Abstrakt
In typical Multiple Object Tracking (MOT) paradigm, the participant's task is to track targets amongst distractors for several seconds. Understanding gaze strategies in MOT can help us reveal attentional mechanisms in dynamic tasks.
Previous attempts relied on analytical strategies (such as averaging object positions). An alternative approach is to find this relationship using machine learning technique.
After preprocessing, we assembled a dataset with 48,000 datapoints, representing 1534 MOT trials or 2.5 hours. In this study, we used feedforward neural networks to predict gaze position and compared predicted gaze with analytical strategies from previous studies using median distance.
Our results showed that neural networks were able to predict eye positions better than current strategies. Particularly, they performed better when we trained the network with all objects, not targets only.
It supports the hypothesis that people are influenced by distractor positions during tracking.