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Rende Shui, Shixian Yu, Ying Zhou, Mowei Shen, Peng Li, Zaifeng Gao; To OBE or Not To OBE? Revisiting Object-based Encoding (OBE) in in Visual Working Memory. Journal of Vision 2016;16(12):357. doi: 10.1167/16.12.357.
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© ARVO (1962-2015); The Authors (2016-present)
It has been suggested that visual working memory (VWM) adopts an object-based encoding (OBE) manner to extract perceptual information into VWM. That is, whenever even one feature-dimension is selected for entry into VWM, the others are also extracted automatically. Almost all extant studies revealing OBE were conducted by probing an "irrelevant-change distracting effect", in which a change of stored irrelevant-feature dramatically affects the change detection performance of the target feature. However, the existence of irrelevant feature change may affect the participants' processing manner, leading to a false positive result. In the current study, we conducted a strict examination of OBE in VWM, by keeping the irrelevant feature of the memory item constant while probing whether task-irrelevant feature can guide the early deployment of attention in visual search. In particular, we required the participants to memorize an object's color while ignoring shape. Critically, we inserted a visual search task into the maintenance phase of VWM, and the participants searched for a target line among distractor lines, each embedded within a different object. One object in the search display could match the shape, the color or both dimensions of the memory item, but this object never contained the target line. Relative to a neutral baseline (no match between the memory and the search displays), we found that the search time was significantly prolonged in all the three match conditions. Moreover, this pattern was not modulated by the exposure time of memory array (100 or 1000ms), suggesting that similar to the task-relevant feature color, the task-irrelevant shape was also extracted into VWM, and hence affected the search task in a top-down manner. Therefore, the OBE exists in VWM.
Meeting abstract presented at VSS 2016
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