Abstract
Human learners are adept at grasping the complex relationships underlying incoming sequential input 1 . In the present work, we formalize complex relationships as graph structures 2 derived from temporal associations 3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties 5 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 936-947 |
| Number of pages | 12 |
| Journal | Nature Human Behaviour |
| Volume | 2 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2018 |
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All Science Journal Classification (ASJC) codes
- Social Psychology
- Experimental and Cognitive Psychology
- Behavioral Neuroscience
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Network constraints on learnability of probabilistic motor sequences. / Kahn, Ari E.; Karuza, Elisabeth A.; Vettel, Jean M.; Bassett, Danielle S.
In: Nature Human Behaviour, Vol. 2, No. 12, 01.12.2018, p. 936-947.Research output: Contribution to journal › Letter
TY - JOUR
T1 - Network constraints on learnability of probabilistic motor sequences
AU - Kahn, Ari E.
AU - Karuza, Elisabeth A.
AU - Vettel, Jean M.
AU - Bassett, Danielle S.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Human learners are adept at grasping the complex relationships underlying incoming sequential input 1 . In the present work, we formalize complex relationships as graph structures 2 derived from temporal associations 3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties 5 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.
AB - Human learners are adept at grasping the complex relationships underlying incoming sequential input 1 . In the present work, we formalize complex relationships as graph structures 2 derived from temporal associations 3,4 in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties 5 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.
UR - http://www.scopus.com/inward/record.url?scp=85056155677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056155677&partnerID=8YFLogxK
U2 - 10.1038/s41562-018-0463-8
DO - 10.1038/s41562-018-0463-8
M3 - Letter
C2 - 30988437
AN - SCOPUS:85056155677
VL - 2
SP - 936
EP - 947
JO - Nature Human Behaviour
JF - Nature Human Behaviour
SN - 2397-3374
IS - 12
ER -