Visual Development Aids the Acquisition of Motion Velocity Sensitivities

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

Bibtex Metadata Paper


Robert Jacobs, Melissa Dominguez


We consider the hypothesis that systems learning aspects of visual per- ception may benefit from the use of suitably designed developmental pro- gressions during training. Four models were trained to estimate motion velocities in sequences of visual images. Three of the models were “de- velopmental models” in the sense that the nature of their input changed during the course of training. They received a relatively impoverished visual input early in training, and the quality of this input improved as training progressed. One model used a coarse-to-multiscale develop- mental progression (i.e. it received coarse-scale motion features early in training and finer-scale features were added to its input as training progressed), another model used a fine-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 coarse-to-multiscale model performed best. Hypotheses are offered to account for this model’s superior performance. We conclude that suit- ably designed developmental sequences can be useful to systems learn- ing to estimate motion velocities. The idea that visual development can aid visual learning is a viable hypothesis in need of further study.