Learning hierarchical sequence representations across human cortex and hippocampus

• Published in: Science advances Vol. 7; no. 8
• Main Authors: Henin, Simon, Turk-Browne, Nicholas B., Friedman, Daniel, Liu, Anli, Dugan, Patricia, Flinker, Adeen, Doyle, Werner, Devinsky, Orrin, Melloni, Lucia
• Format: Journal Article
• Language: English
• Published: United States American Association for the Advancement of Science 19.02.2021
• Subjects: Brain; Hippocampus; Humans; Learning; Life Sciences; Neuroscience; SciAdv r-articles
• ISSN: 2375-2548; 2375-2548
• DOI: 10.1126/sciadv.abc4530

Sequence learning is tracked across the human brain, and the computational systems used to parse the sequences are highlighted. Sensory input arrives in continuous sequences that humans experience as segmented units, e.g., words and events. The brain’s ability to discover 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.