During visual word processing readers identify chunks of co-occurring letters and code for their typical position within words. Using an artificial script, we examined whether these phenomena can be explained by the ability to extract visual regularities from the environment. Participants were first familiarized with a lexicon of pseudoletter strings, each comprising an affix-like chunk that either followed (Experiment 1) or preceded (Experiment 2) a random character sequence. In the absence of any linguistic information, chunks could be defined only by their statistical properties - similarly to affixes in the real language, chunks occurred frequently and assumed a specific position within strings. In a later testing phase, we found that participants were more likely to attribute a previously unseen string to the familiarization lexicon if it contained an affix, and if the affix appeared in its typical position. Importantly, these findings suggest that readers may chunk words using a general, language-agnostic cognitive mechanism that captures statistical regularities in the learning materials.
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