Journal
COGNITION
Volume 159, Issue -, Pages 85-96Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cognition.2016.11.009
Keywords
Change detection; Visual working memory; Global precedence; Object structure; Ensemble representation
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Funding
- German Research Foundation (DFG) [CO 1002/1-1]
- LMUexcellent Junior Researcher Fund
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When remembering a natural scene, both detailed information about specific objects and summary representations such as the gist of a scene are encoded. However, formal models of change detection that are used to estimate working memory capacity, typically assume observers simply encode and maintain memory representations that are treated independently from one another without considering the (hierarchical) object or scene structure. To overcome this limitation, we present a hierarchical variant of the change detection task that attempts to formalize the role of object structure, thus, allowing for richer, more graded memory representations. We demonstrate that detection of a global-object change precedes local-object changes of hierarchical shapes to a large extent. Moreover, when systematically varying object repetitions between individual items at a global or a local level, memory performance declines mainly for repeated global objects, but not for repeated local objects, which suggests that ensemble (i.e., summary) representations are likewise biased toward a global level. In addition, this global memory precedence effect is shown to be independent from encoding durations, and mostly cannot be attributed to differences in saliency or shape discriminability at global/local object levels. This pattern of results is suggestive of a global/local difference occurring primarily during memory maintenance. Altogether, these findings challenge visual-working-memory (vWM) models that propose that a fixed number of objects can be remembered regardless of the individual object structure. Instead, our results support a hierarchical model that emphasizes the role for structured representations among objects in vWM. (C) 2016 Elsevier B.V. All rights reserved.
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