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Aaron Glick, József Fiser; The Less-Is-More principle in realistic visual statistical learning. Journal of Vision 2009;9(8):877. doi: https://doi.org/10.1167/9.8.877.
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© ARVO (1962-2015); The Authors (2016-present)
While in previous studies, a number of abstract characteristics of visual statistical learning have been clarified under various 2-dimesional settings, little effort was directed to understand how real visual dimensions in 3-dimensonal scenes interact during such learning. In a series of experiments using realistic 3D shapes and the dimensions of color, texture, and motion, we tested the Less-Is-More principle of learning, namely the proposal that information in independent dimensions do not interact in a simple additive manner to help learning. Following the original statistical learning paradigm, twelve arbitrary 3D shapes were used to compose scenes, where shape pairs followed particular co-occurrence pattern and scenes were composed of random combinations of such pairs. Similarly to the results with abstract 2D shapes, subjects automatically and implicitly learned the underlying structure of the scenes. However, there were notable differences in learning depending on the features of the stimuli. Humans performed well above chance in the baseline experiment with full colored and textured shapes (63% correct, p[[lt]]0.001). When they received the same training but only with colors using a single type of shape and no texture, performance dropped to chance (51%, ns.), showing that providing the same color label information without “hooks” was not useful. However, removing color and texture or color and shape improved performance (both 68%, p[[lt]]0.001) showing that reducing the richness of the representations is not always detrimental. Finally, adding characteristic motion pattern to each shape did not elevate performance (65%, p[[lt]]0.001) demonstrating that even the most effective type of visual information does not necessarily speed up learning. These results support the Less-Is-More idea that the most effective learning requires the maximum amount of information that the system can reliably process based on its capacity limit and internal representation, which is not equivalent to having the most possible information.
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