Implicit Statistical Learning in Fast Periodic Visual Stimulation

Journal: 

Cortex

Date: 

February, 2021

Authors: 

Mara De Rosa, Maria Ktori, Yamil Vidal, Roberto Bottini and Davide Crepaldi [Mara and Maria share first authorship]

Humans capitalize on statistical cues to discriminate fundamental units of information within complex streams of sensory input. We sought neural evidence for this phenomenon by combining fast periodic visual stimulation (FPVS) and EEG recordings. Skilled readers were exposed to sequences of linguistic items with decreasing familiarity, presented at a fast rate and periodically interleaved with oddballs. Crucially, each sequence comprised stimuli of the same category, and the only distinction between base and oddball items was the frequency of occurrence of individual tokens within a stream. Frequency-domain analyses revealed robust neural responses at the oddball presentation rate in all conditions, reflecting the discrimination between two locally-emerged groups of items purely informed by token frequency. Results provide evidence for a fundamental frequency-tuned mechanism that operates under high temporal constraints and could underpin category bootstrapping. Concurrently, they showcase the potential of FPVS for providing online neural markers of implicit statistical learning.

A pre-print and all data and scripts related to this project are available here.