A model of human heading judgement in forward motion

We developed a new computational model of human heading judgement from retinal flow. The model uses two assumptions: a large number of sampling points in the flow field and a symmetric sampling region around the origin. The algorithm estimates self-rotation parameters by calculating statistics whose expectations correspond to the rotation parameters. After the rotational components are removed from the retinal flow, the heading direction is recovered from the flow field. Performance of the model was compared with human data in three psychophysical experiments. In the first experiment, we generated stimuli which simulated self-motion toward the ground, a cloud or a frontoparallel plane and found that the simulation results of the model were consistent with human performance. In the second and third experiment, we measured the slope of the perceived versus simulated heading function when a perturbation velocity weighted according to the distance relative to the fixation distance was added to the vertical velocity component under the cloud condition. It was found that as the magnitude of the perturbation was increased, the slope of the function increased. The characteristics observed in the experiments can be explained well by the proposed model.