We rely on our ability to pay attention in order to perform several day-to-day tasks ranging from finding a pair of socks to navigating traffic. This is because attention allows us to selectively process only relevant information from what could often be a cluttered environment. The selection of relevant information can be based on the spatial locations of objects (location-based attention) (Pestilli & Carrasco,
2005; Posner, Snyder, & Davidson,
1980; Theeuwes & Van der Burg,
2007) as well as on other attributes such as color or direction of motion (feature-based attention) (Saenz, Buracas, & Boynton,
2002; White & Carrasco,
2011). It has been shown that both these types of attentive selection modulate neural activity in cortical areas belonging to two interconnected but distinct pathways (Kravitz, Saleem, Baker, & Mishkin,
2011; Ungerleider & Mishkin,
1982). Location-based attention is mediated primarily by the dorsal pathway, which projects from the primary visual cortex (V1) and extends into the parietal lobe (Bisley,
2011; Bisley & Goldberg,
2003; Saalmann, Pigarev, & Vidyasagar,
2007). Feature-based attention is subserved mostly by the ventral pathway, which connects V1 to the temporal lobe (Chelazzi, Miller, Duncan, & Desimone,
1993; Motter,
1994). However, the pattern of interaction between these two pathways, which in turn determines the mechanism by which attention operates, is still a matter of debate. One idea that has been proposed is that the dorsal pathway uses its spatial saliency map to spotlight a location and, via top-down feedback channels, gates what is subsequently processed by the ventral stream (Bullier,
2001; Vidyasagar,
1999). Such a mechanism can potentially also solve the binding problem, since the features that are associated with a single object would be bound together by temporal coincidence within the spotlight, despite being processed in different cortical areas. This raises the possibility that location-based attention may be more effective in aiding performance for a given task than feature-based attention.