Abstract
The development of robust frameworks to understand how the human brain represents conscious and unconscious perceptual contents is paramount to make progress in the neuroscience of consciousness. Recent functional MRI studies using multi-voxel pattern classification analyses showed that unconscious contents could be decoded from brain activity patterns. However, decoding does not imply a full understanding of neural representations. Here we re-analysed data from a high-precision fMRI study coupled with representational similarity analysis based on convolutional neural network models to provide a detailed information-based approach to neural representations of both unconscious and conscious perceptual content. The results showed that computer vision model representations strongly predicted brain responses in ventral visual cortex and in fronto-parietal regions to both conscious and unconscious contents. Moreover, this pattern of results generalised when the models were trained and tested with different participants. Remarkably, these observations results held even when the analysis was restricted to observers that showed null perceptual sensitivity. In light of the highly distributed brain representation of unconscious information, we suggest that the functional role of fronto-parietal cortex in conscious perception is unlikely to be related to the broadcasting of information, as proposed by the global neuronal workspace theory, and may instead relate to the generation of meta-representations as proposed by higher-order theories.