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Dynamical systems analysis applied to working memory data.


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.

SUBMITTER: Gasimova F 

PROVIDER: S-EPMC4080465 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Dynamical systems analysis applied to working memory data.

Gasimova Fidan F   Robitzsch Alexander A   Wilhelm Oliver O   Boker Steven M SM   Hu Yueqin Y   Hülür Gizem G  

Frontiers in psychology 20140703


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 updat  ...[more]

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