Bottom-up probabilistic information in visual word recognition: interactions with phonological and morphological functions
| Autor: | Chiara Celata |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Phonotactics
050101 languages & linguistics Linguistics and Language Computer science business.industry 05 social sciences Word processing Probabilistic logic Phonology computer.software_genre 050105 experimental psychology Language and Linguistics Rule-based machine translation Morpheme 0501 psychology and cognitive sciences Artificial intelligence business Priming (psychology) computer Natural language processing Consonant cluster |
| Popis: | This study investigates the role of probabilistic phonotactics in the processing of consonant clusters (as opposed to consonant-vowel sequences) and of complex words (as opposed to monomorphemic words) in two experiments of visual word recognition with masked fragment priming. Target words are more efficiently recognized when the transition probabilities between an initial fragment and the following segment in the target are high, but this effect is not equally distributed across all areas of the phonology. It was found that speakers rely more heavily on transition probabilities between segments of a consonant cluster than between segments of a consonant-vowel sequence. The morphemic integrity of the cluster is also a relevant parameter in predicting the role of probabilistic phonotactics in lexical access, with prefixed items showing a stronger facilitatory effect than pseudoprefixed items. Taken together, these findings demonstrate that phonotactic probabilities are relevant in visual word recognition, and that their effects interact with higher order linguistic information. These findings also enrich our view of the selection-and-competition mechanism that governs written word processing by providing direct evidence of the fact that the speakers anticipate the segment to the right based on implicit knowledge of phonotactic probabilities. |
| Databáze: | OpenAIRE |
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