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Updated: Sep 26, 2026

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
Published on: June 12, 2017
Dynamics of variability and bias in working memory
Ivan Tomić1, Sebastian Schneegans2, Paul M Bays2
1Department of Psychology, Faculty of Humanities and Social Sciences, University of Zagreb.
Abstract:
Visual working memory performance declines with the quantity of material stored and the duration of its maintenance, but it is disputed whether these constraints are independent or interact. Specifically, different studies have reached contradictory conclusions on whether greater memory load accelerates deterioration of the stored information during retention, a question with implications for storage mechanisms. Meanwhile, prominent models of visual working memory based on stable attractors predict that recall should become increasingly biased toward a fixed set of canonical features with longer retention, but empirical support is mixed. Uncertainty is further exacerbated by limited data, methodological variation, and inconsistent analytical approaches across studies. Moreover, previous studies have not always distinguished response variability from stimulus-specific biases nor considered the possibility of swap errors (intrusions). To address these issues, we conducted six new experiments and reanalyzed data from seven published experiments using consistent analytical methods to examine the effects of set size and delay on variability and bias. Our results show that recall variability increased with longer delays and higher set sizes, consistent with prior findings, and importantly, that these effects did not operate independently: The effect of delay was amplified at higher set sizes. We also observed consistent evidence for systematic biases across stimuli, but unlike variability, bias amplitude did not change with either set size or retention interval. These results support diffusion accounts of visual working memory maintenance, in which variability grows through the accumulation of random error, while challenging stable attractor accounts that predict systematic drift toward canonical values during maintenance. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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