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William Ngiam, Edward Awh; Memory compression using statistical regularities requires explicit awareness. Journal of Vision 2017;17(10):855. doi: https://doi.org/10.1167/17.10.855.
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Visual working memory (WM) is a core cognitive ability that predicts broader measures of cognitive ability. Thus, there has been much interest in the factors that can influence WM capacity. Brady, Konkle & Alvarez (2009) argued that statistical regularities may enable a larger number of items to be maintained in this online memory system. In a WM task that required recall of arrays of colors, they included a patterned condition in which specific colors were more likely to appear together. There was a robust improvement in recall performance in the patterned condition relative to one without the regularities, an effect that has been referred to as "memory compression". Brady et al suggested that this effect was a product of visual statistical learning, the ability to apprehend statistical relationships automatically and without reliance on explicit knowledge. This has been an influential finding, but it is inconsistent with multiple other studies that have found no benefit of exact repetitions of sample displays in similar working memory tasks (e.g., Olson and Jiang, 2004; Logie and Brockmole, 2009). What distinguishes the Brady et al. procedure from the others? Here, we offer a possible reconciliation of these findings. We replicated the benefit Brady et al. observed in the patterned condition, but we obtained a larger sample of subjects and included an explicit test of subjects' memory for the repeated pairs. Strikingly, memory compression effects were observed only in the subset of subjects who had perfect explicit recall of the color pairing at the end of the study. The remaining subjects showed no advantage in the patterned condition. These findings argue against the hypothesis that statistical regularities elicit automatic "compression" of multiple items in visual WM. Instead, the effect may be better understood as an example of paired associate learning.
Meeting abstract presented at VSS 2017
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