LSQ 421
Nouns and verbs 424
Pronouns and definite determiners 424
Pronouns in oral languages and in sign languages 425
The effects of perceptual substances on linguistic forms 426
Explaining the different properties 427
Participant deixis 427
Inclusion/exclusion of the speaker or hearer 428
Spatial deixis: proximity, distance, visibility (for third person) 428
Number 428
Class/gender 430
Case markings 431
Summary 433
Consequences for linguistic categorization and universals 433
References 435
Syntactic Categories in Signed versus Spoken Languages 438
Abstract 439
Introduction 440
Lexical categories 440
Grammatical structures 442
Subordination 442
“Spatial syntax” 443
Pronouns 443
Verb agreement 445
Analyses of spatial syntax 446
Complications 448
The use of space to represent space 449
Word order 450
Conclusion 454
Acknowledgments 455
Appendix. Notational conventions 455
References 456
On Syntactic Categories 460
References 467
Part 4 – Acquisition of Categories 468
The Acquisition of Grammatical Categories: the State of the Art 470
Abstract 470
Grammatical categories 471
Two-word utterances and their analysis 472
A semantic approach to grammatical categorization: Semantic bootstrapping 473
Distributional learning 476
Word order 476
Inflection and inflectional class 478
Function words 482
Word classes 484
Other cues to grammatical category learning 486
Models of distributional learning 486
Constraining the search space 487
Conclusion 488
References 489
Semantic Categories in Acquisition 496
Abstract 496
Introduction 497
Space 498
Shape 502
Adding common ground 503
Conceptual domains and lexical options 504
Adding meaning in the course of conversation 509
Universals in mapping? 510
Conclusion 513
References 514
Early Syntactic Categories in Infants' Language 518
Abstract 518
Introduction 519
The acquisition of grammatical categories and the earliest binary distinction of function words and content words 519
Input speech and the categorization of function words and content words 523
Function words and language acquisition 525
Conclusions 529
Acknowledgment 529
References 530
Acquiring Auditory and Phonetic Categories* 534
Abstract 534
Introduction 535
Testing category learning 537
Learning of nonspeech categories 539
Learning of speech categories 543
Conclusion 547
References 548
Syntactic Categories in Second Language Acquisition 552
Abstract 552
Introduction 553
Lexical and functional categories 553
Lexical categories in L2 acquisition 554
Functional categories in acquisition: Issues of evidence 556
Functional categories in the L2 initial state and in L2 development 559
Morphology-before-syntax 559
Syntax-before-morphology 560
Acquiring versus losing categories and features 561
Discussion 566
References 567
The Development of Categories in the Linguistic and Nonlinguistic Domains: the Same or Different? 572
References 578
Part 5 – Neuroscience of Categorization and Category Learning 582
Multiple Systems of Perceptual Category Learning: Theory and Cognitive Tests 584
Abstract 585
Introduction 586
Two Category-Learning Tasks 587
COVIS 587
The COVIS explicit system 589
Switching attention in the explicit system 591
Long-term storage of explicit category knowledge 593
The COVIS procedural-learning system 594
Competition between the COVIS explicit and implicit systems 597
Dissociations between rule-based and information-integration category learning 598
Conclusions 600
Appendix A 601
A 1 Network implementation of the explicit system 601
A 2 Network implementation of the implicit system 603
Acknowledgment 605
References 605
The Neuropsychology of Perceptual Category Learning* 610
Abstract 611
Introduction 612
Competition between verbal and implicit systems (COVIS) 612
Testing a priori Predictions of COVIS 615
Perceptual category learning in neurological patients 618
Nonlinear information-integration category learning in amnesia 619
Model-based analyses 621
Nonlinear information-integration category learning in striatal-damaged patients 622
Rule-based category learning in PD 624
Further study of information-integration category learning in PD 628
Brief summary of the results 630
General discussion 632
References 634
Neural Regions Associated with Categorical Speech Perception and Production 638
Abstract 638
Introduction 639
Evidence for categorical speech processing 639
Prefrontal regions and motor speech categories 642
Temporal–parietal regions and acoustic speech categories 646
Cerebellar contributions to categorical production and perception 648
Concluding remarks 649
References 650
Part 6 – Categories in Perception and Inference 654
Situated Conceptualization 656
Abstract 657
Introduction 658
Conceptual systems 658
Semantic memory 658
Grounding the conceptual system in the modalities 659
Modal reenactments of perception, action, and introspection 660
Storage of modality-specific states that arise in feature systems 660
Reenactments of modality-specific states 660
Simulators and simulations 661
