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WIREs Cogn Sci
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Visual working memory

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Abstract Visual working memory (VWM), the system of storing, manipulating, and utilizing, visual information is fundamental to many cognitive acts. Exploring the limitations of this system is essential to understand the characteristics of higher‐order cognition, since at a basic level, VWM is the interface through which we interact with our environment. Given its important function, this system has become a very active area of research in the recent years. Here, we examine current models of VWM, along with the proposed reasons for what limits its capacity. This is followed by a short description of recent neural findings that have helped constrain models of VWM. In closing, we focus on work exploring individual differences in working memory capacity, and what these findings reveal about the intimate relationship between VWM and attention. WIREs Cogn Sci 2013, 4:179–190. doi: 10.1002/wcs.1219 This article is categorized under: Psychology > Memory

(a) The contralateral delay activity (CDA) as shown by Vogel and Machizawa.47 In this task the contralateral negativity reflects the attended hemifield for a bilateral array of items. ERPs were time‐locked to the onset of the memory array; posterior electrode sites ipsilateral and contralateral to the attended locations were averaged together. (b) Mean amplitude of the CDA as a function of memory array size. Note that amplitude plateaus at three items. (c) Correlation between an individual's memory capacity and their CDA asymptote.

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(a) Recall procedure used by Zhang and Luck.12 Participants were shown a memory array, and following a 900 ms retention period, reported the color of a probed item by clicking on a color wheel. (b) Predicted results of Zhang and Luck's mixture model. The model combines a Gaussian distribution of responses for stored items (dashed blue line), with a uniform distribution of responses for items that were not stored (dashed red line). On average, performance will reflect responses to both stored and not stored items, resulting in a mixture of the two distributions (solid black line). (c) Results for set size 3 and 6; though the probability of reporting a color that was not stored increased with set size (reflected by difference between the tail ends of the distributions), the standard deviation of the distributions did not, indicating no change in resolution.

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(a) Change detection task used by Luck and Vogel.9 Participants were shown a memory array for 100 ms, followed by a 900 ms retention period, and test array. The test array was either identical to the original memory array, or differed by one item. (b) Average accuracy as a function of set size. Performance was very high for set sizes 1–3, and began to systematically decrease as the set size increased beyond three items.

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(a) Filtering task used by Vogel et al.55 Each trial began with an arrow cue indicating which side of the memory array was to be attended and compared to a test array. The memory array could contain two or four relevant items (e.g., two or four red items), or two relevant and two irrelevant items (e.g., two red and two blue, as shown). (b) CDA time‐locked to the memory array, split between high‐ and low‐capacity individuals. For high‐capacity individuals, the amplitude of the CDA for displays containing distractors resembled that for displays containing only two relevant items, while for low‐capacity individuals, activity was similar to that of four relevant items. (c) Correlation of filtering efficiency (as measured by the ability to exclude irrelevant items) and each individual's memory capacity.

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