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Visual encoding of partial unknown shape boundaries

1 Department of Psychology, University of Southern California, Los Angeles, California USA
2 The School of Psychology, University of Auckland, Auckland New Zealand, California USA

Prior research has found that known shapes and letters can be recognized from a sparse sampling of dots that mark locations on their boundaries. Further, unknown shapes that are displayed only once can be identified by a matching protocol, and here also, above-chance performance requires very few boundary markers. The present work examines whether partial boundaries can be identified under similar low-information conditions. Several experiments were conducted that used a match-recognition task, with initial display of a target shape followed quickly by a comparison shape. The comparison shape was either derived from the target shape or was based on a different shape, and the respondent was asked for a matching judgment, i.e., did it “match” the target shape. Stimulus treatments included establishing how density affected the probability of a correct decision, followed by assessment of how much positioning of boundary dots affected this probability. Results indicate that correct judgments were possible when partial boundaries were displayed with a sparse sampling of dots. We argue for a process that quickly registers the locations of boundary markers and distills that information into a shape summary that can be used to identify the shape even when only a portion of the boundary is represented.
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Keywords shape recognition; shape encoding; boundary marking

Citation: Hannah Nordberg, Michael J Hautus, Ernest Greene. Visual encoding of partial unknown shape boundaries. AIMS Neuroscience, 2018, 5(2): 132-147. doi: 10.3934/Neuroscience.2018.2.132


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This article has been cited by

  • 1. Ernest Greene, New encoding concepts for shape recognition are needed, AIMS Neuroscience, 2018, 5, 3, 162, 10.3934/Neuroscience.2018.3.162
  • 2. Ernest Greene, Jack Morrison, Computational Scaling of Shape Similarity that Has Potential for Neuromorphic Implementation, IEEE Access, 2018, 1, 10.1109/ACCESS.2018.2853656
  • 3. Taylor Burchfield, Ernest Greene, Michael B. Steinborn, Evaluating spatiotemporal integration of shape cues, PLOS ONE, 2020, 15, 5, e0224530, 10.1371/journal.pone.0224530

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