Network constraints on learnability of probabilistic motor sequences

TL;DR

This study demonstrates that the topological structure of graph-based motor sequences significantly influences human learning, with modular graphs facilitating faster response times and network properties affecting learning beyond practice effects.

Contribution

It introduces a network science framework to quantify how different graph topologies impact the learnability of probabilistic motor sequences, highlighting the role of meso-scale organization.

Findings

Modular graphs lead to shorter response times than random and lattice graphs.

Node degree and betweenness centrality influence learning independently of practice.

Graph architecture constrains the encoding and acquisition of sequential information.

Abstract

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, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale 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 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.