Network constraints on learnability of probabilistic motor sequences


Human learners are adept at grasping the complex relationships underlying incoming sequential input1. In the present work, we formalize complex relationships as graph structures2 derived from temporal associations3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties5 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 or random organization. Graph nodes each represented a unique button press, and edges represented a transition between button presses. The results indicate that learning, indexed here by participants’ response times, was strongly mediated by the graph’s mesoscale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node’s number of connections (degree) and a node’s role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for the level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.

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Fig. 1: Experimental setup.
Fig. 2: Modular graph learning effects.
Fig. 3: Learning rate and edge surprisal: impact of new edges on reaction time.
Fig. 4: Relationship between small- and large-scale graph statistics and reaction time.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.


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We thank D. M. Lydon-Staley and S. H. Tompson for feedback on earlier versions of this manuscript. This work was supported by the National Science Foundation CAREER award (PHY-1554488 to D.S.B.), Army Research Laboratory through contract number W911NF-10-2-0022 and Army Research Office through contract number Grafton-W911NF-16-1-0474 and contract number DCIST- W911NF-17-2-0181. We also acknowledge additional support from the John D. and Catherine T. MacArthur Foundation, Alfred P. Sloan Foundation, ISI Foundation, Paul G. Allen Family Foundation, Army Research Office (Bassett-W911NF-14-1-0679), Office of Naval Research, National Institute of Mental Health (2-R01-DC-009209-11, R01 MH-112847, R01 MH-107235, R21 MH-106799 and R01 MH-113550), National Institute of Child Health and Human Development (1R01HD086888-01), National Institute of Neurological Disorders and Stroke (R01 NS099348) and National Science Foundation (BCS-1441502, BCS-1631550 and CNS-1626008). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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A.E.K., E.A.K., J.M.V. and D.S.B. conceived the project and planned the experiments and analyses. A.E.K. performed the experiments and analyses. A.E.K., E.A.K. and D.S.B. wrote the manuscript and Supplementary Information. E.A.K., J.M.V. and D.S.B. edited the manuscript and Supplementary Information.

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Correspondence to Danielle S. Bassett.

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Supplementary Methods, Supplementary Figures 1–10, Supplementary Tables 1–6

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Kahn, A.E., Karuza, E.A., Vettel, J.M. et al. Network constraints on learnability of probabilistic motor sequences. Nat Hum Behav 2, 936–947 (2018).

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