Bayesian Microsaccade Detection
Journal of Vision (2017) 17(1):13, 1–23 1 Bayesian microsaccade detection * Center for Neural Science, New York University, Andra Mihali New York, NY, USA $ * Center for Neural Science, New York University, Bas van Opheusden New York, NY, USA $ Center for Neural Science, New York University, New York, NY, USA Department of Psychology, New York University, Wei Ji Ma New York, NY, USA $ Microsaccades are high-velocity fixational eye Introduction movements, with special roles in perception and cognition. The default microsaccade detection method is to determine when the smoothed eye velocity Even when people attempt to fixate, their eyes are exceeds a threshold. We have developed a new always in motion. Fixational eye movements are often method, Bayesian microsaccade detection (BMD), categorized as drift, tremor, and microsaccades (Ciuf- which performs inference based on a simple freda & Tannen, 1995). During most of the fixation statistical model of eye positions. In this model, a time, the eye is in a drift state, which is low-amplitude, hidden state variable changes between drift and low-velocity motion, sometimes modeled as a random microsaccade states at random times. The eye walk (Cornsweet, 1956; Ditchburn & Ginsborg, 1953; position is a biased random walk with different Ratliff & Riggs, 1950). A few times a second, the eye velocity distributions for each state. BMD generates makes a microsaccade, which is a high-velocity samples from the posterior probability distribution movement along a relatively straight trajectory (Corn- overtheeyestatetimeseriesgiventheeyeposition sweet, 1956; Engbert & Kliegl, 2003; Engbert, Mer- timeseries.Appliedtosimulateddata,BMDrecovers genthaler, Sinn, & Pikovsky, 2011; Rolfs, 2009). the ‘‘true’’ microsaccades with fewer errors than Microsaccades have been implicated in several alternative algorithms, especially at high noise.
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