Spatial attention in early vision for the perception of border ownership

Abstract

Spatial attention alters contrast gain in early visual areas, which might affect the determination of border ownership (BO) that indicates the direction of figure with respect to the border. We investigated the role of spatial attention applied to early vision in the determination of BO with a computational model that consists of V1, V2, and posterior parietal (PP) modules. Attention alters contrast gain in the V1 module so that it enhances local contrast. The V2 module determines BO based on the surrounding contrast extracted by the V1 module. The simulation results showed that the attention significantly modulates BO; BO is even flipped in figures with ambiguous BO while BO is stable for unambiguous figures such as a simple square. To evaluate the model quantitatively, we carried out psychophysical experiments to measure the effects of attention in the perception of BO and compared the results with those from corresponding simulations. The model showed good agreement with human perception including the determination of BO for ambiguous random-block stimuli. These results indicate that the activity of BO-selective neurons could be modulated significantly by spatial attention that alters local contrast gain in V1, which may account in part for automatic, bi-stable perception in ambiguous figures.

Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

“…Although this model was rather abstract in that the responses of BO-selective model cells were determined solely by the balance of early-level features, it successfully reproduced the characteristics of BO-selective cells and was supported by psychophysical experiments (Sugihara et al, 2007; Sakai et al, 2012). Based on this mechanism for determining the BO, we proposed a model of attention for the modulation of BO-selective cells (Wagatsuma et al, 2008, 2013a). The responses of these models showed good agreement with human perception for the modulation magnitude.…”

“…Our current model consists of three modules, i.e., V1, V2, and PP modules, as illustrated in Figure 1A (also see Wagatsuma et al, 2008). In our model, weak modulatory feedback representing the responses of the PP module and mediating the spatial attention increase contrast gain in V1, which then modulates the responses of V2 that determine BO from the surrounding suppression/facilitation based on low-level features (Figure 1B).…”

“…Although neural mechanisms underlying the BO selectivity are currently under investigation (Qiu et al, 2007; Dong et al, 2008; Martin and von der Heydt, 2015), computational studies have provided insightful evidence from a number of aspects (e.g., Zhaoping, 2005; Supér et al, 2010). Specifically, spatial characteristics of BO selectivity have been studied extensively from a variety of aspects, including contour groupings, attentional modulation, DOF discrimination, and the representations of shape (Oh and Choe, 2007; Wagatsuma et al, 2008; Mihalas et al, 2011; Grossberg, 2016). …”

“…This mechanism was extended to deal with the algorithms for representing the medial axis that was the precursor of the perception of 2D shape and 3D objects (Hatori and Sakai, 2014; Qiu et al, 2015). Our previous study on a network model based on surround modulation showed that attention applied to early visual processing underlies the modulation of the activities of BO-selective cells which evokes the alternation of figural objects in ambiguous images (Wagatsuma et al, 2008, 2013a). The model showed good agreements with human perception in the attentional modulation of the response magnitude.…”

“…In the present study, we investigated the roles of the integration of feedforward signals via surrounding suppression/facilitation and the cortical interactions between early and parietal visual areas for the time-course of the responses of BO-selective cells in the extrastriate cortex through simulation of a computational model that comprised of V1, V2, and posterior parietal (PP) modules (Wagatsuma et al, 2008). In this model, the PP module represented the dorsal pathway and was designed to represent an object's location based on the luminance contrast.…”