Gaze Preference Decision Making Predictor Using RNN Classifier

This paper presents a gaze preference decision making predictor using a recurrent neural network (RNN) classifier from human eye movement when someone needs to choose one of two targets. When the two target objects are displayed side by side on a screen of a VR head mount display (HMD), this predictor predicts his/her final decision in real time by inputting data of the eye movement to the RNN classifier. It is well known that a chosen likelihood increases rapidly approximately 0.5s in advance from when the participants made decision. This result is based on a statistic analysis. In other words, this is not prediction but postdiction. This phenomenon is called the gaze cascade effect. This effect means that people has a positive feedback loop, i.e., the more someone looks at something he/she likes better by choosing one from two candidates according to left or right eye movement, the better he/she likes it. This paper aims at achieving not to postdict but to predict someone's decision making in real time. In the experiments, we used Blu-ray packages as a target of the gaze preference decision making. First, two packages were chosen randomly from the 78 Blu-ray ones, and were displayed on the VR HMD side by side. 220 sets of eye movement data were measured from 11 participants by an eye-tracking device equipped on the VR HMD. They were recorded with the participants' final decision of left or right for training the RNN classifier. The 11 participants were instructed to choose one package they like better. Each participant repeated this trial 20 times. With respect to the experimental results, the authors classified all the eye movement data into three categories according to types of movement and data length. Our RNN classifier predicted the participants' decision making successfully by approximately 91 percent, when the eye data were longer than 3 seconds and include a switching motion between left and right.