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Sami Yousif, Vladislav Ayzenberg, Stella Lourenco; Spatial memory demands modulate shape representations. Journal of Vision 2016;16(12):793. doi: https://doi.org/10.1167/16.12.793.
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© ARVO (1962-2015); The Authors (2016-present)
For decades, scientists have struggled with the problem of object recognition. Human vision allows for the rapid recognition of objects even when presented from different viewpoints, requiring a representation that is both detailed and computationally efficient. The medial axis is one possibility. Recently, when subjects were tasked with spontaneously tapping once within a shape, the aggregated taps revealed evidence of a medial axis for various shapes (Firestone & Scholl, 2014). Nevertheless, it is unclear whether the medial axis representation is recruited for other processes such as object memory. Here we examined whether bias to the medial axis is modulated by spatial memory demands. Borrowing from a classic paradigm (Huttenlocher, Hedges, & Duncan, 1991), we asked participants to memorize the locations of either 1 or 20 dots within a rectangle and, once the dots had disappeared, to "place" those dots in their original locations. When only one dot was present, participants showed no bias in relation to the medial axis (p > .30) but were, instead, biased towards the "prototypes" of the rectangle's quadrants (p < .001). In contrast, with 20 dots, participants were significantly biased towards the medial axis over and above any bias towards other models (e.g., principal axis [p < .01], or center of rectangle [p < .001]). These findings suggest that humans have access to multiple shape representations that are task dependent. When the task requires recalling multiple locations, participants rely on a representation that captures the medial axis. However, when the task requires recalling a single location, subjects appear to maximize the precision of their estimates by segmenting space into smaller units (i.e., quadrants). We suggest that whereas a medial axis representation may be ideal for capturing a spatial "gist", other representations (i.e., segmentation with prototypes) are better suited to precise localization within a shape.
Meeting abstract presented at VSS 2016
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