Rapid contextualization of fragmented scene information in the human visual system
Real-world environments are extremely rich in visual information. At any given moment in time, only a fraction of this information is available to the eyes and the brain, rendering naturalistic vision a collection of incomplete snapshots. Previous research suggests that in order to successfully cont...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920305310 |
| _version_ | 1818511122858246144 |
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| author | Daniel Kaiser Gabriele Inciuraite Radoslaw M. Cichy |
| author_facet | Daniel Kaiser Gabriele Inciuraite Radoslaw M. Cichy |
| author_sort | Daniel Kaiser |
| collection | DOAJ |
| description | Real-world environments are extremely rich in visual information. At any given moment in time, only a fraction of this information is available to the eyes and the brain, rendering naturalistic vision a collection of incomplete snapshots. Previous research suggests that in order to successfully contextualize this fragmented information, the visual system sorts inputs according to spatial schemata, that is knowledge about the typical composition of the visual world. Here, we used a large set of 840 different natural scene fragments to investigate whether this sorting mechanism can operate across the diverse visual environments encountered during real-world vision. We recorded brain activity using electroencephalography (EEG) while participants viewed incomplete scene fragments at fixation. Using representational similarity analysis on the EEG data, we tracked the fragments’ cortical representations across time. We found that the fragments’ typical vertical location within the environment (top or bottom) predicted their cortical representations, indexing a sorting of information according to spatial schemata. The fragments’ cortical representations were most strongly organized by their vertical location at around 200 ms after image onset, suggesting rapid perceptual sorting of information according to spatial schemata. In control analyses, we show that this sorting is flexible with respect to visual features: it is neither explained by commonalities between visually similar indoor and outdoor scenes, nor by the feature organization emerging from a deep neural network trained on scene categorization. Demonstrating such a flexible sorting across a wide range of visually diverse scenes suggests a contextualization mechanism suitable for complex and variable real-world environments. |
| first_indexed | 2024-12-10T23:29:11Z |
| format | Article |
| id | doaj.art-b73bb3b1e54f43fd93d1655a7e41f08a |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-10T23:29:11Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-b73bb3b1e54f43fd93d1655a7e41f08a2022-12-22T01:29:28ZengElsevierNeuroImage1095-95722020-10-01219117045Rapid contextualization of fragmented scene information in the human visual systemDaniel Kaiser0Gabriele Inciuraite1Radoslaw M. Cichy2Department of Psychology, University of York, York, UK; Corresponding author. Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.Department of Education and Psychology, Freie Universität Berlin, Berlin, GermanyDepartment of Education and Psychology, Freie Universität Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, GermanyReal-world environments are extremely rich in visual information. At any given moment in time, only a fraction of this information is available to the eyes and the brain, rendering naturalistic vision a collection of incomplete snapshots. Previous research suggests that in order to successfully contextualize this fragmented information, the visual system sorts inputs according to spatial schemata, that is knowledge about the typical composition of the visual world. Here, we used a large set of 840 different natural scene fragments to investigate whether this sorting mechanism can operate across the diverse visual environments encountered during real-world vision. We recorded brain activity using electroencephalography (EEG) while participants viewed incomplete scene fragments at fixation. Using representational similarity analysis on the EEG data, we tracked the fragments’ cortical representations across time. We found that the fragments’ typical vertical location within the environment (top or bottom) predicted their cortical representations, indexing a sorting of information according to spatial schemata. The fragments’ cortical representations were most strongly organized by their vertical location at around 200 ms after image onset, suggesting rapid perceptual sorting of information according to spatial schemata. In control analyses, we show that this sorting is flexible with respect to visual features: it is neither explained by commonalities between visually similar indoor and outdoor scenes, nor by the feature organization emerging from a deep neural network trained on scene categorization. Demonstrating such a flexible sorting across a wide range of visually diverse scenes suggests a contextualization mechanism suitable for complex and variable real-world environments.http://www.sciencedirect.com/science/article/pii/S1053811920305310Visual perceptionScene representationSpatial schemaEEGRepresentational similarity analysisDeep neural networks |
| spellingShingle | Daniel Kaiser Gabriele Inciuraite Radoslaw M. Cichy Rapid contextualization of fragmented scene information in the human visual system NeuroImage Visual perception Scene representation Spatial schema EEG Representational similarity analysis Deep neural networks |
| title | Rapid contextualization of fragmented scene information in the human visual system |
| title_full | Rapid contextualization of fragmented scene information in the human visual system |
| title_fullStr | Rapid contextualization of fragmented scene information in the human visual system |
| title_full_unstemmed | Rapid contextualization of fragmented scene information in the human visual system |
| title_short | Rapid contextualization of fragmented scene information in the human visual system |
| title_sort | rapid contextualization of fragmented scene information in the human visual system |
| topic | Visual perception Scene representation Spatial schema EEG Representational similarity analysis Deep neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920305310 |
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