Search

Research investigating gaze in natural scenes has identified a number of spatial biases in where people look, but it is unclear whether these are partly due to constrained testing environments (e.g., a participant with their head restrained and looking at a landscape image framed within a computer monitor). We examined the extent to which image shape (square vs. circle), image rotation, and image content (landscapes vs. fractal images) influence eye and head movements in virtual reality (VR). Both the eyes and head were tracked while observers looked at natural scenes in a virtual environment. In line with previous work, we found a bias for saccade directions parallel to the image horizon, regardless of image shape or content. We found that, when allowed to do so, observers move both their eyes and head to explore images. Head rotation, however, was idiosyncratic; some observers rotated a lot, whereas others did not. Interestingly, the head rotated in line with the rotation of landscape but not fractal images. That head rotation and gaze direction respond differently to image content suggests that they may be under different control systems. We discuss our findings in relation to current theories on head and eye movement control and how insights from VR might inform more traditional eye-tracking studies.

Saliency map models account for a small but significant amount of the variance in where people fixate, but evaluating these models with natural stimuli has led to mixed results. In the present study, the eye movements of participants were recorded while they viewed color photographs of natural scenes in preparation for a memory test (encoding) and when recognizing them later. These eye movements were then compared to the predictions of a well defined saliency map model (L. Itti & C. Koch, 2000), in terms of both individual fixation locations and fixation sequences (scanpaths). The saliency model is a significantly better predictor of fixation location than random models that take into account bias toward central fixations, and this is the case at both encoding and recognition. However, similarity between scanpaths made at multiple viewings of the same stimulus suggests that repetitive scanpaths also contribute to where people look. Top-down recapitulation of scanpaths is a key prediction of scanpath theory (D. Noton & L. Stark, 1971), but it might also be explained by bottom-up guidance. The present data suggest that saliency cannot account for scanpaths and that incorporating these sequences could improve model predictions.