Broken time reversal symmetry in visual motion detection.

Our intuition suggests that when a movie is played in reverse, our perception of motion in the reversed movie will be perfectly inverted compared to the original. This intuition is also reflected in many classical theoretical and practical models of motion detection. However, here we demonstrate that this symmetry of motion perception upon time reversal is often broken in real visual systems. In this work, we designed a set of visual stimuli to investigate how stimulus symmetries affect time reversal symmetry breaking in the fruit fly 's well-studied optomotor rotation behavior. We discovered a suite of new stimuli with a wide variety of different properties that can lead to broken time reversal symmetries in fly behavioral responses. We then trained neural network models to predict the velocity of scenes with both natural and artificial contrast distributions. Training with naturalistic contrast distributions yielded models that break time reversal symmetry, even when the training data was time reversal symmetric. We show analytically and numerically that the breaking of time reversal symmetry in the model responses can arise from contrast asymmetry in the training data, but can also arise from other features of the contrast distribution. Furthermore, shallower neural network models can exhibit stronger symmetry breaking than deeper ones, suggesting that less flexible neural networks promote some forms of time reversal symmetry breaking. Overall, these results reveal a surprising feature of biological motion detectors and suggest that it could arise from constrained optimization in natural environments.