Abstract
I present evidence supporting key predictions of the Reynolds-Heeger Normalization Model of Attention: 1) Both exogenous and endogenous covert spatial attention, can affect performance by contrast or response gain changes, depending on the stimulus size and the relative size of the attention field. 2) Feature-based attention improves performance, consistent with a change in response gain, when the featural extent of the attention field is small (low uncertainty) or large (high uncertainty) relative to the featural extent of the stimulus. 3) An expanded model of spatial resolution and attention that distinguishes between exogenous and endogenous attention, focusing on texture-based segmentation as a model system, which has revealed a dissociation between these types of attention across eccentricity. Our model emulates sensory encoding to segment figures from their background and predict performance. To explain attentional effects, exogenous and endogenous attention require separate operating regimes across spatial frequency. The model reproduces behavioral performance across several experiments and resolves three unexplained phenomena: 1) the parafoveal advantage in segmentation, 2) the uniform improvements across eccentricity by endogenous attention, and 3) the peripheral improvements and central impairments by exogenous attention. This model provides a generalizable framework for predicting effects of endogenous and exogenous attention on perception across the visual field.
Funding: Funding: NIH R01-EY019693