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Sumeyye Cakal, Ru Qi Yu, Jiaying Zhao; The perception of multi-dimensional regularities. Journal of Vision 2015;15(12):1033. doi: 10.1167/15.12.1033.
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© 2017 Association for Research in Vision and Ophthalmology.
Regularities are prevalent in many aspects of the environment. How does the visual system extract structured information from multiple sources? One possibility is that the visual system selectively focuses on one source. Alternatively, it may incorporate all sources to form a weighted representation of the regularities. To address this question, we generated matrices containing cells that varied independently on the color dimension (red/blue) or the shape dimension (circle/square). Each matrix could be divided into two equal halves either horizontally or vertically. One half was fully random, whereas the other half was structured (i.e., organized in chunks). Observers discriminated the boundary between the two halves in three conditions (Experiment 1). In the color condition, the cells were structured only on the color dimension; in the shape condition, the cells were structured only on the shape dimension; and in the color+shape condition, the cells were structured both on the color and the shape dimensions. Importantly, each dimension contained an equal amount of regularities. We found that the boundary discrimination accuracy was higher in the color+shape condition than that in the shape condition, but not different from the color condition. This suggests that color was prioritized when regularities were present in both dimensions. To examine whether this prioritization was specific to color, we introduced a new surface dimension (solid/hollow) in the matrices (Experiment 2). Now the boundary discrimination accuracy was the highest in the surface condition, compared to the other dimensions. Critically, the accuracy was equally high when the cells were structured on all three dimensions. This suggests that the surface dimension was prioritized over the others. These findings demonstrate that the visual system relies on one feature dimension to extract regularities, even though every dimension is equally informative. Moreover, such extraction did not benefit from the presence of multiple sources of regularities.
Meeting abstract presented at VSS 2015
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