Humans are known to detect probabilistic regularities in learning materials. In language, this ability is particularly well-evidenced for the auditory modality. In two experiments, we tested if learners rely on statistical regularities also when processing text. Italian speakers performed a lexical decision task on a large set of letter strings, distinguishing between nonword strings and strings that were words in a novel language. Participants successfully detected that novel words complied with the statistics of the Italian lexicon, and used this information to perform the task.

It has been suggested that the visual word identification system identifies recurrent letter clusters (n–grams) as a bridge between letters and words. Some evidence may suggest, rather indirectly, that n–gram coding is a more general visual mechanism, used beyond reading to extract statistical regularities in smaller visual units to build larger visual units. Here we test this hypothesis directly, and explore the boundaries of the phenomenon—what type of visual object, if any, fails n–gram based statistical learning?

The temporal dynamics of spoken word recognition are highly debated. While some studies suggest serial processing of sublexical and lexico-semantic information (e.g., Kocagoncu et al., 2017), others reported parallel processing since early stages (e.g., Lewis & Poeppel, 2014). The current study employed multiple linear regression to predict MEG-evoked responses in 20 native Italian speakers during the semantic judgment of 438 Italian spoken words.