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Human Sensitivity to Community Structure Is Robust to Topological Variation

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  • Elisabeth A. Karuza
  • Ari E. Kahn
  • Danielle S. Bassett

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.

Suggested Citation

  • Elisabeth A. Karuza & Ari E. Kahn & Danielle S. Bassett, 2019. "Human Sensitivity to Community Structure Is Robust to Topological Variation," Complexity, Hindawi, vol. 2019, pages 1-8, February.
    • Handle: RePEc:hin:complx:8379321
    DOI: 10.1155/2019/8379321
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    References listed on IDEAS

    as
    1. Ari E. Kahn & Elisabeth A. Karuza & Jean M. Vettel & Danielle S. Bassett, 2018. "Network constraints on learnability of probabilistic motor sequences," Nature Human Behaviour, Nature, vol. 2(12), pages 936-947, December.
    2. Celeste Kidd & Steven T Piantadosi & Richard N Aslin, 2012. "The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-8, May.
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