Gaze-based human intention prediction in the hybrid foraging search task

Abstract

An agent’s ability to predict a human’s intention can facilitate the effectiveness of a human-agent team. The aim of this study was to explore the feasibility of predicting human intention in the hybrid foraging search task using eye gaze data and machine learning. In the hybrid foraging search paradigm, the human performs searches in patches and decides to stay and exploit a patch or to leave and explore another patch. Researchers have modeled the “patch-leaving problem” on the collective level using the marginal value theorem. However, few have attempted to predict the exact moment when the human will leave. In the current study, 40 participants performed the hybrid foraging search task while eye gaze data were collected with an eye-tracker. The leaving intention was associated with larger pupil size, shorter average fixation duration, larger number of fixations, longer saccade amplitude, and faster saccade velocity compared to the searching intention. A cross-subject machine learning model with an artificial neural network algorithm was able to predict whether a participant would leave the current patch with up to 78% accuracy with a 2-second data analysis window. The length of the data analysis window did not significantly affect prediction accuracy. Furthermore, the earlier the behavior prediction was made, the lower the reliability of the prediction. These results demonstrate that eye gaze features and machine learning algorithms are useful in predicting human intention in visual search tasks.