Predicting bias in perceived position using attention field models

Abstract

Attention is the mechanism through which we select relevant information from our visual environment. We have recently demonstrated that attention attracts receptive fields across the visual hierarchy (Klein, Harvey, & Dumoulin, 2014). We captured this receptive field attraction using an attention field model. Here, we apply this model to human perception: We predict that receptive field attraction results in a bias in perceived position, which depends on the size of the underlying receptive fields. We instructed participants to compare the relative position of Gabor stimuli, while we manipulated the focus of attention using exogenous cueing. We varied the eccentric position and spatial frequency of the Gabor stimuli to vary underlying receptive field size. The positional biases as a function of eccentricity matched the predictions by an attention field model, whereas the bias as a function of spatial frequency did not. As spatial frequency and eccentricity are encoded differently across the visual hierarchy, we speculate that they might interact differently with the attention field that is spatially defined.