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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...

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Detalles Bibliográficos
Autores principales: Hall, Sophie, Bourke, Patrick, Guo, Kun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059619/
https://www.ncbi.nlm.nih.gov/pubmed/24932494
http://dx.doi.org/10.1371/journal.pone.0098268
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author Hall, Sophie
Bourke, Patrick
Guo, Kun
author_facet Hall, Sophie
Bourke, Patrick
Guo, Kun
author_sort Hall, Sophie
collection PubMed
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.
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spelling pubmed-40596192014-06-19 Low Level Constraints on Dynamic Contour Path Integration Hall, Sophie Bourke, Patrick Guo, Kun PLoS One Research Article 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. Public Library of Science 2014-06-16 /pmc/articles/PMC4059619/ /pubmed/24932494 http://dx.doi.org/10.1371/journal.pone.0098268 Text en © 2014 Hall et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Hall, Sophie
Bourke, Patrick
Guo, Kun
Low Level Constraints on Dynamic Contour Path Integration
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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059619/
https://www.ncbi.nlm.nih.gov/pubmed/24932494
http://dx.doi.org/10.1371/journal.pone.0098268
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