Reading Your Mind Through Your Eyes: Using Eye Scan Patterns and Machine Learning to Predict Number Choice
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Eye tracking technology measures eye movements in real time. Studies have shown that eye movement patterns can express spatial cognitive thoughts, confirming the existence of the eye-cognition link. The purpose of this experiment is to determine a correlation between spatial cognition and number processing. If a correlation exists, features suggesting this correlation will be identified to predict a subject’s response using machine learning. Fifty subjects were asked to look at a screen and respond to the following auditory prompt: “Think of a number x/y/z and say it out loud”. These intervals were named Number Line, Pre-prompt, Mid-prompt, and Post-prompt. A predicted model was created by training a Random Forest (RF) Algorithm. Results indicate a strong correlation between saccade vector and participants’ response, suggesting subjects made saccades toward the direction of response in mind. A strong correlation was also identified between Fixation X and participants’ response as these fixations were made in relative locations of the given numbers on a number line. Test accuracy of 90.5% for Trial 1 was obtained with 50 trees. All ten trials resulted in a mean accuracy of 89%. Our future plans involve determining the existence of a vertical component of spatial cognition.