It should have appeared as: This work was supported by the National Nature Science Foundation of China, Major International (Regional) Joint Research Project (grant no. 30910103913), National Nature Science Foundation of China (grant no. 81000396) and the National Basic Research Program of China (National 973 project, PD-1/PD-L1 inhibition grant no. 2007CB512203). “
“Early studies by Stratton, 1902 and Stratton, 1906 showed that free exploration of natural scenes is performed through a spatiotemporal
sequence of saccadic eye movements and ocular fixations. This sequence indicates the focus of spatial attention (Biedermann, 1987, Crick and Koch, 1998 and Noton and Stark, 1971a), and is guided by bottom–up and top–down attentional factors. Bottom–up factors are related to low-level features of the objects present in the scene being explored (Itti and Koch, 1999, Itti and Koch, 2001, Koch and Ullman, 1985 and Treisman and Gelade, 1980) while top–down factors depend on the task being executed during exploration of a
scene (Buswell, 1935, Just and Carpenter, 1967 and Yarbus, 1967), the context in which those objects are located (Torralba, et al., 2006), and the behavioral meaning of the objects being observed (Guo et al., 2003 and Guo et al., 2006). For example, traffic lights can attract attention and eye movements both by bottom–up and top–down factors: they are very salient in virtue of their Epacadostat in vitro low-level, intrinsic properties (color and intensity), and also very meaningful to the driver (behavior and context). Several computational models have been proposed to explain guidance of eye movements and attentional shifts during free viewing of natural scenes (e.g., Itti et al., 1998, Milanse et al., 1995, Tsotsos et al., 1995 and Wolfe, 1994). The most common strategy
includes the computation of saliency maps to account for bottom–up factors and defines the regions-of-interest (ROIs) that attract eye movements. The saliency maps are then fed into selleck inhibitor a winner-take-all algorithm to account for the top–down attentional contribution (Itti et al., 1998 and Milanse et al., 1995). During the execution of specific visual search tasks, the nature of the task itself can be used to estimate contextual, task-relevant scene information that will add up to the saliency model (Torralba et al., 2006). However, during free viewing of natural scenes, where no particular task is executed, it is more difficult to estimate the appropriate context. Furthermore, although meaningful objects populate natural scenes, there are currently no computational tools that allow to link behaviorally relevant images and exploration strategies solely based on local or global features. We hypothesize that the spatial clustering of ocular fixations provides a direct indication of the subjective ROIs in a natural scene during free viewing conditions.