Statistical learning of temporal community structure in the hippocampus.
The hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher-order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher-order structure that requires sensitivity to overlapping associations.
Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540.
Anna C
AC
Schapiro
Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540.
Nicholas B
NB
Turk-Browne
Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540.
Kenneth A
KA
Norman
Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, 08540.
Matthew M
MM
Botvinick
D001931:Brain Mapping; D002560:Cerebrovascular Circulation / Q000502:physiology; D007839:Functional Laterality; D006624:Hippocampus / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D009434:Neural Pathways / Q000502:physiology; D009483:Neuropsychological Tests; D010100:Oxygen / Q000097:blood; D017397:Prefrontal Cortex / Q000502:physiology; D011337:Probability Learning; D013998:Time Perception / Q000502:physiology*
D010100:Oxygen
background connectivity; event representation; fMRI; pattern analysis; transition probability