Words in the CLOUD: How orthographic similarity and bilingual experience facilitate foreign vocabulary learning

Abstract

Learning a foreign language as an adult is a rewarding but challenging endeavor that entails accruing a massive vocabulary. The literature independently highlights that orthographic similarity and bilingual experience could facilitate foreign vocabulary acquisition. Here, we explored the combined effects of orthographic similarity and bilingual experience on foreign vocabulary learning using behavioral and computational approaches. Experiment 1 compared Spanish monolingual, Spanish-English, and Spanish-Basque bilingual participants when learning an artificial vocabulary with varying orthographic similarity to Spanish. The results indicated that similar words were easier to recognize and produce than dissimilar words, and both bilingual groups outperformed the monolingual group in learning the vocabulary, irrespective of orthographic similarity. In Experiment 2, we developed a neural network model that implemented a unified, distributed, and dynamic view of the orthographic lexicon to explain how these effects could emerge from exposure to bilingual input. We simulated adults’ orthographic lexicons by pre-training this architecture on monolingual and bilingual input. We then tested the monolingual and bilingual versions’ capacity to learn the novel words used in the behavioral task. The simulations reproduced the orthographic similarity effects and showed an overall advantage of experience with bilingual input, as observed in the behavioral results. The present study unifies the seemingly disparate effects of orthographic similarity and bilingual experience under a common computational framework, whereby distributed representations of orthographic word forms are stored in a unified space and dynamically modified by learning experiences.