Neural models of motion integration and segmentation

Abstract

A neural model is developed of how motion integration and segmentation processes compute global motion percepts. Figure-ground properties, such as occlusion, influence which motion signals determine the percept. For visible apertures, a line's extrinsic terminators do not specify true line motion. For invisible apertures, a line's intrinsic terminators create veridical feature tracking signals, which are amplified before they propagate across space and are integrated with ambiguous motion signals within line interiors. This integration process is the result of several processing stages: directional transient cells respond to image transients and input to a directional short-range filter that selectively boosts feature tracking signals. Competitive interactions further boost feature tracking signals and create speed-selective receptive fields. A long-range filter gives rise to true directional cells by pooling signals over multiple orientations and opposite contrast polarities. A distributed population code of speed tuning realizes a size–speed correlation, whereby activations of multiple spatially short-range filters of different sizes are transformed into speed-tuned cell responses. These mechanisms use transient cell responses, output thresholds that covary with filter size, and competition. The model reproduces empirically derived speed discrimination curves and simulates data showing how visual speed perception and discrimination are affected by stimulus contrast.