Human Sensitivity to Community Structure Is Robust to Topological Variation

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.

[1]  R. Aslin,et al.  Statistical learning of higher-order temporal structure from visual shape sequences. , 2002, Journal of experimental psychology. Learning, memory, and cognition.

[2]  Raymond J Dolan,et al.  A map of abstract relational knowledge in the human hippocampal–entorhinal cortex , 2017, eLife.

[3]  Danielle S Bassett,et al.  Individual Differences in Learning Social and Nonsocial Network Structures , 2017, Journal of experimental psychology. Learning, memory, and cognition.

[4]  Jeffrey M. Zacks,et al.  Event understanding and memory in healthy aging and dementia of the Alzheimer type. , 2006, Psychology and aging.

[5]  Richard N. Aslin,et al.  The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex , 2012, PloS one.

[6]  E. Vogel,et al.  Visual working memory capacity: from psychophysics and neurobiology to individual differences , 2013, Trends in Cognitive Sciences.

[7]  Erik D. Thiessen,et al.  Beyond Word Segmentation , 2013 .

[8]  M. Botvinick,et al.  Neural representations of events arise from temporal community structure , 2013, Nature Neuroscience.

[9]  Jeffrey M. Zacks,et al.  Event boundaries in perception affect memory encoding and updating. , 2009, Journal of experimental psychology. General.

[10]  R. Aslin,et al.  Statistical learning in a serial reaction time task: access to separable statistical cues by individual learners. , 2001, Journal of experimental psychology. General.

[11]  Timothy E. J. Behrens,et al.  Organizing conceptual knowledge in humans with a gridlike code , 2016, Science.

[12]  Danielle S. Bassett,et al.  Structure from noise: Mental errors yield abstract representations of events , 2018 .

[13]  Michael C Hout,et al.  The Novel Object and Unusual Name (NOUN) Database: A collection of novel images for use in experimental research , 2016, Behavior research methods.

[14]  Timothy F. Brady,et al.  PSYCHOLOGICAL SCIENCE Research Article Statistical Learning Using Real-World Scenes Extracting Categorical Regularities Without Conscious Intent , 2022 .

[15]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[16]  Elizabeth K. Johnson,et al.  Testing the limits of statistical learning for word segmentation. , 2010, Developmental science.

[17]  Scott P. Johnson,et al.  Visual statistical learning in infancy: evidence for a domain general learning mechanism , 2002, Cognition.

[18]  Michael C. Hout,et al.  The versatility of SpAM: a fast, efficient, spatial method of data collection for multidimensional scaling. , 2013, Journal of experimental psychology. General.

[19]  R N Aslin,et al.  Statistical Learning by 8-Month-Old Infants , 1996, Science.

[20]  Cynthia S. Q. Siew,et al.  Community structure in the phonological network , 2013, Front. Psychol..

[21]  Jeffrey M. Zacks,et al.  Event Segmentation , 2007, Current directions in psychological science.

[22]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[23]  R. Aslin,et al.  Encoding multielement scenes: statistical learning of visual feature hierarchies. , 2005, Journal of experimental psychology. General.

[24]  Sharon L. Thompson-Schill,et al.  Local Patterns to Global Architectures: Influences of Network Topology on Human Learning Relating Two Approaches Complex Networks Are Pervasive Network Topology Influences Learning and Memory Local Statistics Underpin Network Architecture , 2022 .

[25]  Jeffrey M. Zacks,et al.  Segmentation in the perception and memory of events , 2008, Trends in Cognitive Sciences.

[26]  Emanuel Averbach,et al.  The span of apprehension as a function of exposure duration , 1963 .

[27]  Danielle S Bassett,et al.  Process reveals structure: How a network is traversed mediates expectations about its architecture , 2017, Scientific Reports.

[28]  Jessica Maye,et al.  Infant sensitivity to distributional information can affect phonetic discrimination , 2002, Cognition.

[29]  Danielle S Bassett,et al.  Network constraints on learnability of probabilistic motor sequences , 2017, Nature Human Behaviour.

[30]  Mason A. Porter,et al.  Communities in Networks , 2009, ArXiv.

[31]  E. Newport,et al.  WORD SEGMENTATION : THE ROLE OF DISTRIBUTIONAL CUES , 1996 .