Project Details


Computational vision and vision science have traditionally looked to the statistics of the natural world and each other for insights into visual processing. Until recently, these approaches have been primarily static and correlational: the natural world has been treated as a collection of images for which processing should be optimized, and the averaged regularities in natural scenes have been shown to be correlated with perceptual biases. Any dynamic adjustment to recent experience influencing perception has often been minimized, in large part because there have not been ways to disrupt the environment and test the effects. But recent advances in computing and virtual reality hardware have made possible the manipulation of visual input in near-real time.This research combines mobile computing technology with immersive augmented reality to explore how visual perception dynamically adapts to encountered regularities in the environment. The PI will investigate perception of orientation, a feature of the first cortical layer of human visual processing, and thus a logical starting point. If stimuli are encoded under a framework that uses recent environmental statistics to dynamically optimize perception, then altering the typical environmental regularities should have predictable effects on human visual performance. The PI argues that existing computational models of human perception can be extended to predict which changes in the input will improve (or inhibit) human perceptual performance. This, in turn, will open up the possibility of training human perception to optimize performance on real world tasks that previously required extensive specialized training or costly, custom-built software. With the goal of creating a more precise model of the flexibility of the human visual system by quantifying the extent to which encoding biases can be altered or obliterated, this project will include three interrelated thrusts. First, the PI will develop a suite of software tools to process the visual environment in near real-time, and will use these tools to systematically investigate changes in human perception in response to experience with environments whose statistical content is atypical. He will measure changes in human perceptual performance on a variety of real-world tasks (e.g., object detection), in response to immersive experience with atypical environmental input. And he will develop and test a computational model of this human perceptual learning. Preliminary research suggests that the combination of computer image-filtering and virtual reality hardware can be used to change subsequent visual processing in ways that are predictable based on the filtered input.
Effective start/end date12/1/1511/30/18


  • National Science Foundation (National Science Foundation (NSF))


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