Tuning in to non-adjacencies
Exposure to learnable patterns supports discovering otherwise difficult structures
Bibliographic Data
| ID | 21307100 |
|---|---|
| Authors | Martin Zettersten (0000-0002-0444-7059, University of Wisconsin–Madison, corresponding author), Christine E Potter (0000-0001-9893-5389, Princeton University), Jenny Saffran (0000-0003-3749-8773, University of Wisconsin–Madison), Jenny R Saffran |
| Year | 2020 |
| Volume | 202 |
| Pages | 104283 |
| Publication date | 2020-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cognition (JOURNAL) |
| Journal identifiers | ISSN: 0010-0277 • E-ISSN: 1873-7838 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.cognition.2020.104283 |
| PMID | 32623134 |
| OpenAlex | W3039923779 |
| Language | EN |
| Citations received | 2 |
| References cited | 51 |
Context (archaeology) · Natural language processing · Set (abstract data type) · Artificial Intelligence · Computer Science · Language and cultural evolution · Natural Language Processing Techniques · Psychology · Topic Modeling
The Novel Object and Unusual Name (Noun) Database
Variability and Detection of Invariant Structure
Signal-Driven Computations in Speech Processing
Learning at a distance I. Statistical learning of non-adjacent dependencies
Random effects structure for confirmatory hypothesis testing
Fitting Linear Mixed-Effects Models Using lme4
Categorical data analysis
Mixed-effects modeling with crossed random effects for subjects and items
Finding Structure in Time
The Developmental Trajectory of Nonadjacent Dependency Learning
Acquisition of Nonadjacent Phonological Dependencies in the Native Language During the First Year of Life
Transfer effects in learning a second language grammatical gender system
Gleaning Structure from Sound
Infants’ selective use of reliable cues in multidimensional language input
Grammatical gender in L2
The extraction and integration framework
Concurrent Statistical Learning of Adjacent and Nonadjacent Dependencies
Statistical Learning and Language
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |