This book examines the role of experience-based learning on children’s acquisition of language and concepts. It reviews, compares, and contrasts accounts of how the opportunity to recognize and generalize patterns influences learning. The book offers the first systematic integration of three highly influential research traditions in the domains of language and concept acquisition: Statistical Learning, Structural Alignment, and the Bayesian learning perspective. Chapters examine the parameters that constrain learning, address conditions that optimize learning, and offer explanations for cases in which implicit exemplar-based learning fails to occur. By exploring both the benefits and challenges children face as they learn from multiple examples, the book offers insight on how to better able to understand children’s early unsupervised learning about language and concepts.
Topics featured in this book include:
Competing models of statistical learning and how learning might be constrained by infants’ developing cognitive abilities. How experience with multiple exemplars helps infants understand space and other relations. The emergence of category-based inductive reasoning during infancy and early childhood. How children learn individual verbs and the verb system over time. How statistical learning leads to aggregation and abstraction in word learning. Mechanisms for evaluating others’ reliability as sources of knowledge when learning new words. The Search for Invariance (SI) hypothesis and its role in facilitating causal learning.
Language and Concept Acquisition from Infancy Through Childhood is an essential resource for researchers, clinicians and related professionals, and graduate students in infancy and early child development, applied linguistics, language education, child, school, and developmental psychology and related mental health and education services.

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zlib/no-category/Childers, Jane B./Language and Concept Acquisition from Infancy Through Childhood (Learning from Multiple Exemplars) ||_111224943.pdf

Childers, Jane B.

Springer Nature Switzerland AG

Springer Nature, Cham, Switzerland, 2020

1st edition 2020, Cham, 2020

First edition, Cham, 2020

Switzerland, Switzerland

1, 2020-03-28

Feb 04, 2020

1, 20200203

SoftArchive

sm80913854

producers:
Adobe PDF Library 10.0.1

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Source title: Language and Concept Acquisition from Infancy Through Childhood: Learning from Multiple Exemplars