Simulators 661
Simulations 662
Sources of simulators 662
Situated conceptualizations 663
Multimodal simulations implement situated conceptualizations 663
Entrenched situated conceptualizations 664
Inference via pattern completion 665
Pattern completion with entrenched situated conceptualizations 665
The statistical character of inference 666
Empirical evidence 666
Behavioral evidence for a modal nonmodular conceptual system 666
Predictions for modular amodal vs. nonmodular modal theories 667
Assessing the presence of modality-specific effects in conceptual processing 668
Occlusion during property generation 668
Size during property verification 669
Shape during property verification 670
Modality switching during property verification 671
Shape and orientation during comprehension 672
Movement direction in comprehension 672
Further evidence for simulation from comprehension studies 673
Behavioral evidence for embodiment in social cognition 673
Neural evidence for a modal nonmodular conceptual system 674
Category-specific deficits 674
Neuroimaging studies of category knowledge 674
Evidence for situated conceptualizations 676
Inferences about goal-relevant properties of the focal category 677
Evidence for setting inferences 677
Evidence for action inferences 678
Evidence for introspective state inferences 679
Evidence for dynamical simulations 680
Conclusion 681
Important issues for future research 682
Amodal symbols 682
Symbolic functions 683
Abstract concepts 683
Acknowledgment 684
References 684
Perceptual and Semantic Reorganization during Category Learning 688
Abstract 689
Introduction 690
Concept learning and perception 690
Object segmentation 691
Experiment 1 692
Method 693
Results and discussion 694
Experiment 2 694
Method 696
Results and discussion 697
Conclusions on perceptual reorganization 699
Semantic reorganization during category learning 701
Integral versus separable dimensions 701
Experiment 3 702
Method 703
Results and discussion 704
Experiment 4 706
Method 706
Results and discussion 706
Conclusions on semantic reorganization 708
Integrating perceptual and semantic reorganization 708
Characterizing psychological features 709
Characterizing featural change 710
Prospects for synthesizing perceptual and semantic reorganization 712
Acknowledgments 713
References 713
The Return of Concept Empiricism 716
Abstract 716
Introduction 717
Concept empiricism 717
Representing and doing: Two faces of concepts 717
Variable mechanisms 721
Perceptual vehicles 723
Innateness 725
Summary 728
The abstract ideas objections 729
References 731
Part 7 – Grounding, Recognition, and Reasoning in Categorization 734
Categorization, Reasoning, and Memory from a Neo-logical Point of View 736
Abstract 737
Introduction 738
Order, Information, and Categories 738
Inferences, Arguments, and Information 738
Logical and Creative Arguments 740
Types of Creative Arguments 740
Two Rather Incompatible Views in Cognitive Science 742
Experiments on Memory and Logical Competence 743
Logical Weakness and Classification of Errors 746
A New Theory of Logical Error and Logical Competence 747
The Cognitive Functions of Logic 748
Corrective Inferences and Cognitive Progress 750
The Fundamental Cognitive Function of Logical Reasoning 752
The Mind as a Dynamic System: Inference and Memory 752
General Conclusions: Categorization, Reasoning, and Memory 753
References 754
Approaches to Grounding Symbols in Perceptual and Sensorimotor Categories 756
Abstract 756
Cognitive symbol grounding 757
The symbol grounding problem 757
Grounding symbols in cognition 758
Linking vision and language: connectionist approaches to category learning and symbol grounding 761
Connectionist modeling of category learning and naming 761
Connectionist modeling of symbol grounding transfer 764
Linking vision, action and language: embodied approaches to language learning and evolution 766
Grounding symbols in simulated agents: The symbolic theft hypothesis 766
The emergence of language in robots 768
Discussion and conclusion 770
References 772
Embodied Categorization* 776
Abstract 777
Introduction: Embodied categorization 778
Purely reactive categorizers 780
The perceptual aliasing problem [Whitehead and Ballard (1991)] 781
Type I versus II problems [Clark and Thornton (1997)] 781
Reactive categorizers that learn 784
Representing categorizers 786
Emulating and simulating categorizers 789
Emulating categorizers 790
Simulating categorizers 792
Simulation of physical categories (folk physics) 794
Simulation of functional categories (folk biology and mechanics) 795
Simulation of intentional categories (folk psychology) 795
Analogizing categorizers 796
Linguistic categorizers 798
Conclusion 798
References 799
Categorization of Objects, Scenes, and Faces through Time 804
Abstract 805
A model of categorization 806
Basic-level literature 809
Discrete processing cycles 810
A Bubbles primer 810
Stimulus Set? 810
Stimulus generation space? 811
