Statistical learning of temporal community structure in the hippocampus

Abstract

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.

Keywords: background connectivity; event representation; fMRI; pattern analysis; transition probability.

© 2015 Wiley Periodicals, Inc.

Figures

Figure 1. Pattern similarity in the hippocampus

(A) Graph used to generate sequences of stimuli. An abstract visual stimulus was assigned to each node and the edges represent possible transitions between stimuli. The three graph communities are colored in purple, green, and orange, with community boundary nodes in a lighter shade. (B) Mean pattern similarity (Fisher transformed correlation) for within and between community pairs of items when defining patterns across all voxels in an ROI. Error bars denote ± 1 within-subject SEM (Morey, 2008). * p < .05. (C) MDS for visualization of the representational clustering in the bilateral hippocampus. Colors indicate correspondence to the nodes in the graph. (D) Voxel centers of searchlights that showed greater similarity within- vs. across-community within right hippocampus (outlined with green border). The searchlight surrounding the brightest voxel passed a threshold of p < .001 uncorrected.

Figure 2. Boundary effects in the hippocampus

(A) Illustration of the regressor used in the GLM to examine sensitivity to boundaries, with higher activity within a community compared to at event boundaries. (B) The average beta weights from the boundary regressor across voxels in hippocampal ROIs. The negative beta values reflect lower activity at community boundaries compared to within communities. Error bars denote ± 1 SEM. * p < .05; § p < .1.

Figure 3. Background connectivity with the hippocampus

Connectivity between mPFC and hippocampus and between left IFG and hippocampus within a community and at community boundaries. Error bars denote ± 1 SEM. * p < .05; ** p < .01; § p < .1.