Human learners are adept at grasping the complex relationships underlying incoming
sequential input. In the present work, we formalize complex relationships as graph structures
derived from temporal associations, in motor sequences. Next, we explore the extent to
which learners are sensitive to key variations in the topological properties inherent to those
graph structures. Participants performed a probabilistic motor sequence task in which the
order of button presses was determined by the traversal of graphs with modular, lattice-like …