Word surprisal is a well-established computational predictor of human neural responses during language comprehension, but it remains less clear whether local semantic fit explains neural response variation beyond lexical expectation during naturalistic reading. Using the Dublin EEG-based Reading Experiment Corpus (DERC...
Since the 1990s, the fields of numerical cognition and psycholinguistics have evolved largely independently. However, the research since then demonstrates that language and numbers are intrinsically related, particularly in multilingual contexts. Language characteristics (e.g., morpho-syntactic properties) and language...
A neural network is used to model a bilingual mapping within an English-Mandarin semantic space and indicates interesting possibilities for using quantitative word-level modeling as a tool to analyze the overlapping impacts of bilingualism, emotionality, and abstraction on each other.
Previous evidence has shown an important role for semantic diversity in visual word recognition. These results are theoretically relevant, as they suggest that it is not only the mere repetition of word reading (lexical frequency) but also the semantic context in which words are encountered that affects lexical represe...
Abstract This data-driven study investigates the licensing factors of object omission in English transitive verbs (e.g., I’m eating Ø vs. *I’m devouring Ø). Using corpus data from the British component of the International Corpus of English and psycholinguistic measurements from the South Carolina Psycholinguistic Meta...
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