The samples? 811
The task? 811
Response? 811
Observers? 812
Fossilized discrete processing cycles 812
What can temporal bubbles reveal about a SLIP categorizer? 814
The need for flexibility and a paradox 815
Limited processing capacity 815
The need for flexibility 816
Back to the paradox 817
Categorization as an iterative process 817
Compulsory feedforward processing sweeps 818
The nature of the information processed during compulsory feedforward sweeps 819
Flexible iterative processing sweeps 819
Empirical evidence for flexible and iterative processing sweeps 821
Deactivation studies 823
General discussion 823
References 825
Adaptive Categorization and Neural Networks* 830
Abstract 831
The problem of divergence 835
The solution: dual Hebbian/anti-Hebbian learning 836
Stabilization 837
Oscillation 838
Linearity 838
Additional properties of the learning rule 838
The Eidos model 839
The letter classification task 840
Methodology 840
Results 841
The problem of convergence 843
The solution: Unlearnin 845
The letter-classification task revisited 846
Methodology 846
Results 846
Current trends: Elimination of spurious attractors 849
Conclusion 850
References 851
A Grounded Mind in a Robotic Body 854
References 857
Part 8 – Machine Category Learning 858
Concept Learning and Nonmonotonic Reasoning¹ 860
Abstract 861
The role of concepts 862
Three kinds of cognitive representations 862
Learning in symbolic systems 863
Learning in connectionist systems 864
Conceptual spaces as a representational framework 864
The origin of quality dimensions 866
Properties and concepts 868
Prototypes and conceptual spaces 869
Learning in conceptual spaces 871
The role of similarity in learning 873
Nonmonotonic aspects of concepts 875
Change from general category to subordinate 876
Context effects 877
Conclusion 878
References 879
Categorization in Symbolic Data Analysis 882
Abstract 883
Introduction 884
Categories, concepts, and symbolic data 885
From individuals to concepts 885
Categories in a database 886
From categories to concepts: reification of a category in a concept 886
Sources of symbolic data 888
Symbolic data tables and their background knowledge, concepts, and categories 889
Symbolic data tables 889
Building a symbolic data table by reification of categories in concepts 889
Description of concepts when the individuals are described by fuzzy data 890
Adding conceptual variables, joining concepts, and the DB2SO module of SODAS 891
Modeling concepts by “symbolic objects,” with certain philosophical aspects 892
Kinds of concepts and intuitive introduction of “symbolic objects” 892
Modeling concepts with four spaces: “individuals,” “concepts,” “descriptions,” and “symbolic objects” 892
Extent of concepts and symbolic objects 893
Syntax of symbolic objects in the case of “assertions” 895
Extent of a symbolic object 895
Concepts: Four approaches 895
Tools for symbolic objects 896
Order between symbolic objects 896
Finding a unique description for a concept: “T-norm of descriptive generalization” 896
Finding several descriptions for a concept 897
Dissimilarities between concepts 898
Finding prototypes from a concept 898
Underlying structures of symbolic objects 898
A generalized conceptual lattice 898
Mathematical framework of a symbolic data analysis 900
Steps and tools for Symbolic Data Analysis 900
Main steps 900
Descriptive SDA in SODAS 901
Overview of SODAS 902
Some advantages of the use of concepts modeled by symbolic objects 902
Overview of SODAS software 902
Final remarks 902
References 903
Category Formation in Self-organizing Embodied Agents 906
Abstract 906
Introduction 907
The method 907
Categories emerging from the interaction between the agent and the environment 908
Finding and remaining in favorable environmental areas 908
Discriminating objects with different shapes on the basis of tactile information 910
Behavior emerging from the dynamic interaction between the agent and its environment 913
Action-mediated sensory states 915
Discriminating larger and smaller cylindrical objects 916
Navigating toward a target area of the environment 918
Integrating sensorimotor information over time and the emergence of complex internal categories 920
The self-localization problem 921
Conclusions 924
Acknowledgments 925
References 925
An Information-based Discussion of Vagueness*: Six Scenarios Leading to Vagueness 928
Abstract 929
Introduction 930
The information framework 931
Classical vs. gradual properties 932
Graduality and partial preorderings 932
Membership functions as total preorders 933
Fuzzy sets and similarity to prototypes 934
Set-theoretic operations 934
Graduality is a useful form of vagueness 935
Precisely defined vs. poorly defined properties 936
Classification ambiguity 937
Vagueness as limited perception 937
Supervaluations 938
Ill-known partial membership 938
Refining precisely defined properties using closeness relations 938
Single agent vs. multiple agents 939
Ill-known attribute values and twofold sets 941
