Human sensitivity to community structure is robust to topological variation

Elisabeth Karuza, Ari E. Kahn, Danielle S. Bassett

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number8379321
JournalComplexity
Volume2019
DOIs
StatePublished - Jan 1 2019

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Learning
Costs and Cost Analysis

All Science Journal Classification (ASJC) codes

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title = "Human sensitivity to community structure is robust to topological variation",
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Human sensitivity to community structure is robust to topological variation. / Karuza, Elisabeth; Kahn, Ari E.; Bassett, Danielle S.

In: Complexity, Vol. 2019, 8379321, 01.01.2019.

Research output: Contribution to journalArticle

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