Visual encoding of partial unknown shape boundaries

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.