Generalization of Syntactic Knowledge in Semiartificial Language Learning
Bibliographic Data
| ID | 4503254 |
|---|---|
| Authors | Yan Tao (0000-0001-5228-2853, Beijing Foreign Studies University, corresponding author), John N Williams (0000-0002-5473-5382, University of Cambridge) |
| Year | 2018 |
| Volume | 68 |
| Issue | 4 |
| Pages | 1001-1031 |
| Publication date | 2018-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Language Learning (JOURNAL) |
| Journal identifiers | ISSN: 0023-8333 • E-ISSN: 1467-9922 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/lang.12299 |
| OpenAlex | W2810305129 |
| Language | EN |
| Citations received | 4 |
| References cited | 41 |
A hallmark of grammatical knowledge is the ability to parse novel syntactic structures. Previous artificial language studies have examined learning hierarchical structures, but few have involved meaningful language and shown generalization to novel structures. This study addressed this issue using the semiartificial language paradigm. The experimental group of monolingual English speakers was exposed to singly embedded sentences combining Chinese syntax with English lexis. A novel testing procedure was used where participants made plausibility judgments on grammatical sentences featuring two-level embedding. Only correct application of the rules underlying the training sentences permitted correct discrimination of plausible and implausible sentences. The experimental group performed above chance, an untrained control group performed at chance, and an untrained and unaware Chinese control group showed above-chance performance. Confidence judgments and rule recognition analysis suggested that learning was implicit. We concluded that the experimental group demonstrated structural generalization and might have acquired the syntactic rule system
Control (management) · Generalization · Language acquisition · Lexis · Linguistics · Natural language processing · Parsing · Syntax · Artificial Intelligence · Computer Science · Natural Language Processing Techniques · Neurobiology of Language and Bilingualism · Psychology · Second Language Acquisition and Learning
Learning Condition, Linguistic Complexity, and First Language Transfer in Semiartificial Language Learning
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The effects of incidental learning and input frequency on the perception of non-native speech
Factors influencing L2 learners’ use of the English dative construction
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The item-based nature of children’s early syntactic development
Implicit learning and tacit knowledge.
Measuring Implicit and Explicit Linguistic Knowledge
Measuring Implicit and Explicit Knowledge of a Second Language
Implicit learning of artificial grammars
Measuring unconscious knowledge
Three models for the description of language
Fitting Linear Mixed-Effects Models Using lme4
LmerTest Package
Comparing a nativist and emergentist approach to the initial stage of SLA
| Unique citing works | 4 |
|---|---|
| Citations per year | 0.8 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 4 |