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Description: In this study we employed fast periodic visual stimulation (FPVS), a highly sensitive and behaviour-free approach, to examine whether a discrimination-selective neural signal can be elicited even in the absence of preexisting categorical representations. Sequences of words and pseudo-reading material (base stimuli) presented at a fast rate of 6Hz were interleaved with oddballs, inserted periodically every 5 items. Crucially, sequences were made of stimuli that belonged to the same category (e.g., words in words) and the only distinction between base and oddball items was the frequency of individual tokens within a stream. Within a few minutes of stimulation, oddballs evoked a reliable neural response at the predefined stimulation frequency of 1.2 Hz (i.e., 6/5) and its harmonics, indicating the discrimination between two locally-defined, distinct groups of items informed only by token frequency. This neural response was independent of stimulus familiarity -- it emerged similarly with words, pseudowords, letter strings and pseudoletters. Our findings provide evidence for an online neural marker of implicit statistical learning emerging fast and automatically under rapid presentation conditions, and highlight the potential of FPVS for this line of research.

Has supplemental materials for Frequency-based Discrimination in Fast Periodic Visual Stimulation on OSF Preprints

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