Dynamical systems analysis applied to working memory data

Frontiers in Psychology

Fidan Gasimova¹ 

关键词: dynamical systems analysis;    intensive longitudinal data;    intraindividual variability;    B-spline imputation;    simulation study;   

DOI  :  10.3389/fpsyg.2014.00687

学科分类：心理学（综合）

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

CC BY