Attention and Biased Competition in Multi-voxel Object Representations

Abstract

The biased-competition theory accounts for attentional effects at the single-neuron level: It predicts that the neuronal response to simultaneously-presented stimuli is a weighted average of the response to isolated stimuli, and that attention biases the weights in favor of the attended stimulus. Perception, however, relies not on single neurons but on larger neuronal populations. The responses of such populations are in part reflected in large-scale multivoxel fMRI activation patterns. Because the pooling of neuronal responses into blood-oxygen-level-dependent signals is nonlinear, fMRI effects of attention need not mirror those observed at the neuronal level. Thus, to bridge the gap between neuronal responses and human perception, it is fundamental to understand attentional influences in large-scale multivariate representations of simultaneously-presented objects. Here, we ask how responses to simultaneous stimuli are combined in multivoxel fMRI patterns, and how attention affects the paired response. Objects from four categories were presented singly, or in pairs such that each category was attended, unattended, or attention was divided between the two. In a multidimensional voxel space, the response to simultaneously-presented categories was well described as a weighted average. The weights were biased toward the preferred category in category-selective regions. Consistent with single-unit reports, attention shifted the weights by ≈30% in favor of the attended stimulus. These findings extend the biased-competition framework to the realm of large-scale multivoxel brain activations.