TY - JOUR T1 - Learning hierarchical sequence representations across human cortex and hippocampus JF - bioRxiv DO - 10.1101/583856 SP - 583856 AU - Simon Henin AU - Nicholas B. Turk-Browne AU - Daniel Friedman AU - Anli Liu AU - Patricia Dugan AU - Adeen Flinker AU - Werner Doyle AU - Orrin Devinsky AU - Lucia Melloni Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/05/11/583856.abstract N2 - Sensory input arrives in continuous sequences that humans experience as units, e.g., words and events. The brain’s ability to discover extrinsic regularities is called statistical learning. Structure can be represented at multiple levels, including transitional probabilities, ordinal position, and identity of units. To investigate sequence encoding in cortex and hippocampus, we recorded from intracranial electrodes in human subjects as they were exposed to auditory and visual sequences containing temporal regularities. We find neural tracking of regularities within minutes, with characteristic profiles across brain areas. Early processing tracked lower-level features (e.g., syllables) and learned units (e.g., words); while later processing tracked only learned units. Learning rapidly shaped neural representations, with a gradient of complexity from early brain areas encoding transitional probability, to associative regions and hippocampus encoding ordinal position and identity of units. These findings indicate the existence of multiple, parallel computational systems for sequence learning across hierarchically organized cortico-hippocampal circuits.Competing Interest StatementThe authors have declared no competing interest. ER -