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The present paper addresses the study of non‐arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non‐arbitrary phonological patterns across a set of typologically distant languages. Different sequence‐processing neural networks are trained in a set of languages to associate the phonetic vectorization of a set of words to their sensory (Experiment 1), semantic (Experiment 2), and word‐class representations (Experiment 3). The models are then tested, without further training, in a set of novel instances in a language belonging to a different language family, and their performance is compared with a randomized baseline. We show that the three cross‐domain mappings can be successfully transferred across languages and language families, suggesting that the phonological structure of the lexicon is pervaded with language‐invariant cues about the words' meaning and their syntactic classes. [ABSTRACT FROM AUTHOR]

We release a database of cloze probability values, predictability ratings, and computational estimates for a sample of 205 English sentences (1726 words), aligned with previously released word-by-word reading time data (both self-paced reading and eye-movement records; Frank et al., Behavior Research Methods, 45(4), 1182–1190. 2013) and EEG responses (Frank et al., Brain and Language, 140, 1–11. 2015). Our analyses show that predictability ratings are the best predictors of the EEG signal (N400, P600, LAN) self-paced reading times, and eye movement patterns, when spillover effects are taken into account. The computational estimates are particularly effective at explaining variance in the eye-tracking data without spillover. Cloze probability estimates have decent overall psychometric accuracy and are the best predictors of early fixation patterns (first fixation duration). Our results indicate that the choice of the best measurement of word predictability in context critically depends on the processing index being considered. [ABSTRACT FROM AUTHOR]