Continuous Psychophysics: measuring visual sensitivity by dynamic target tracking

Abstract

We introduce a novel framework for estimating visual sensitivity using a continuous target-tracking task in concert with a dynamic internal model of human visual performance. In our main experiment, observers used a mouse cursor to track the center of a 2D Gaussian luminance target as it moved in a Brownian walk in a field of dynamic Gaussian luminance noise. To estimate visual sensitivity, we fit a Kalman filter to the tracking data assuming that humans behave roughly as Bayesian ideal observers. Such observers optimally combine prior information with noisy observations to produce an estimate of target location at each point in time. We found that estimates of human sensory noise obtained from the Kalman filter model were highly correlated with traditional psychophysical measures of human sensitivity (R2 > 0.97). Because data can be collected at the display frame rate, the amount of time required to measure sensitivity is greatly reduced relative to traditional psychophysics. While our modeling framework provides principled estimates of sensitivity that are directly comparable with those from traditional psychophysics, easily-computed summary statistics based on cross-correlograms of the tracking data also accurately predict relative sensitivity, and are thus good empirical substitutes for the more computationally-intensive Kalman filter fitting. As a second example, we show contrast sensitivity functions quickly determined using target tracking. Finally, we show that psychophysical reverse-correlation can also be quickly done via tracking. We conclude that dynamic target tracking is a viable and faster alternative to traditional psychophysical methods in many situations. Meeting abstract presented at VSS 2015