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David E. Anderson, Edward Awh; Variations in mnemonic resolution across set sizes support discrete resource models of capacity in working memory. Journal of Vision 2010;10(7):731. doi: 10.1167/10.7.731.
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© ARVO (1962-2015); The Authors (2016-present)
Discrete resource models propose that WM capacity is determined by a small number of discrete “slots” that share a limited pool of resources. By contrast, flexible resource models posit a single resource pool that can be allocated across an unlimited number of items. To test these models, we measured mnemonic resolution for orientation as a function of set size (1–8). Using a mixture model consistent with discrete resource models (Zhang and Luck, 2008), we estimated number (Pmem) and resolution (SD) as a function of set size. To test the flexible resource model, we fitted a single Gaussian distribution to the distribution of recall errors to operationalize WM capacity. Although both models predict worse mnemonic resolution for larger set sizes, the discrete resource model predicts that resolution should reach an asymptote when capacity has been achieved because items that are not stored should not affect the precision of the stored representations. In line with this hypothesis, the group data revealed a clear asymptote in resolution at set size 4. Critically, we also found that observers with fewer “slots” reached asymptote at smaller set sizes, leading to a strong correlation between individual slot estimates and the set size at which mnemonic resolution reached asymptote. By contrast, capacity estimates based on the assumptions of the flexible resource model were significantly worse at predicting resolution as a function of set size. Thus, discrete resource models provide superior predictive validity for understanding the relationship between resolution and set size in visual WM.
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