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20 February 2012A compressed sensing model of crowding in peripheral vision
We here model peripheral vision in a compressed sensing framework as a strategy of optimally guessing what
stimulus corresponds to a sparsely encoded peripheral representation, and find that typical letter-crowding effects
naturally arise from this strategy. The model is simple as it consists of only two convergence stages. We apply
the model to the problem of crowding effects in reading. First, we show a few instructive examples of letter
images that were reconstructed from encodings with different convergence rates. Then, we present an initial
analysis of how the choice of model parameters affects the distortion of isolated and flanked letters.