How attention extracts objects from noise

Abstract

The visual system is remarkably proficient at extracting relevant object information from noisy, cluttered environments. Although attention is known to enhance sensory processing, the mechanisms by which attention extracts relevant information from noise are not well understood. According to the perceptual template model, attention may act to amplify responses to all visual input, or it may act as a noise filter, dampening responses to irrelevant visual noise. Amplification allows for improved performance in the absence of visual noise, whereas a noise-filtering mechanism can only improve performance if the target stimulus appears in noise. Here, we used fMRI to investigate how attention modulates cortical responses to objects at multiple levels of the visual pathway. Participants viewed images of faces, houses, chairs, and shoes, presented in various levels of visual noise. We used multivoxel pattern analysis to predict the viewed object category, for attended and unattended stimuli, from cortical activity patterns in individual visual areas. Early visual areas, V1 and V2, exhibited a benefit of attention only at high levels of visual noise, suggesting that attention operates via a noise-filtering mechanism at these early sites. By contrast, attention led to enhanced processing of noise-free images (i.e., amplification) only in higher visual areas, including area V4, fusiform face area, mid-Fusiform area, and the lateral occipital cortex. Together, these results suggest that attention improves people's ability to discriminate objects by de-noising visual input in early visual areas and amplifying this noise-reduced signal at higher stages of visual processing.