Network constraints on learnability of probabilistic motor sequences
Bibliographic Data
| ID | 4654100 |
|---|---|
| Authors | Alfred E Kahn (0000-0002-2127-0507, DEVCOM Army Research Laboratory), Elisabeth A Karuza (0000-0003-4474-8793, University of the Sciences), Jean M Vettel (0000-0001-5336-2986, University of California, Santa Barbara), Danielle S Bassett (0000-0002-6183-4493, California University of Pennsylvania) |
| Year | 2018 |
| Volume | 2 |
| Issue | 12 |
| Pages | 936-947 |
| Publication date | 2018-11-05 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Nature Human Behaviour (JOURNAL) |
| Journal identifiers | ISSN: 2397-3374 • E-ISSN: 2397-3374 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1038/s41562-018-0463-8 |
| PMID | 30988437 |
| OpenAlex | W2756395463 |
| Language | EN |
| Citations received | 4 |
| References cited | 39 |
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. Kahn et al. show that learners capitalize on higher-order topological properties when they learn a probabilistic motor sequence based on a network traversal
Betweenness centrality · Centrality · Combinatorics · ENCODE · Graph · Learnability · Modular design · Probabilistic logic · Random graph · Complex Network Analysis Techniques · Computer Science · Functional Brain Connectivity Studies · Mathematics · Neural dynamics and brain function · Artificial Intelligence · Theoretical Computer Science
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| Unique citing works | 4 |
|---|---|
| Citations per year | 0.5 |
| Citation span | 2018 - 2025 (8) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |