Motion clouds: model-based stimulus synthesis of natural-like random textures for the study of motion perception

TL;DR

Motion Clouds are a flexible, model-based method for synthesizing natural-like random textures for studying motion perception, allowing controlled experimental stimuli generation across multiple sensory modalities.

Contribution

The paper introduces a novel generative framework for creating natural-like motion stimuli called Motion Clouds, with an open-source implementation and potential extensions to other sensory modalities.

Findings

Motion Clouds effectively simulate natural motion textures.

The framework allows precise control over stimulus parameters.

Extensions to color, touch, and audition are feasible.

Abstract

Choosing an appropriate set of stimuli is essential to characterize the response of a sensory system to a particular functional dimension, such as the eye movement following the motion of a visual scene. Here, we describe a framework to generate random texture movies with controlled information content, i.e., Motion Clouds. These stimuli are defined using a generative model that is based on controlled experimental parametrization. We show that Motion Clouds correspond to dense mixing of localized moving gratings with random positions. Their global envelope is similar to natural-like stimulation with an approximate full-field translation corresponding to a retinal slip. We describe the construction of these stimuli mathematically and propose an open-source Python-based implementation. Examples of the use of this framework are shown. We also propose extensions to other modalities such as color vision, touch, and audition.