Satoshi Nishida

Art expertise reduces influence of visual salience on fixation in viewing abstract-paintings.

When viewing a painting, artists perceive more information from the painting on the basis of their experience and knowledge than art novices do. This difference can be reflected in eye scan paths during viewing of paintings. Distributions of scan paths of artists are different from those of novices even when the paintings contain no figurative object (i.e. abstract paintings). There are two possible explanations for this difference of scan paths. One is that artists have high sensitivity to high-level features such as textures and composition of colors and therefore their fixations are more driven by such features compared with novices. The other is that fixations of artists are more attracted by salient features than those of novices and the fixations are driven by low-level features. To test these, we measured eye fixations of artists and novices during the free viewing of various abstract paintings and compared the distribution of their fixations for each painting with a topological attentional map that quantifies the conspicuity of low-level features in the painting (i.e. saliency map). We found that the fixation distribution of artists was more distinguishable from the saliency map than that of novices. This difference indicates that fixations of artists are less driven by low-level features than those of novices. Our result suggests that artists may extract visual information from paintings based on high-level features. This ability of artists may be associated with artists’ deep aesthetic appreciation of paintings.

Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity

Quantitative analysis of human brain activity based on language representations, such as the semantic categories of words, have been actively studied in the field of brain and neuroscience. Our study aims to generate natural language descriptions for human brain activation phenomena caused by visual stimulus by employing deep learning methods, which have gained interest as an effective approach to automatically describe natural language expressions for various type of multi-modal information, such as images. We employed an image-captioning system based on a deep learning framework as the basis for our method by learning the relationship between the brain activity data and the features of an intermediate expression of the deep neural network owing to lack of training brain data. We conducted three experiments and were able to generate natural language sentences which enabled us to quantitatively interpret brain activity.

Lining Up Brains via a Common Representational Space

Guntupalli, Haxby, and colleagues have proposed a new quantitative way to align whole-brain functional imaging data. The new technique, searchlight hyperalignment, allows transformations of a subjects brain activity into a latent common representational space and vice versa.