Low level constraints on dynamic contour path integration.
Contour integration is a fundamental visual process. The constraints on integrating discrete contour elements and the associated neural mechanisms have typically been investigated using static contour paths. However, in our dynamic natural environment objects and scenes vary over space and time. Wit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4059619?pdf=render |
| _version_ | 1811292423521828864 |
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| author | Sophie Hall Patrick Bourke Kun Guo |
| author_facet | Sophie Hall Patrick Bourke Kun Guo |
| author_sort | Sophie Hall |
| collection | DOAJ |
| description | Contour integration is a fundamental visual process. The constraints on integrating discrete contour elements and the associated neural mechanisms have typically been investigated using static contour paths. However, in our dynamic natural environment objects and scenes vary over space and time. With the aim of investigating the parameters affecting spatiotemporal contour path integration, we measured human contrast detection performance of a briefly presented foveal target embedded in dynamic collinear stimulus sequences (comprising five short 'predictor' bars appearing consecutively towards the fovea, followed by the 'target' bar) in four experiments. The data showed that participants' target detection performance was relatively unchanged when individual contour elements were separated by up to 2° spatial gap or 200 ms temporal gap. Randomising the luminance contrast or colour of the predictors, on the other hand, had similar detrimental effect on grouping dynamic contour path and subsequent target detection performance. Randomising the orientation of the predictors reduced target detection performance greater than introducing misalignment relative to the contour path. The results suggest that the visual system integrates dynamic path elements to bias target detection even when the continuity of path is disrupted in terms of spatial (2°), temporal (200 ms), colour (over 10 colours) and luminance (-25% to 25%) information. We discuss how the findings can be largely reconciled within the functioning of V1 horizontal connections. |
| first_indexed | 2024-04-13T04:46:14Z |
| format | Article |
| id | doaj.art-96478b52777c4a88b8131aba894e036e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T04:46:14Z |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-96478b52777c4a88b8131aba894e036e2022-12-22T03:01:51ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0196e9826810.1371/journal.pone.0098268Low level constraints on dynamic contour path integration.Sophie HallPatrick BourkeKun GuoContour integration is a fundamental visual process. The constraints on integrating discrete contour elements and the associated neural mechanisms have typically been investigated using static contour paths. However, in our dynamic natural environment objects and scenes vary over space and time. With the aim of investigating the parameters affecting spatiotemporal contour path integration, we measured human contrast detection performance of a briefly presented foveal target embedded in dynamic collinear stimulus sequences (comprising five short 'predictor' bars appearing consecutively towards the fovea, followed by the 'target' bar) in four experiments. The data showed that participants' target detection performance was relatively unchanged when individual contour elements were separated by up to 2° spatial gap or 200 ms temporal gap. Randomising the luminance contrast or colour of the predictors, on the other hand, had similar detrimental effect on grouping dynamic contour path and subsequent target detection performance. Randomising the orientation of the predictors reduced target detection performance greater than introducing misalignment relative to the contour path. The results suggest that the visual system integrates dynamic path elements to bias target detection even when the continuity of path is disrupted in terms of spatial (2°), temporal (200 ms), colour (over 10 colours) and luminance (-25% to 25%) information. We discuss how the findings can be largely reconciled within the functioning of V1 horizontal connections.http://europepmc.org/articles/PMC4059619?pdf=render |
| spellingShingle | Sophie Hall Patrick Bourke Kun Guo Low level constraints on dynamic contour path integration. PLoS ONE |
| title | Low level constraints on dynamic contour path integration. |
| title_full | Low level constraints on dynamic contour path integration. |
| title_fullStr | Low level constraints on dynamic contour path integration. |
| title_full_unstemmed | Low level constraints on dynamic contour path integration. |
| title_short | Low level constraints on dynamic contour path integration. |
| title_sort | low level constraints on dynamic contour path integration |
| url | http://europepmc.org/articles/PMC4059619?pdf=render |
| work_keys_str_mv | AT sophiehall lowlevelconstraintsondynamiccontourpathintegration AT patrickbourke lowlevelconstraintsondynamiccontourpathintegration AT kunguo lowlevelconstraintsondynamiccontourpathintegration |