Preface 6
Acknowledgments 7
Contents 8
Contributors 10
About the Editor 12
Chapter 1: Introduction 13
References 21
Chapter 2: Mechanisms of Statistical Learning in Infancy 22
Statistical Learning in Infancy 23
Kinds of Statistical Structure Infants Are Able to Learn 26
Testing Methods 30
Implications of Infant SL for Cognitive Development and Developmental Disabilities 31
Mechanisms Underlying Statistical Learning in Infancy 33
Conclusions and Broader Implications 35
References 36
Chapter 3: How Multiple Exemplars Matter for Infant Spatial Categorization 42
Why Spatial Relations? 44
Do Infants Require Multiple Exemplars for Forming Spatial Categories? 46
Procedures for Testing Infants’ Categorization of Spatial Relations 49
Does Infant Spatial Categorization Benefit from Multiple Examples? 53
What Mechanisms Are Central to Infant Spatial Categorization? 58
How Could the Other Theories in This Area Impact or Contribute to Your Findings? 62
Can Infant Spatial Categorization Inform Other Types of Spatial Learning? 63
References 64
Chapter 4: How the Demands of a Variable Environment Give Rise to Statistical Learning 70
Conditional Statistical Learning and Language Acquisition 72
Distributional Statistics 75
Representing Variability Across Exemplars 77
Encoding and Generalization in Memory 79
Memory and Statistical Learning 80
References 83
Chapter 5: Structure-Mapping Processes Enable Infants’ Learning Across Domains Including Language 89
When Is High Variability Helpful and When Not? 97
Promoting Relational Learning 98
What Paradigms Are Usually Used to Test Our Theory? 99
How Could Structure-Mapping Theory Extend Beyond Contexts? 102
Conclusions 106
References 107
Chapter 6: The Emergence of Inductive Reasoning During Infancy: Learning from Single and Multiple Exemplars 115
Inductive Reasoning During Early Childhood 116
Inductive Reasoning in Infancy 118
Developmental Origins of Inductive Reasoning 123
Unfamiliar Animal Categories 125
Familiar Animal Categories 128
Learning from One Versus Many: Integrating Findings Across Studies 130
Conclusions 134
References 135
Chapter 7: Learning Individual Verbs and the Verb System: When Are Multiple Examples Helpful? 141
Children’s Challenge in Acquiring the Lexical System of Verbs 142
Bootstrapping from Perceptual to Relational Similarity in Extracting the Core of Verb Meanings 144
Use of Multimodal Similarity (Iconicity) 145
Use of Object Similarity 146
Summary and Implications 149
An Additional Mechanism for Verb Learning: Contrast 149
Verb Meaning Acquisition Within the Constraints of the Lexical System 152
Complexity of the Semantic Structures in Lexical Domains in the Real World 152
What Do Children Need to Discover to Acquire Verbs in a Complex-Structured Lexical Domain? 153
Findings from the “Carry” Verb Acquisition Study 154
How Many Verb Types Did Children Know? 154
Does Children’s Representation of the Lexical Domain Stay the Same Between 3 and 7? 155
Reliance of Object Similarity to Structure the Semantic Domain 156
Factors Determining the Ease of Learning 161
Summary and Implications: Object Saliency, Similarity, and Contrast as Driving Forces for Structuring the Verb Lexicon 164
Conclusion 164
References 165
Chapter 8: Multiple Examples Support Children’s Word Learning: The Roles of Aggregation, Decontextualization, and Memory Dynamics 169
Statistical Learning 170
Multiple Examples Provide Support for Aggregation 172
Multiple Examples Provide Support for Abstraction and Decontextualization 176
Multiple Examples Provide Support for Retention and Memory 179
Conclusion 183
References 184
Chapter 9: Mechanisms for Evaluating Others’ Reliability When Learning Novel Words 189
Associative Origins of Selective Learning 191
Do More Sophisticated Mechanisms Underlie Children’s Social Learning: A Gaze Following Example 193
Questions Can Answer Questions About Mechanisms of Selective Word Learning 199
Concluding Thoughts: Beyond Selective Word Learning 203
References 204
Chapter 10: The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning 207
Positive Testing Strategy 209
PTS in Scientific Reasoning 209
PTS in Rule Learning 211
Theories of PTS 211
PTS as a Means of Generating Outcomes 212
PTS as a Means of Generating Evidence 213
Other Accounts and Overlapping Evidence 215
The Current Theory: Positive Testing and Causal Learning 216
Causal Invariance and Interventionism 217
The Search for Invariance (SI) Hypothesis 219
As an Alternative to “Engineering Desirable Outcomes” 220
As an Alternative to “Seeking Confirmatory Evidence” 221
Beyond Evidence of Sufficiency 221
As an Account of Previously Ambiguous Evidence 223
Conclusion: Relationship to Truth 224
References 225
Chapter 11: Multiple Exemplars of Relations 230
Exemplars of Relations—Unique Challenges 231
Why Is It Difficult to Perceive Multiple Exemplars of Relations? 231
Not Knowing the Relations 231
The Relations Are Known, but the Relational Similarity Is Not Salient 232
The Solution: Structural Alignment 233
Comparing What? Similarity of Exemplars 236
How Many Exemplars to Compare? 237
How Does Comparison Happen? 239
Object Similarity Invites Comparison 239
Progressive Alignment 241
Language Invites Comparison 242
Social Relational Learning 244
The Problem 244
Problem 1: Lack of Domain Knowledge 245
Problem 2: Relational Versus Object Matches 246
The Solution: How Does Structure Mapping Work in the Social Domain? 247
Language for Learning Relational Concepts 248
Does Object Similarity Help? 248
Social Comparison and Object Versus Relational Similarity 249
Alignable Differences 250
Summary 251
References 252
Chapter 12: Epilogue: Comparing Comparison Theories: What Can We Gain? 255
References 260
Index 261

