Morphemes are chunks of frequently co-occurring letters with semantic and syntactic properties. Using an artificial script, the present study examined the role of visual statistical learning in the way developing readers learn to recognize morphemes and code for their typical position within words. Ninety-three children (Grades 2-4) were familiarized with a lexicon of pseudo-letter strings, each composed of a random character sequence and an affix-like chunk of frequently co-occurring characters.

Multiple sources of evidence support the idea that reading and visual word identification build upon statistical regularties in the (written) language. However, direct experimental evidence for this connection is still meager, and sometimes mixed (e.g., bigram frequency effects). Focusing on children, I present evidence that, yes, there is evidence for sensitivity to letter statistics in natural reading for comprehension, specifically in the form of nGram frequency effects that can't be traced back to word-level statistics.

Using an artificial script, we have previously demonstrated that readers use co-occurrence statistics to learn about the presence and position of af ix-like chunks in strings of pseudo-letters (Lelonkiewicz, Ktori & Crepaldi, 2020). These findings were taken as evidence that visual statistical learning might be implicated in morphological processing during visual word recognition.

Reading involves an interaction of linguistic and perceptual processes, in which top-down, lexical knowledge aids letter perception. Does such facilitation interact with letter co-occurrence regularities? Skilled readers were briefly exposed to strings of five consonants; critically, letters in position 2 and 4 were embedded in either high (L in e.g., GLVTZ) or low (e.g., NLRTZ) transitional probability (TP) triplets. When presented with two strings differing by the critical letter (GLVTZ vs.