Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
| Associative Learning | 2 |
| Error Patterns | 2 |
| Accuracy | 1 |
| Artificial Languages | 1 |
| Computation | 1 |
| Cues | 1 |
| Eye Movements | 1 |
| Language Patterns | 1 |
| Language Research | 1 |
| Learning Processes | 1 |
| Models | 1 |
| More ▼ | |
Source
| Cognitive Science | 2 |
Publication Type
| Journal Articles | 2 |
| Reports - Research | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
Erdin Mujezinovic; Vsevolod Kapatsinski; Ruben van de Vijver – Cognitive Science, 2024
A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney,…
Descriptors: Morphology (Languages), Phonology, Morphemes, Cues

Peer reviewed
Direct link
