Time-resolved functional connectivity during visuomotor graph learning

Humans naturally attend to patterns that emerge in our perceptual environments, building mental models that allow future experiences to be processed more effectively and efficiently. Statistical learning research shows that people extract structure from stimuli even when it is not explicitly observable. A growing line of work formalizes this implicit structure as graphs in which perceptual events correspond to nodes and statistical transitions to edges, revealing that behavior is sensitive to underlying graph topology. Yet little is known about how different topologies shape the neural dynamics of learning itself. Here, we used time-resolved network analyses of fMRI data collected during a visuomotor graph-learning task in which stimuli were presented according to random walks on either a modular or lattice graph. Participants responded faster to modular graphs early in learning, though this advantage diminished over runs, replicating prior behavioral findings. Neurally, task performance was characterized by a flexible visual system, relatively stable large-scale community structure, and increased cohesiveness (or within-system recruitment) of the dorsal attention, limbic, default-mode, and subcortical systems. Across runs, integration between visual and ventral-attention regions increased, while coupling between frontoparietal control and visual/dorsal-attention regions decreased-signaling a shift from top-down to bottom-up processing as learning progressed. These results show that the brain's dynamic functional organization reflects the statistical topology of experience: stronger integration among limbic, default-mode, temporoparietal, and subcortical systems predicted faster responses for modular but not lattice graphs. By linking behavioral sensitivity to graph structure with its time-resolved neural correlates, this work advances understanding of how the brain represents and adapts to complex statistical environments.