Complex Span Versus Updating Tasks of Working Memory: The Gap Is Not That Deep

Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.
Journal of Experimental Psychology Learning Memory and Cognition (Impact Factor: 2.86). 08/2009; 35(4):1089-96. DOI: 10.1037/a0015730
Source: PubMed


How to best measure working memory capacity is an issue of ongoing debate. Besides established complex span tasks, which combine short-term memory demands with generally unrelated secondary tasks, there exists a set of paradigms characterized by continuous and simultaneous updating of several items in working memory, such as the n-back, memory updating, or alpha span tasks. With a latent variable analysis (N = 96) based on content-heterogeneous operationalizations of both task families, the authors found a latent correlation between a complex span factor and an updating factor that was not statistically different from unity (r = .96). Moreover, both factors predicted fluid intelligence (reasoning) equally well. The authors conclude that updating tasks measure working memory equally well as complex span tasks. Processes involved in building, maintaining, and updating arbitrary bindings may constitute the common working memory ability underlying performance on reasoning, complex span, and updating tasks.

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    • "2.2.4. Alpha span (EF — updating) A version of this task was created by modifying the procedure used by Schmiedek et al. (2009). Ten letters appeared sequentially in the centre of computer screen, with each having a number (from 1 to 10) displayed below it. "
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    • "At each measurement occasion different versions of the tasks were used. One of the working memory capacity tasks used in this intensive longitudinal study was a memory updating task (Oberauer et al., 2000, 2003; Schmiedek et al., 2009). "
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    ABSTRACT: In the present paper we investigate weekly fluctuations in the working memory capacity (WMC) assessed over a period of 2 years. We use dynamical system analysis, specifically a second order linear differential equation, to model weekly variability in WMC in a sample of 112 9th graders. In our longitudinal data we use a B-spline imputation method to deal with missing data. The results show a significant negative frequency parameter in the data, indicating a cyclical pattern in weekly memory updating performance across time. We use a multilevel modeling approach to capture individual differences in model parameters and find that a higher initial performance level and a slower improvement at the MU task is associated with a slower frequency of oscillation. Additionally, we conduct a simulation study examining the analysis procedure's performance using different numbers of B-spline knots and values of time delay embedding dimensions. Results show that the number of knots in the B-spline imputation influence accuracy more than the number of embedding dimensions.
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    • "Tau indexes the exponential component of the RT distribution and reflects the subset of extremely slow responses that otherwise have a strong influence on mean RT and standard deviation of RT calculation. Tau is similar conceptually to a distribution's skewness, but is considered a more reliable metric (Schmiedek et al., 2007). A latent factor was created using principal components factor analysis separately for sigma (59.87% variance accounted for; both factor loadings r ϭ .77; "
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