A data driven approach to understanding the organization of high-level visual cortex
Abstract The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2017-06-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-03974-5 |
| _version_ | 1818431503177089024 |
|---|---|
| author | David M. Watson Timothy J. Andrews Tom Hartley |
| author_facet | David M. Watson Timothy J. Andrews Tom Hartley |
| author_sort | David M. Watson |
| collection | DOAJ |
| description | Abstract The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain. |
| first_indexed | 2024-12-14T15:50:20Z |
| format | Article |
| id | doaj.art-dbb9e6e78ede4a28a10b73030804327c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T15:50:20Z |
| publishDate | 2017-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-dbb9e6e78ede4a28a10b73030804327c2022-12-21T22:55:24ZengNature PortfolioScientific Reports2045-23222017-06-017111410.1038/s41598-017-03974-5A data driven approach to understanding the organization of high-level visual cortexDavid M. Watson0Timothy J. Andrews1Tom Hartley2Department of Psychology and York Neuroimaging Centre, University of YorkDepartment of Psychology and York Neuroimaging Centre, University of YorkDepartment of Psychology and York Neuroimaging Centre, University of YorkAbstract The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain.https://doi.org/10.1038/s41598-017-03974-5 |
| spellingShingle | David M. Watson Timothy J. Andrews Tom Hartley A data driven approach to understanding the organization of high-level visual cortex Scientific Reports |
| title | A data driven approach to understanding the organization of high-level visual cortex |
| title_full | A data driven approach to understanding the organization of high-level visual cortex |
| title_fullStr | A data driven approach to understanding the organization of high-level visual cortex |
| title_full_unstemmed | A data driven approach to understanding the organization of high-level visual cortex |
| title_short | A data driven approach to understanding the organization of high-level visual cortex |
| title_sort | data driven approach to understanding the organization of high level visual cortex |
| url | https://doi.org/10.1038/s41598-017-03974-5 |
| work_keys_str_mv | AT davidmwatson adatadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT timothyjandrews adatadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT tomhartley adatadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT davidmwatson datadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT timothyjandrews datadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex AT tomhartley datadrivenapproachtounderstandingtheorganizationofhighlevelvisualcortex |