A Persian-English Cross-Linguistic Dataset for Research on the Visual Processing of Cognates and Noncognates

Abstract

Finding out which lexico-semantic features of cognates are critical in cross-language studies and comparing these features with noncognates helps researchers to decide which features to control in studies with cognates. Normative databases provide necessary information for this purpose. Such resources are lacking in the Persian language. We created a dataset and determined norms for the essential lexico-semantic features of 288 cognates and noncognates and matched them across conditions. Furthermore, we examined the relationship between these features and the response time (RT) and accuracy of responses in a masked-priming lexical decision task. This task was performed in English by Persian-English speakers in conditions where the prime and target words were related or unrelated in terms of meaning and/or form. Overall, familiarity with English words and English frequency were the best predictors of RT in related and unrelated priming conditions. Pronunciation similarity also predicted RT in the related condition for cognates, while the number of phonemes in the prime predicted RT for the unrelated condition. For both related and unrelated conditions, English frequency was the best predictor for noncognates. This bilingual dataset can be used in bilingual word processing and recognition studies of cognates and noncognates.