Publication details for Dr Anna GrubertGrubert, A. & Eimer, M. (2015). Does visual working memory represent the predicted locations of future target objects? An event-related brain potential study. Brain Research 1626: 258-266.
- Publication type: Journal Article
- ISSN/ISBN: 0006-8993 (print)
- DOI: 10.1016/j.brainres.2014.10.011
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
During the maintenance of task-relevant objects in visual working memory, the contralateral delay activity (CDA) is elicited over the hemisphere opposite to the visual field where these objects are presented. The presence of this lateralised CDA component demonstrates the existence of position-dependent object representations in working memory. We employed a change detection task to investigate whether the represented object locations in visual working memory are shifted in preparation for the known location of upcoming comparison stimuli. On each trial, bilateral memory displays were followed after a delay period by bilateral test displays. Participants had to encode and maintain three visual objects on one side of the memory display, and to judge whether they were identical or different to three objects in the test display. Task-relevant memory and test stimuli were located in the same visual hemifield in the no-shift task, and on opposite sides in the horizontal shift task. CDA components of similar size were triggered contralateral to the memorized objects in both tasks. The absence of a polarity reversal of the CDA in the horizontal shift task demonstrated that there was no preparatory shift of memorized object location towards the side of the upcoming comparison stimuli. These results suggest that visual working memory represents the locations of visual objects during encoding, and that the matching of memorized and test objects at different locations is based on a comparison process that can bridge spatial translations between these objects. This article is part of a Special Issue entitled SI: Prediction and Attention.