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Updated: Jul 9, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
Published on: December 15, 2023
The contrasting impact of global and local object attributes on Kanizsa figure detection
Markus Conci1, Hermann J Müller, Mark A Elliott
1Department Psychologie, Ludwig-Maximilians-Universität München, Munich, Germany. conci@psy.uni-muenchen.de
Abstract:
Studies on the involvement of object completions in search for illusory figures have so far reported equivocal results. We have addressed this issue by investigating at which level object attributes in Kanizsa figures influence search. Employing a paradigm that investigated global and local attributes in the composition of distractors with relation to target composition, we report a selective involvement of multilevel processing upon detection. Four experiments demonstrate that global surface information, but not the surrounding global contour, determines the speed of Kanizsa figure detection. By contrast, local inducer information is encoded far less efficiently in search than processes computing the global object. Our conclusions are that surface filling-in acts as a major determinant of search, but depends on the relevance of the particular hierarchical level (local or global) coding the target.
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