Flow parsing as causal source separation: A computational model for concurrent retrieval of object and self-motion information from optic flow

Optic flow, the retinal pattern of motion that is experience during self-motion contains information about ones direction of heading. If the visual scene contains other moving objects optic flow becomes a combination of self-motion and independent object motion. The global pattern due to self-motion is locally confounded, and the locally restricted object flow is the sum of components due to these different causal sources of motion. Nonetheless, humans are able to retrieve information from such flow accurately, including the direction of heading and the scene-relative motion of an object. One way to handle such complex flow is flow parsing, a process speculated to allow the brains sensitivity to optic flow to separate the causal sources of retinal motion in information due to self-motion and information due to object motion. In a computational model that retrieves object and self-motion information from optic flow, we implemented such a process of causal separation based on heading likelihood maps, whose distributions indicate the consistency of parts of the flow with self-motion alone. The flow parsing allows for concurrent estimation of heading, the detection and localization of and independently moving object, and the estimation of scene-relative motion of that object. We developed a paradigm that allows the model to perform all the different estimations while system-atically varying how the object contributes to the flow field. Simulations of that paradigm showed that the model replicates many aspects of human performance, including the dependence of heading estimation on object speed and how different object movements bias that estimation. Regarding object detection and motion estimation, the models results fit human behavioral data, the latter even for flow of reduced quality.