Human Sensitivity to Community Structure Is Robust to Topological Variation
Joint Authors
Karuza, Elisabeth A.
Kahn, Ari E.
Bassett, Danielle S.
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-11
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles.
The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work.
Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively.
Nodes of the graph correspond to a unique object and edges correspond to their immediate succession in the stream.
In short, we find that previously observed processing costs associated with community boundaries persist across an array of graph architectures.
These results indicate that statistical learning mechanisms can flexibly accommodate variation in community structure during visual event segmentation.
American Psychological Association (APA)
Karuza, Elisabeth A.& Kahn, Ari E.& Bassett, Danielle S.. 2019. Human Sensitivity to Community Structure Is Robust to Topological Variation. Complexity،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1132907
Modern Language Association (MLA)
Karuza, Elisabeth A.…[et al.]. Human Sensitivity to Community Structure Is Robust to Topological Variation. Complexity No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1132907
American Medical Association (AMA)
Karuza, Elisabeth A.& Kahn, Ari E.& Bassett, Danielle S.. Human Sensitivity to Community Structure Is Robust to Topological Variation. Complexity. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1132907
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132907