Creative Model Construction in Scientists and Students: The Role of Imagery, Analogy, and Mental Simulation

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Springer, Jun 10, 2008 - Science - 602 pages
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How do scientists use analogies and other processes to break away from old theories and generate new ones? This book documents such methods through the analysis of video tapes of scientifically trained experts thinking aloud while working on unfamiliar problems. Some aspects of creative scientific thinking are difficult to explain, such as the power of analogies, the use of physical intuition, and the enigmatic ability to learn from thought experiments. The book examines the hypothesis that these processes are based on imagistic mental simulation as an underlying mechanism. This allows the analysis of insight ("Aha!") episodes of creative theory formation. Advanced processes examined include specialized conserving transformations, Gedanken experiments, and adjusted levels of divergence in thinking. Student interviews are used to show that students have natural abilities for many of these basic reasoning and model construction processes and that this has important implications for expanding instructional theories of conceptual change and inquiry. "I regard this work as the most comprehensive account ever attempted to show how imagistic, analogic, and sensory-motor representations participate in creative thinking." Professor Ryan Tweney

 

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Contents

Thought Experiments and Imagistic Simulation in Plausible Reasoning
277
1512 Nersessian
278
1513 Focus of This Chapter
279
1514 What are Some Major Functions of and Benefits from Untested Thought Experiments?
280
1516 Secondary Functions
281
1518 Summary
285
How Can an Untested Thought Experiment Generate Findings with Conviction?
286
PerceptualMotor Schemas
287

Contact Between Data and Theory
12
15 General Theoretical Framework
14
16 Section Summaries and Approaches to Reading This Book
15
Major Processes Involved in Spontaneous Analogical Reasoning
21
212 Definitions of Analogy
22
213 Theories of Analogical Reasoning
23
214 Preview of Alternative Processes for Analogical Reasoning Identified in This Book
24
22 Method of Study
26
23 Initial Observations
27
232 Observations from Transcripts
28
233 Evaluating the Analogy Relation
29
241 Analogies from a Second Subject
30
242 Analysis of Major Events in S3s Transcript
31
25 Conclusion
32
Methods Experts Use to Generate Analogies
33
32 Definitions of Basic Concepts and Observations
34
322 Observed Spontaneous Analogies
36
323 Analogy Generation Methods
37
324 Frequency of Different Analogy Generation Methods
40
325 Summary of Observations with Respect to Analogy Generation
42
332 Generation Methods and Invention
44
333 Summary
45
Methods Experts Use to Evaluate an Analogy Relation
47
42 Examples from Case Studies
48
422 Bridging Analogies
49
423 A Pulley as an Analogy for the Wheel
50
43 Analogy Evaluation in the Doughnut Problem
52
44 Discussion of Findings and Connections to History of Science
53
442 Analogies and Bridges in the History of Science
54
443 Beyond Bridging
55
45 Summary
56
Expert Methods for Developing an Understanding of the Analogous Case and Applying Findings
57
512 Indirect Methods
58
Developing Understanding of the Source Analogue
60
52 Inference Projection
61
522 Data on Inference Projection
62
53 Section I Summary for Creative Analogy Generation
63
Expert Model Construction and Scientific Insight
66
Case Study of Model Construction and Criticism in Expert Reasoning
67
62 Background Questions from Philosophy of Science
68
Empiricism vs Rationalism
70
63 How are Theoretical Hypotheses Formed in the Individual Scientist?
72
Creative Intuition
73
A Thorny Issue
74
64 Protocol Evidence on Construction Cycles That Use Analogies
76
643 Analysis of Insight Episode
81
65 Summary of Evidence for a Model Construction Cycle as a Noninductive Source for Hypotheses
84
652 Explanatory vs Nonexplanatory Expedient Models
88
66 Major Nonformal Reasoning Patterns in the Preceding Chapters
95
Creativity and Scientific Insight in the Case Study for S2
97
712 Is There a Sudden Reorganizing Change in S2s Understanding?
98
713 Does the Subject Use Extraordinary Reasoning Processes?
100
714 Defining Insight
102
715 Summary
104
721 Anomalies and Persistence in Protocols and Paradigms
105
722 Transformations Invention and Memory Provocation
108
Constrained Successive Refinement vs Blind Variation
110
73 Darwins Theory of Natural Selection
112
74 Initial List of Features of Creative Thinking from This Case Study and Remaining Challenges
113
742 Limitations of the Case Study
115
Creative Nonformal Reasoning in Students and Implications for Instruction
118
Spontaneous Analogies Generated by Students Solving Science Problems
119
81 Use of Analogies by Students
120
82 Conclusion
123
Examples of Problems and Spontaneous Analogies
124
834 Rocket Problem
125
Case Study of a Student Who Counters and Improves His Own Misconception by Generating a Chain of Analogies
127
911 Protocol for 20
129
912 Protocol Summary
130
Creative Case Generation
132
914 Developing Hypotheses about Cognitive Events that can Account for the Observations
133
ExpertNovice Similarities
136
921 Instructional Implications
137
Using Analogies and Models in Instruction to Deal with Students Preconceptions
139
102 Teaching Strategy
140
1022 Anchoring Case
141
1031 Tutoring Session
142
1032 Discussion of First Case Study
144
1033 A Second Case Study
145
1034 Explanatory Models
148
1035 Abstract Transfer vs Explanatory Model Construction
149
1036 Summary of Cases
150
105 Conclusion
153
1054 Plausible Reasoning vs Logical Proof Processes in Learning
154
Transformations Imagery and Simulation in Experts and Students
157
Analogy Extreme Cases and Spatial Transformations in Mathematical Problem Solving by Experts
161
113 Results on the Use of Analogies for Eight Subjects
163
1132 Evaluating the Cylinder Conjecture
164
114 Other Creative Nonformal Reasoning Processes
165
1143 Reassembly of a Partition
167
1144 Embedding
168
1151 Imagistic Reasoning
169
116 Conclusion
170
Depictive Gestures and Other Case Study Evidence for Use of Imagery by Experts and Students
171
1212 Imagery Questions and Hypotheses
172
1213 Previous Research on Hand Motions
173
1214 Limitations of Previous Research
175
1222 Relations Between Observations and Hypotheses
177
123 Case Studies
181
1232 Analysis of S15s Protocol
182
1233 Evidence Supporting the Use of Imagery in the Solution
183
1234 Argument Structure
184
1235 A Student Protocol
189
1236 Analysis of S20s Protocol
190
1237 Summary of S20 Analysis
192
1242 Can Depictive Hand Motions be a Direct Product of Imagery?
193
1243 Summary of Relations Between Observations and Hypotheses
194
1251 The Existence of Kinesthetic Imagery
195
1254 Gestures Can Reflect Imagery
196
126 Conclusion
197
1262 Limitations
198
127 Appendix 1 Detailed Justification for Using Evidence of Imagery from Hand Motions in S15s Protocol
199
1272 Motions Can Be a Direct Product of Solution Process
201
Appendix 2
202
Physical Intuition Imagistic Simulation and Implicit Knowledge
205
1311 Abstract vs Concrete Thinking in Experts
206
1321 Intuition Reports
207
1322 Defining Features and Observable Behaviors Associated with Intuition
208
1323 Physical Intuitions
209
1332 Schemadriven Imagistic Simulation Processes
210
1333 Precedents in the Literature on PerceptualMotor Schemas
215
1334 Relations Between Observations and Hypotheses
218
1335 Importance of Concrete Intuitions and Imagistic Simulation
219
134 Implicit Knowledge
221
1341 Distinguishing Different Levels of Implicit Knowledge
222
1342 Evidence for Unconscious Knowledge
225
135 Knowledge Can Be Dynamic
226
1352 Knowledge Experienced in Imagistic Simulations Is Not Static
227
The Role of Concrete Physical Intuitions and Simulations in Embodied Thinking by Experts
229
1363 Intuitions and Imagistic Simulation
230
1365 Using PerceptualMotor Schemas as an Initial Foothold for Understanding the Use of Intuitions and Imagistic Simulation
232
1366 Imagery Intuitions and Anchoring
233
Advanced Uses of Imagery in Analogies Thought Experiments and Model Construction
236
The Use of Analogies Imagery and Thought Experiments in Both Qualitative and Mathematical Model Construction
237
1411 Stages in Model Construction Leading up to Quantitative Modeling During the Solution
238
1412 Issues in the Field
240
1413 Ways to Read this Chapter
241
1422 II Searching for and Evaluating Initial Qualitative Explanatory Model Elements
244
1423 III Seeking a More Fully Imageable and Causally
255
of the Spatial and Physical Relationships Projected from the Model into the Target Until They Are Ready to Support Quantitative Predictions
