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Johannes Burge, Assistant Professor in the Department of Psychology at the University of Pennsylvania, and Wilson Geisler, Professor and Director of the Center for Perceptual Systems at UT Austin, have analyzed all of the steps involved in estimating how fast an object is moving in order to build an "ideal observer,” an optimal visual model. They then tested this ideal observer model against people’s performance in a speed-estimation experiment; in this way, the neural mechanisms associated with speed estimation could be very precisely understood and predicted. Most previous studies of this aspect of visual processing used only artificial images; by employing small patches of natural images, the researchers’ model is more generally applicable to how speed estimation is accomplished in natural conditions in the real world. The research was published in Nature Communications.

Click here to read the full text of this article: Penn Research Helps Develop Predictive Model of How Humans Estimate Speed