Network constraints on learnability of probabilistic motor sequences
Ari E. Kahn,
Elisabeth A. Karuza,
Jean M. Vettel and
Danielle S. Bassett ()
Additional contact information
Ari E. Kahn: University of Pennsylvania
Elisabeth A. Karuza: University of Pennsylvania
Jean M. Vettel: University of Pennsylvania
Danielle S. Bassett: University of Pennsylvania
Nature Human Behaviour, 2018, vol. 2, issue 12, 936-947
Abstract:
Abstract 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.
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.nature.com/articles/s41562-018-0463-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:nathum:v:2:y:2018:i:12:d:10.1038_s41562-018-0463-8
Ordering information: This journal article can be ordered from
https://www.nature.com/nathumbehav/
DOI: 10.1038/s41562-018-0463-8
Access Statistics for this article
Nature Human Behaviour is currently edited by Stavroula Kousta
More articles in Nature Human Behaviour from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().