"This book examines the role of experience-based learning on children's acquisition of language and concepts. It reviews, compares, and contrasts accounts of how the opportunity to recognize and generalize patterns influences learning. The book offers the first systematic integration of three highly influential research traditions in the domains of language and concept acquisition: Statistical Learning, Structural Alignment, and the Bayesian learning perspective. Chapters examine the parameters that constrain learning, address conditions that optimize learning, and offer explanations for cases in which implicit exemplar-based learning fails to occur. By exploring both the benefits and challenges children face as they learn from multiple examples, the book offers insight on how to better able to understand children's early unsupervised learning about language and concepts. Topics featured in this book include: Competing models of statistical learning and how learning might be constrained by infants' developing cognitive abilities. How experience with multiple exemplars helps infants understand space and other relations. The emergence of category-based inductive reasoning during infancy and early childhood. How children learn individual verbs and the verb system over time. How statistical learning leads to aggregation and abstraction in word learning. Mechanisms for evaluating others' reliability as sources of knowledge when learning new words. The Search for Invariance (SI) hypothesis and its role in facilitating causal learning. Language and Concept Acquisition from Infancy Through Childhood is an essential resource for researchers, clinicians and related professionals, and graduate students in infancy and early child development, applied linguistics, language education, child, school, and developmental psychology and related mental health and education services."-- Provided by publisher

"This book examines the role of experience-based learning on children's acquisition of language and concepts. It reviews, compares, and contrasts accounts of how the opportunity to recognize and generalize patterns influences learning. The book offers the first systematic integration of three highly influential research traditions in the domains of language and concept acquisition: Statistical Learning, Structural Alignment, and the Bayesian learning perspective. Chapters examine the parameters that constrain learning, address conditions that optimize learning, and offer explanations for cases in which implicit exemplar-based learning fails to occur. By exploring both the benefits and challenges children face as they learn from multiple examples, the book offers insight on how to better able to understand children's early unsupervised learning about language and concepts. Topics featured in this book include: Competing models of statistical learning and how learning might be constrained by infants' developing cognitive abilities. How experience with multiple exemplars helps infants understand space and other relations. The emergence of category-based inductive reasoning during infancy and early childhood. How children learn individual verbs and the verb system over time. How statistical learning leads to aggregation and abstraction in word learning. Mechanisms for evaluating others' reliability as sources of knowledge when learning new words. The Search for Invariance (SI) hypothesis and its role in facilitating causal learning. Language and Concept Acquisition from Infancy Through Childhood is an essential resource for researchers, clinicians and related professionals, and graduate students in infancy and early child development, applied linguistics, language education, child, school, and developmental psychology and related mental health and education services."-- Publisher's website

"This book examines the role of experience-based learning on children's acquisition of language and concepts. It reviews, compares, and contrasts accounts of how the opportunity to recognize and generalize patterns influences learning. The book offers the first systematic integration of three highly influential research traditions in the domains of language and concept acquisition: Statistical Learning, Structural Alignment, and the Bayesian learning perspective. Chapters examine the parameters that constrain learning, address conditions that optimize learning, and offer explanations for cases in which implicit exemplar-based learning fails to occur. By exploring both the benefits and challenges children face as they learn from multiple examples, the book offers insight on how to better able to understand children's early unsupervised learning about language and concepts. Topics featured in this book include: Competing models of statistical learning and how learning might be constrained by infants' developing cognitive abilities. How experience with multiple exemplars helps infants understand space and other relations. The emergence of category-based inductive reasoning during infancy and early childhood. How children learn individual verbs and the verb system over time. How statistical learning leads to aggregation and abstraction in word learning. Mechanisms for evaluating others' reliability as sources of knowledge when learning new words. The Search for Invariance (SI) hypothesis and its role in facilitating causal learning. Language and Concept Acquisition from Infancy Through Childhood is an essential resource for researchers, clinicians and related professionals, and graduate students in infancy and early child development, applied linguistics, language education, child, school, and developmental psychology and related mental health and education services." -- prové de l'editor

2020-02-04