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Developmental Constraints Aid the Acquisition of Binocular Disparity Sensitivities
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@article{dominguez:Aid-Binocular:NC02,
  author =       {Melissa Dominguez and Robert A. Jacobs},
  title =        {Developmental Constraints Aid the Acquisition of Binocular Disparity Sensitivities},

  journal =      {Neural Computation},
  volume =       {15}
  number =       {1}
  year =         {2002},
  month =        {June},

  abstract = { This article considers the hypothesis that systems
                  learning aspects of visual perception may benefit
                  from the use of suitably designed developmental
                  progressions during training. We report the results
                  of simulations in which four different models were
                  trained to detect binocular disparities in paris of
                  visual images. Three of the models were
                  "developmental models" in the sense that the nature
                  of their visual input changed during the course of
                  training. These models received a relatively
                  impoverished visual input early in training, and the
                  quality of this input improved as training
                  progressed. One model used a
                  course-scale-to-multiscale developmental
                  progression, and another model used a
                  fine-scale-to-multiscale progression, and the third
                  model used a random progression. The final model was
                  non-developmental in the sense that the nature of
                  its input remained the same throughout the training
                  period. The simulation results show that the two
                  developmental models whose progressions were
                  organized by spatial frequency content consistently
                  outperformed the non-developmental and random
                  developmental models. We speculate that the superior
                  performance of these two models is due to two
                  important features of their developmental
                  progressions: (1) these models were exposed to
                  visual inputs at a single scale early in training,
                  and (2) the spatial scale of their inputs progressed
                  in an orderly fashion from one scale to a
                  neighboring scale during training. Simulation resuls
                  consistent with these speculations are presented. We
                  conclude that suitably designed developmental
                  sequences can be useful to systems learning to
                  detect binocular disparities. The idea that visual
                  development can aid visual learning is a viable
                  hypothesis in need of future study. }                  

}