Approximately described sets 942
Concluding remarks 943
References 944
Part 9 – Data Mining for Categories and Ontologies 948
A Smooth Introduction to Symbolic Methods for Knowledge Discovery 950
Abstract 951
Introduction 952
Methods for KDD 953
An introductory example 953
Data mining methods 954
Lattice-based classification 955
Frequent itemset search and association rule extraction 957
Frequent itemset search 958
Association rule extraction 960
Applications 960
Mining chemical reaction database 961
The chemical context 961
Mining of a reaction database 962
Discussion 963
An experiment in biology 964
An introduction to Web mining 965
Discussion 967
Conclusion 967
References 968
Genre-Specific Text Mining and Extensional Inductive Concept Recognition: A Pseudocognitive Approach 972
Abstract 973
Introduction and definition of text mining (TM) 974
Text mining 974
Our approach 975
Text retrieval 976
Standardization 977
Grammatical tagging 977
Why expert rules at the tagging stage? 978
The accuracy rates are correct, but this is still not “good enough” 978
Accuracy on what? 978
Recursion 978
A tagging language 979
Our approach to grammatical tagging 980
Automatic learning of new tagging rules 980
Terminology 981
Concept recognition in texts 981
Polysemy 982
General versus local collocations 982
Terms and collocations 982
ACT as a friendly interface helping the expert 983
ACT as an inductive program 984
Validation 988
Validation of the concepts 988
Validation of the induction 988
Conclusion 988
Acknowledgments 989
References 990
Classification and Categorization in Computer-Assisted Reading and Text Analysis 992
Abstract 993
Introduction 994
CARAT: General presentation 994
Difficulties with the technology 995
The nature of reading and analyzing a text 996
Definitions of classification and categorization for CARAT 998
Text classification and categorization 999
Text classification 999
Text categorization 999
Computer text classification and categorization 1000
Methodology for text classifying and categorizing 1000
Steps 1, 2, and 3: From a text to a matrix 1001
Step 1: Identification of units of information and domains of information 1001
Step 2: Cleaning and filtering 1002
Step 3: The matrix 1003
Steps 4 and 5 1004
The classification process 1004
The categorization process 1005
Step 6: Navigation 1005
Step 7: Evaluation 1005
Applications in CARAT 1006
Thematic analysis 1006
Categorical exploration of philosophical texts 1007
Content analysis 1009
The computer design: SATIM 1011
The workshop 1011
The laboratory 1012
Applications 1012
Conclusion 1013
References 1013
Graph Matching, System Design and Knowledge Modeling 1016
Abstract 1016
Introduction 1017
Knowledge represented as graph structures 1019
Learning heuristic knowledge 1021
Viability conditions 1022
The complexity of learning 1023
Categorization of knowledge in layers 1025
Conclusion 1026
References 1026
Part 10 – The Naturalization of Categories 1028
Nominalism and the Theory of Concepts 1030
Abstract 1030
Nominalism 1031
Ockham’s cleaver 1032
Motivations 1036
Nominalistic constraints for the theory of concepts 1038
Represented things as singular 1039
Representations as singular 1041
References 1043
Why do we think Racially? 1046
Abstract 1047
Introduction 1048
Is racialism a mere social construct? 1049
Racial skepticism 1049
Races are interactive kinds 1050
Races are transient kinds 1050
Merits and problems 1052
Is racialism a by-product of a human kind module? 1053
The nature of racialism 1053
The human kind module 1054
Empirical evidence 1054
Merits and problems 1056
Are races mere coalitions? 1058
Races and coalitions 1058
Empirical evidence 1058
Merits and problems 1059
Is racialism a by-product of an evolved ethnic cognitive system? 1061
“Ethnies” are not mere coalitions 1061
An adaptive scenario: Ethnic cognition and the exaptation of human folk biology 1062
Empirical evidence 1063
Merits and problems 1064
Conclusion 1066
References 1068
Neurosemantics and Categories 1072
Abstract 1073
Introduction 1074
Why “neuro”? 1074
The explanandum 1076
Mental representations as neural codes 1077
Representations 1077
Transformation 1078
A representational hierarchy 1079
The meaning of neural representations: Neurosemantics 1080
The representation relation 1080
A neurosemantic theory 1081
Systems 1081
Vehicles 1082
Referents 1083
Content 1084
Discussion 1086
Misrepresentation 1087
Conclusion 1089
References 1089
Conceptual Analysis and Philosophical Naturalism 1092
Abstract 1092
Introduction 1093
What is intuitive about conceptual analysis? 1094
Cognitive privileges, metaphysical privileges, and the Transparency Thesis 1095
Against privileges 1096
The inward approach 1098
Conceptual truths or truths about concepts? 1099
The outward approach 1101
‘Bachelors are unmarried men’ is about facts 1102
Explaining away the illusion 1104
A “mixed bag” 1105
Conclusion 1107
References 1107
Crisis! What Crisis? 1110
Index 1118