The proposed research will develop a “universal” connectionist architecture and a set of models capable of handling key cross-linguistic differences across English, Spanish, and Hebrew. This work is complemented with targeted empirical investigations.Computational Investigations. I will develop improved connectionist models which employ more general architectural assumptions and that are able to learn to comprehend words from languages that impose different division of labour between orthography, phonology, and semantics. These models will explain and generate predictions regarding how a general architecture and the statistical properties of a language give rise to a range of cross-linguistic differences reported in the behavioural literature. This will be accomplished by combining a model of English, Spanish, and Hebrew with a response generation model to simulate performance in a range of tasks (e.g., differential transposed-letter priming in English & Spanish vs. Hebrew; stronger syllable priming effects in Spanish vs. English; greater semantic priming in naming Hebrew vs. English and English vs. Spanish). Additionally, the models will be developed within a biologically-plausible connectionist framework to connect the model to the ERP literature (Armstrong & Plaut; 2013; Laszlo & Armstrong, 2013).Behavioural & pilot EEG/ERP Investigations. To evaluate the modeling work, behavioral (and pilot electrophysiological) data will be collected from masked priming lexical decision tasks designed to engage orthographic, syllabic, and semantic representations. Language development will also be studied in pilot behavioural experiments with children.I will develop and release a public implementation of my empirically-validated models and data. These models will account for a broad range of existing effects, generate predictions for new experiments, and be readily extendable to related areas (e.g., modeling bilingualism and language impairments).