Attention modulates neural representation to render reconstructions according to subjective appearance.
Tomoyasu HorikawaYukiyasu KamitaniPublished in: Communications biology (2022)
Stimulus images can be reconstructed from visual cortical activity. However, our perception of stimuli is shaped by both stimulus-induced and top-down processes, and it is unclear whether and how reconstructions reflect top-down aspects of perception. Here, we investigate the effect of attention on reconstructions using fMRI activity measured while subjects attend to one of two superimposed images. A state-of-the-art method is used for image reconstruction, in which brain activity is translated (decoded) to deep neural network (DNN) features of hierarchical layers then to an image. Reconstructions resemble the attended rather than unattended images. They can be modeled by superimposed images with biased contrasts, comparable to the appearance during attention. Attentional modulations are found in a broad range of hierarchical visual representations and mirror the brain-DNN correspondence. Our results demonstrate that top-down attention counters stimulus-induced responses, modulating neural representations to render reconstructions in accordance with subjective appearance.
Keyphrases
- working memory
- deep learning
- convolutional neural network
- neural network
- image quality
- optical coherence tomography
- resting state
- high glucose
- diabetic rats
- functional connectivity
- machine learning
- computed tomography
- sleep quality
- signaling pathway
- oxidative stress
- depressive symptoms
- white matter
- endothelial cells
- multiple sclerosis