Differential effects of free viewing and goal-directed search on visual attention: An eye-tracking study.

In natural scenes, human gaze behavior is driven jointly by external stimuli from scene characteristics and individual internal goals. To investigate the influence of these two factors on gaze behavior, this paper systematically analyzes eye movement behavior under two task paradigms: free viewing and goal-directed search. First, the guiding effects of different hierarchical visual features on fixation behavior under the two task conditions are analyzed. Second, the differences in eye movement trajectory characteristics between the two task paradigms are compared. Finally, the CNN-TransGaze model is proposed to distinguish eye movement behavior patterns under different task conditions. The results show that shape, location and orientation features exhibit strong fixation attraction under both task paradigms, while semantic features are significantly enhanced in goal-directed search compared with free viewing. There are significant differences in eye movement behavior characteristics between the two task scenarios. Moreover, compared with traditional statistical feature extraction methods, deep learning models can capture latent high-dimensional features more effectively, thereby enabling accurate discrimination of distinct eye movement patterns. Efficient feature extraction from diverse eye movement patterns using deep learning enables the implementation of relevant applications, such as distinguishing healthy individuals from patients with abnormal eye movement patterns caused by neurodegenerative diseases.