Neural Mechanisms of Object-Based Attention

Abstract

What neural mechanisms underlie the ability to attend to a complex object in the presence of competing overlapping stimuli? We evaluated whether object-based attention might involve pattern-specific feedback to early visual areas to selectively enhance the set of low-level features corresponding to the attended object. Using fMRI and multivariate pattern analysis, we found that activity patterns in early visual areas (V1-V4) are strongly biased in favor of the attended object. Activity patterns evoked by single faces and single houses reliably predicted which of the 2 overlapping stimulus types was being attended with high accuracy (80-90% correct). Superior knowledge of upright objects led to improved attentional selection in early areas. Across individual blocks, the strength of the attentional bias signal in early visual areas was highly predictive of the modulations found in high-level object areas, implying that pattern-specific attentional filtering at early sites can determine the quality of object-specific signals that reach higher level visual areas. Through computational modeling, we show how feedback of an average template to V1-like units can improve discrimination of exemplars belonging to the attended category. Our findings provide a mechanistic account of how feedback to early visual areas can contribute to the attentional selection of complex objects.