Predicting the Human Reaction Time Based On Natural Image Statistics in a Rapid Categorization Task

The human visual system is developed by viewing natural scenes. In controlled experiments, natural stimuli therefore provide a realistic framework with which to study the underlying information processing steps involved in human vision. Studying the properties of natural images and their effects on the visual processing can help us to understand underlying mechanisms of visual system. In this study, we used a rapid animal vs. non-animal categorization task to assess the relationship between the reaction times of human subjects and the statistical properties of images. We demonstrated that statistical measures, such as the beta and gamma parameters of a Weibull, fitted to the edge histogram of an image, and the image entropy, are effective predictors of subject reaction times. Using these three parameters, we proposed a computational model capable of predicting the reaction times of human subjects. ⺠We propose a model that predicts human reaction times in response to natural images. ⺠We probe the role of statistical properties of images in predicting of reaction times. ⺠Our model is designed based on these statistical properties of natural images. ⺠We found a strong correlation between the subjects’ reaction times and the parameters. ⺠Our results show that the perception latency depends on Weibull β and γ parameters.