Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 0 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Experiments | 2 |
| Lexicology | 2 |
| Models | 2 |
| Word Recognition | 2 |
| Alphabets | 1 |
| Coding | 1 |
| Decision Making | 1 |
| English | 1 |
| Eye Movements | 1 |
| Identification | 1 |
| Language Processing | 1 |
| More ▼ | |
Author
| Lupker, Stephen J. | 2 |
| Campbell, Crystal R. | 1 |
| Perea, Manuel | 1 |
| Sears, Christopher R. | 1 |
Publication Type
| Journal Articles | 2 |
| Reports - Evaluative | 1 |
| Reports - Research | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkSears, Christopher R.; Campbell, Crystal R.; Lupker, Stephen J. – Journal of Experimental Psychology: Human Perception and Performance, 2006
What is the effect of a word's higher frequency neighbors on its identification time? According to activation-based models of word identification (J. Grainger & A. M. Jacobs, 1996; J. L. McClelland & D. E. Rumelhart, 1981), words with higher frequency neighbors will be processed more slowly than words without higher frequency neighbors because of…
Descriptors: Word Recognition, Identification, Word Frequency, Models
Perea, Manuel; Lupker, Stephen J. – Journal of Memory and Language, 2004
Nonwords created by transposing two "adjacent" letters (i.e., transposed-letter (TL) nonwords like "jugde") are very effective at activating the lexical representation of their base words. This fact poses problems for most computational models of word recognition (e.g., the interactive-activation model and its extensions), which assume that exact…
Descriptors: Alphabets, Word Recognition, Models, Lexicology