258
1425 V Developing a Quantitative Model on the Foundation of the New Qualitative and Geometric Models
260
1431 Some Possible Precision Levels for Relationship R Between X and Y
265
1432 Transforms to of Solution
269
1433 Summary
270
1442 Source Analogues
271
1443 Triangular Not Dual Relation in Model Construction
272
1444 Source Analogues are Projected into the Composite Model and Must Be Imagistically Aligned
273
145 Conclusion
274
1452 Parallels and Differences Between Qualitative and Mathematical Modeling
275
Spatial Reasoning Symmetry and Compound Simulation
290
1524 Summary
293
153 Imagery Enhancement Phenomena Support the Proposed Answer to the Paradox
294
1532 Imagery Enhancement Focused on Enhancing the Application of a Schema in a Simulation
295
1533 Analysis of Transcripts
297
1534 Sources of Conviction in Imagery Enhancement
298
1535 Implications of These Extreme Case Examples for a Theory of Thought Experiments
300
1536 Imagery Enhancement Focused on Enhancing Spatial Reasoning
301
1537 Enhancing Spatial Reasoning Via Image Size and Orientation
302
1538 Symmetry Enhancement
303
1539 Compound or Linearity Enhancement
304
154 How Are Imagistic Simulation and Thought Experiments Used Within More Complex Reasoning Modes?
305
1542 Evaluative Gedanken Experiments as the Most Impressive Kind of Thought Experiment
312
1543 Multiple Types of Reasoning Processes that can Utilize Thought Experiments Run Via Imagistic Simulations
315
155 Are Imagistic Simulations Operating in the Mathematical Part of the Solution?
316
156 How Thought Experiments Contribute to Model Evaluation
317
1562 Summary
319
157 Chapter Summary
321
Sources of Conviction
322
A Punctuated Evolution Model of Investigation and Model Construction Processes
325
1612 Construction Occurred via Generative Abduction Rather than Induction or Deduction
327
Basic Model
330
162 Qualitative Investigation Processes
332
1622 GEM Cycles
338
1624 The Three Cycles in the Outlined Investigation Process Can Generate the Five Major Observed Modes of Investigation in the Protocol
342
1625 Separate Explanation and Description Processes
347
1626 Computational Model of Todd Griffith
348
1627 Evaluation Functions can Guide Control
350
1628 Comparison to Griffith Study
352
Unpredictable Spontaneous Accessing of Subprocesses
354
16210 Generality
355
16212 Limitations of the Model Presented
357
163 Mathematical Modeling Processes
359
1632 Untested Thought Experiments at Higher Levels of Precision than Qualitative Modeling
361
1633 Mathematics and Explanation
362
How Evaluation Processes Complement Generative Abduction
363
1641 Multiple Sources of Ideas and Constraints for the Generative Abduction Process
364
1643 Role of Transformations in Model Modification
367
1644 Distinctions Between Constructive Transformations Running a Schema in an Imagistic Simulation and Basic Spatial Reasoning Operators
368
1645 Coherence and Competition Between Models
369
165 Seeking an Optimal Level of Divergence
370
1652 Need for an Effective Middle Road with Respect to Creative Divergence
371
The Need for Optimal Divergence
372
1656 Mechanisms for Modulating Divergence
374
1657 Summary for Section on Divergence
376
166 Chapter Summary
377
1662 Multiple Cycles and Goals in the Overall Investigation Process
378
Generative Abduction Model Evaluation Schema Alignment and Mathematization
380
Imagistic Processes in Analogical Reasoning Transformations and Dual Simulations
383
1712 Wertheimers Parallelogram
384
172 Conserving Transformations
385
1722 Are Conserving Transformations Just Memorized Rules?
386
1732 Spring Problem
387
1733 Newtons Canon
389
1741 Do Dual Simulations Differ from Transformations?
391
1742 Dual Simulation for the Square and Circular Coils
392
Overlay Simulations and Model Projections May Involve Similar Processes
395
1753 Model Projection
396
1755 Dual Simulation vs Compound Simulation in Modeling
397
Contrasting Mechanisms for Determining Similarity
398
1761 Mechanisms for Dual Simulation Including Overlay Simulation
399
1762 Mechanisms for Conserving Transformations
400
1763 Bridging is a Higherorder Strategy Compared to Others
401
1765 Comparison to Structural Mapping of Images
404
Four Main Analogy Evaluation Methods Not One
405
178 Conclusion
407
How Grounding in Runnable Schemas Contributes to Producing Flexible Scientific Models in Experts and Students
409
1811 Review of Findings on Imagistic Simulation and Runnable Schemas
410
1813 Models Can Inherit the Capacity for Simulation from Anchors
412
1814 What Exactly is Transferred?
415
1815 Example of Transfer of Imagery and Runnability in Instruction
416
182 Cognitive Benefits of Anchoring and Runnability for Models
418
1821 Traditional Benefits of Building on Prior Knowledge
419
1823 Recursive Runnability of Models As Thought Experiments Explains Many of These Benefits
424
183 How Runnable Models Contribute Desirable Properties to Scientific Theories
425
1831 Scientific Theories and the Role of Runnability
426
184 Conclusion
428
1841 Initial Support for the Runnability Hypothesis
429
Conclusions
432
Summary of Findings on Plausible Reasoning and Learning in Experts I Basic Findings
433
1912 Model Construction in Students
435
1922 Literal Similarity and the Problem of What Counts as an Analogy
438
1924 Initial New Distinctions and Findings on Analogy
439
193 Model Construction Findings Part One and Initial Connections to General Issues in HistoryPhilosophy of Science
440
1932 Extraordinary Thinking?
441
1934 A Case Study of Scientific Insight
442
1935 Initial Exploration of Mechanisms of Hypothesis Generation
444
1936 Section Summary
445
194 Imagistic Simulation Findings Part One
446
1942 Mechanisms for Imagistic Simulation
447
1943 Terminology for Imagistic Simulations
448
1944 Imagery During Simulation Behavior
449
1946 Sources of New Knowledge in Imagistic Simulations
451
1947 How Perceptual Motor Schemas are Useful in Scientific Thinking
452
1948 Intuitive Anchors
453
19410 Connection to Experiments and Situated Action
454
Summary of Findings on Plausible Reasoning and Learning in Experts II Advanced Topics
457
2012 Analogies and Imagery
463
2013 Analogies and Model Construction
468
Thought Experiments and Their Uses in Plausible Reasoning
471
2023 Broader and Narrower Categories of Thought Experiments
473
2024 Can Thought Experiments Allow One to Get the Physics for Free?
474
2025 Section Conclusion
475
An Evolutionary Model of Investigation Processes
476
Description Cycle
478
Mathematical Modeling
484
204 The Important Role of Imagery in the Expert Investigations
485
2042 Evidence for Imagery Involvement in a Wide Range of Reasoning Processes
486
2043 Evidence for the Importance to Subjects of Imagistic Simulation
488
2044 Possible Advantages of Imagistic Representations as Knowledge Structures
489
2045 Possible Advantages of Imagistic Representations for Creative Reasoning
492
205 Transfer of Runnability Leads to Outcomes of Flexible Model Application and Generativity
499
2052 Role ofRunnable Intuitions in Conceptual Understanding and Recursive Runnability
501
2053 Comparison to Lakoffand Nunezs Embodied Mathematics
503
2054 Payoffs from Transfer of Runnability
504
2062 Links Between Data and Theory
505
Creativity in Experts Nonformal Reasoning and Educational Applications
507
2112 Central Role of Imagery
510
2114 Larger Integrating Processes
517
2115 Position on Concrete vs Abstract Thinking
518
212 How Experts Used Creativity Effectively
521
2122 How a Coalition of Weak Nonformal Methods are Able to Overcome the Dilemma of Fostering Both Creativity and Validity
523
2123 Overlap Between the Context of Discovery and Context of Evaluation
530
2124 Section Conclusion
531
Needed Additions to the Classical Theory of Conceptual Change in Education
532
2133 Need for an Expanded Theory of Conceptual Change for Education
533
2141 Similarities Concerning Resistance to Change
534
2142 Similarities in the Use of Intuition and Imagery
536
2143 Use of Analogies by Students
537
2144 Model Construction by Students
538
ExpertNovice Comparisons
540
2151 Strategies Suggested by Initial Studies of Analogy and Model Construction in Part One of the Book
541
2152 Strategies and Implications Suggested by Findings on Imagistic Knowledge Representations in Part Two of the Book
548
2153 Educational Implications of Imagistic Learning Processes in Part Two of the Book
552
2154 Conclusion Educational Applications
556
216 Are Creative Processes in Experts a Natural Extension of Everyday Thinking?
559
2162 ExpertNovice Differences in Reasoning
560
2165 A Spectrum from Ordinary Thinking to Unusually Effective Creative Thinking to Extraordinary Thinking
562
How Creative Expert Reasoning is not Ordinary
566
Utilizing Natural Reasoning Processes
567
217 Assessing the Potential for a Model of Creative Theory Construction in Science
568
2172 Can Creative Behavior be Explained?
569
218 Conclusion
572
2183 Questions About Scientific Thinking
574
References
575
Name Index
591
Subject Index
595
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