Implicit learning of non-adjacent dependencies
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Luca Onnis
Abstract
Language and other higher-cognitive functions require structured sequential behavior including non-adjacent relations. A fundamental question in cognitive science is what computational machinery can support both the learning and representation of such non-adjacencies, and what properties of the input facilitate such processes. Learning experiments using miniature languages with adult and infants have demonstrated the impact of high variability (Gómez, 2003) as well as nil variability (Onnis, Christiansen, Chater, & Gómez (2003; submitted) of intermediate elements on the learning of nonadjacent dependencies. Intriguingly, current associative measures cannot explain this U shape curve. In this chapter, extensive computer simulations using five different connectionist architectures reveal that Simple Recurrent Networks (SRN) best capture the behavioral data, by superimposing local and distant information over their internal ‘mental’ states. These results provide the first mechanistic account of implicit associative learning of non-adjacent dependencies modulated by distributional properties of the input. We conclude that implicit statistical learning might be more powerful than previously anticipated.
Abstract
Language and other higher-cognitive functions require structured sequential behavior including non-adjacent relations. A fundamental question in cognitive science is what computational machinery can support both the learning and representation of such non-adjacencies, and what properties of the input facilitate such processes. Learning experiments using miniature languages with adult and infants have demonstrated the impact of high variability (Gómez, 2003) as well as nil variability (Onnis, Christiansen, Chater, & Gómez (2003; submitted) of intermediate elements on the learning of nonadjacent dependencies. Intriguingly, current associative measures cannot explain this U shape curve. In this chapter, extensive computer simulations using five different connectionist architectures reveal that Simple Recurrent Networks (SRN) best capture the behavioral data, by superimposing local and distant information over their internal ‘mental’ states. These results provide the first mechanistic account of implicit associative learning of non-adjacent dependencies modulated by distributional properties of the input. We conclude that implicit statistical learning might be more powerful than previously anticipated.
Chapters in this book
- Prelim pages i
- Table of contents v
- Foreword vii
- List of contributors ix
-
Introduction
- Introduction xiii
-
Theoretical perspectives
- Implicit AND explicit language learning 3
- Explaining phenomena of first and second language acquisition with the constructs of implicit and explicit learning 25
- Implicit learning in SLA 47
- Semantic implicit learning 69
- What does current generative theory have to say about the explicit-implicit debate? 91
- Explicit knowledge about language in L2 learning 117
- The learnability of language 139
- Tracking multiple inputs 167
- Implicit statistical learning and language acquisition 191
- Implicit learning of non-adjacent dependencies 213
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Methodology
- Artificial grammar learning 249
- Challenges in implicit learning research 275
- Effects of conditions on L2 development 301
- Investigating implicit and explicit processing using L2 learners’ eye-movement data 325
- Contributions of event-related potential research to issues in explicit and implicit second language acquisition 349
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Practical applications
- Implicit learning of a L2 morphosyntactic rule, and its relevance for language teaching 387
- Form-focused instruction and the measurement of implicit and explicit L2 knowledge 417
- Implicit and explicit instruction in L2 learning 443
- Index 483
Chapters in this book
- Prelim pages i
- Table of contents v
- Foreword vii
- List of contributors ix
-
Introduction
- Introduction xiii
-
Theoretical perspectives
- Implicit AND explicit language learning 3
- Explaining phenomena of first and second language acquisition with the constructs of implicit and explicit learning 25
- Implicit learning in SLA 47
- Semantic implicit learning 69
- What does current generative theory have to say about the explicit-implicit debate? 91
- Explicit knowledge about language in L2 learning 117
- The learnability of language 139
- Tracking multiple inputs 167
- Implicit statistical learning and language acquisition 191
- Implicit learning of non-adjacent dependencies 213
-
Methodology
- Artificial grammar learning 249
- Challenges in implicit learning research 275
- Effects of conditions on L2 development 301
- Investigating implicit and explicit processing using L2 learners’ eye-movement data 325
- Contributions of event-related potential research to issues in explicit and implicit second language acquisition 349
-
Practical applications
- Implicit learning of a L2 morphosyntactic rule, and its relevance for language teaching 387
- Form-focused instruction and the measurement of implicit and explicit L2 knowledge 417
- Implicit and explicit instruction in L2 learning 443
- Index 483
