Dynamic Perception Revealed by Cursor Movements and Hidden Markov Modeling

We explore the dynamic coordination of perception, decision, and action underpinning perceptual choices by recording cursor movements during a binary response task. Stimuli were presented sequentially to control the time-course of perception, and we utilized a Hidden Markov Model (HMM) to relate measured movements of the mouse cursor to latent cognitive processes. Stimuli were simple perceptual objects comprised of two features, one of which was fully diagnostic of the correct response, while the other provided a probabilistic cue. The order of their arrival varied across trials, allowing us to manipulate the order of feature processing. The model builds upon response time methods and makes predictions about when individual features were perceived and the accumulation of evidence towards a response, every 10 milliseconds of each trial