Figure 6. Convolutional Neural Network for classifying patterns of motion vectors as rotating or wobbling.

(A) Two examples of the 9000 vector fields from random dot moving stimuli that were used to train and validate the CNN, (Left) the rotating vector field and (Right) the wobbling vector field. The 9000 vector fields were randomly divided into 6300 training and 2700 validation fields. (B) The network consists of two convolutional layers followed by two fully connected layers. The output layer gives a confidence level between rotation and wobbling on a 0.0 −1.0 scale. (C) Proportion of non-rigid percepts for CNN output from motion energy units for each shape. Symbol shape and color indicate ring-pair shape. For all ring shapes, the proportion of non-rigid classifications was 0.996