A neural model for vector decomposition and relative-motion perception

Abstract

The perception of motion not only depends on the detection of motion signals but also on choosing and applying reference-frames according to which motion is interpreted. Here we propose a neural model that implements the common-fate principle for reference-frame selection. The model starts with a retinotopic layer of directionally-tuned motion detectors. The Gestalt common-fate principle is applied to the activities of these detectors to implement in two neural populations the direction and the magnitude (speed) of the reference-frame. The output activities of retinotopic motion-detectors are decomposed using the direction of the reference-frame. The direction and magnitude of the reference-frame are then applied to these decomposed motion-vectors to generate activities that reflect relative-motion perception, i.e., the perception of motion with respect to the prevailing reference-frame. We simulated this model for classical relative motion stimuli, viz., the three-dot, rotating-wheel, and point-walker (biological motion) paradigms and found the model performance to be close to theoretical vector decomposition values. In the three-dot paradigm, the model made the prediction of perceived curved-trajectories for the target dot when its horizontal velocity was slower or faster than the flanking dots. We tested this prediction in two psychophysical experiments and found a good qualitative and quantitative agreement between the model and the data. Our results show that a simple neural network using solely motion information can account for the perception of group and relative motion.