Inductive reasoning is everyday, intuitive reasoning; it contrasts with deductive or logical reasoning. Inductive reasoning is much more prevalent than deductive reasoning, yet there has been much less research on inductive reasoning. Using contributions from the leading researchers in the field, the interdisciplinary approach of this book is relevant to those interested in psychology (including cognitive and developmental psychology), decision-making, philosophy, computer science, and education.
Author(s): Aidan Feeney, Evan Heit
Edition: 1
Year: 2007
Language: English
Pages: 374
Cover......Page 1
Half-title......Page 3
Title......Page 5
Copyright......Page 6
Contents......Page 7
List of Figures......Page 9
List of Tables......Page 11
List of Contributors......Page 13
Preface......Page 15
Who is this book for?......Page 17
Organisation of the book......Page 18
References......Page 21
1 What Is Induction and Why Study It?......Page 23
How is induction related to deductiion?......Page 24
The Problem View......Page 25
The Process View......Page 29
Approaches to studying induction: the case of the diversity principle......Page 33
The Historical Approach......Page 34
The Philosophical/Normative Approach......Page 35
The Experimental Approach......Page 37
The Developmental Approach......Page 39
The Model-Based Approach......Page 41
References......Page 44
2 The Development of Inductive Reasoning......Page 47
Perceptual Similarity......Page 48
Taxonomic Relations......Page 49
Hierarchical Relations......Page 53
Relations Involving Multiple Premises: Diversity and Monotonicity......Page 54
Property Knowledge......Page 57
Causal Relations......Page 60
Summary and Discussion......Page 62
II. Implications for theories of induction......Page 63
Feature-Based Induction Model (FBI)......Page 66
Bayesian Models......Page 67
Relevance Theory......Page 68
III. What develops in inductive reasoning?......Page 69
Early Acquisition of Inductive Potential......Page 70
Are There Developmental Changes in Inductive Processes?......Page 71
Acknowledgments......Page 72
References......Page 73
I. Introduction......Page 77
II. Asymmetries in category-based induction......Page 78
A. Typicality Effects......Page 80
B. Knowledge and Experience Effects......Page 83
C. Distinctive Categories and Features of the Target......Page 85
A. Cautionary Notes in Comparing across Studies and Populations......Page 89
B. Additional Asymmetries Help to Constrain the Alternative Theories......Page 91
C. Bringing the Evidence from CBI to Bear on the Theoretical Alternatives......Page 94
D. BringingMore Evidence to Bear: Justification Data......Page 96
E. Summary......Page 97
V. Implications......Page 98
References......Page 100
4 Property Generalization as Causal Reasoning......Page 103
Research with Background Causal Knowledge......Page 106
Research with Experimentally-Provided Causal Knowledge......Page 108
When causality and similarity compete in property generalization......Page 117
Research with Background Causal Knowledge......Page 118
Research with Experimentally Provided Causal Knowledge......Page 119
Research with Background Causal Knowledge......Page 124
Research with Experimentally Provided Causal Knowledge......Page 126
Discussion......Page 129
References......Page 133
5 Availability in Category-Based Induction......Page 136
Availability in category-based induction......Page 137
Context-Based Changes in Availability......Page 139
Availability, Experience, and Default Domain Knowledge......Page 145
Summary: Availability in Category-Based Induction......Page 150
Connections and extensions......Page 152
Availability in Action......Page 154
References......Page 156
1.1 The Property Problem......Page 159
1.2 Towards Non-Blank butManageable Predicates......Page 161
1.3 Theoretical Goals......Page 163
2.1 Qualitative Requirements: The One-Premise Case......Page 164
2.2 Qualitative Requirements: The Two-Premise Case......Page 165
2.3 Formulas for One-Premise Arguments......Page 166
2.4 Formulas for Two-Premise Arguments......Page 167
3.1 Stimuli and Procedure......Page 169
3.2 Results......Page 170
3.3 Second Test of the Model......Page 171
4. Extensions of gap2......Page 173
5. Constructing joint probability distributions using similarity......Page 174
5.1 Subjective Probability......Page 176
5.2 Overview of the Algorithm......Page 177
5.3 The Function f......Page 178
5.4 Test of the Algorithm QPf......Page 179
5.5 Results......Page 180
5.6 Application to the Design of Autonomous Intelligent Agents......Page 183
References......Page 185
1. Introduction......Page 189
2. Property induction......Page 192
3.1 Similarity-Coverage Model......Page 195
3.2 Feature-Based Models......Page 196
3.3 A Bayesian Analysis......Page 197
4.1 The Bayesian Inference Engine......Page 198
4.2 Theory-Based Priors......Page 200
5.1 Reasoning about Generic Biological Properties......Page 210
5.2 Reasoning about Causally Transmitted Properties......Page 216
6. The generality of bayesian models of induction......Page 219
7. Conclusions and open questions......Page 221
References......Page 223
8 Use of Single or Multiple Categories in Category-Based Induction......Page 227
The basic framework: how categories should be used in induction......Page 228
Evidence from Natural Categories......Page 229
Evidence from Artificial Categories......Page 230
Explanations of the use of single categories......Page 232
Cross-classification......Page 237
Recent bayesian approaches......Page 240
Conclusion......Page 245
References......Page 246
1. What is abduction?......Page 248
2. Abduction in philosophy, artificial intelligence, and psychology......Page 251
3. Neural structures......Page 253
4. Explanation and causality......Page 256
5. Inference......Page 259
6. Emotional initiation......Page 262
7. Mechanisms......Page 263
References......Page 266
10 Mathematical Induction and Induction in Mathematics......Page 270
Mathematical induction and universal generalization......Page 273
Worry Number 1: The Justification of (IAx)......Page 274
Worry Number 2: The Justification of (UG)......Page 277
Induction in mathematics......Page 280
Algebra......Page 281
Geometry......Page 282
Implications......Page 285
Summary and conclusion......Page 287
References......Page 289
11 Induction, Deduction, and Argument Strength in Human Reasoning and Argumentation......Page 291
1. Two systems of reasoning......Page 295
1.1 Deductively Incorrect and Inductively Strong Arguments......Page 296
1.2 Deductively Correct and Inductively Weak Arguments......Page 299
2. Conditional inference......Page 302
2.1 Inferential Asymmetries and Inductive Processes in "Deductive" Reasoning......Page 305
2.2 Measures of Argument Strength Applied to Conditional Inference......Page 308
3. Argument strength and informal reasoning fallacies......Page 312
3.1 The Argument from Ignorance......Page 313
4. Conclusions......Page 317
References......Page 320
12 Individual Differences, Dual Processes, and Induction......Page 324
Dual-process theories of thinking......Page 325
Individual differences in cognitive ability and thinking......Page 328
The Normative Argument for Diversity......Page 331
Dual-Process Predictions......Page 332
Some Recent Evidence......Page 335
Interpreting the Data......Page 337
Normative Issues......Page 339
The Relationship between Induction and Deduction......Page 343
References......Page 346
1. The prevalence of induction......Page 350
Normative Criteria......Page 351
On Computational Models......Page 353
Descriptive Models......Page 354
3. Causality......Page 360
4. A taxonomic proposal......Page 361
References......Page 363
Index......Page 367
