title: Feature-based attention in early vision for the modulation of figure-ground segregation.
abstract: We investigated psychophysically whether feature-based attention modulates the perception of figure-ground (F-G) segregation and, based on the results, we investigated computationally the neural mechanisms underlying attention modulation. In the psychophysical experiments, the attention of participants was drawn to a specific motion direction and they were then asked to judge the side of figure in an ambiguous figure with surfaces consisting of distinct motion directions. The results of these experiments showed that the surface consisting of the attended direction of motion was more frequently observed as figure, with a degree comparable to that of spatial attention (Wagatsuma et al., 2008). These experiments also showed that perception was dependent on the distribution of feature contrast, specifically the motion direction differences. These results led us to hypothesize that feature-based attention functions in a framework similar to that of spatial attention. We proposed a V1-V2 model in which feature-based attention modulates the contrast of low-level feature in V1, and this modulation of contrast changes directly the surround modulation of border-ownership-selective cells in V2; thus, perception of F-G is biased. The model exhibited good agreement with human perception in the magnitude of attention modulation and its invariance among stimuli. These results indicate that early-level features that are modified by feature-based attention alter subsequent processing along afferent pathway, and that such modification could even change the perception of object.
authors.affiliation: The Zanvyl-Krieger Mind/Brain Institute, Johns Hopkins University Baltimore, MD, USA ; Riken Brain Science Institute Wako, Japan.
authors.forename: Nobuhiko
authors.initials: N
authors.lastname: Wagatsuma
authors.forename: Megumi
authors.initials: M
authors.lastname: Oki
authors.forename: Ko
authors.initials: K
authors.lastname: Sakai
keywords: border-ownership; computational model; early vision; feature-based attention; figure–ground segregation; psychophysical experiment