Principal Component Analysis of Working Memory Variables during Child and Adolescent Development

2016 ◽  
Vol 19 ◽  
Author(s):  
Catarina I. Barriga-Paulino ◽  
Elena I. Rodríguez-Martínez ◽  
María Ángeles Rojas-Benjumea ◽  
Carlos M. Gómez
Keyword(s):  
Principal Component Analysis ◽  
Working Memory ◽  
Memory Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Test Battery ◽  
Childhood And Adolescence ◽  
Behavioral Measures ◽  
Age Related ◽  
Speed Accuracy

AbstractCorrelation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6–18 years old. The correlation analysis (p < .05) results showed a common maturational trend in working memory performance between these two types of tasks. Applying PCA (Eigenvalues > 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

Automatically Terminated Particle Swarm Optimization with Principal Component Analysis

2015 ◽  
Vol 14 (01) ◽  
pp. 171-194 ◽  
Author(s):  
Bun Theang Ong ◽  
Masao Fukushima
Keyword(s):  
Principal Component Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Principal Component ◽  
Component Analysis ◽  
Convergence Speed ◽  
Swarm Optimization ◽  
Termination Criteria ◽  
Hybrid Particle Swarm Optimization ◽  
Speed Accuracy

A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called "Gene Matrix" to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant.

scholarly journals Predictive Power of Head Impact Intensity Measures for Recognition Memory Performance

2019 ◽  
Vol 184 (Supplement_1) ◽  
pp. 206-217
Author(s):  
Daniel D Leeds ◽  
Christopher D’Lauro ◽  
Brian R Johnson
Keyword(s):  
Principal Component Analysis ◽  
Memory Performance ◽  
Head Injuries ◽  
Principal Component ◽  
Component Analysis ◽  
Head Impact ◽  
Cognitive Changes ◽  
Intensity Measures ◽  
Post Exercise ◽  
Head Impacts

Abstract Subconcussive head injuries are connected to both short-term cognitive changes and long-term neurodegeneration. Further study is required to understand what types of subconcussive impacts might prove detrimental to cognition. We studied cadets at the US Air Force Academy engaged in boxing and physical development, measuring head impact motions during exercise with accelerometers. These head impact measures were compared with post-exercise memory performance. Investigators explored multiple techniques for characterizing the magnitude of head impacts. Boxers received more head impacts and achieved lower performance in post-exercise memory than non-boxers. For several measures of impact motion, impact intensity appeared to set an upper bound on post-exercise memory performance – stronger impacts led to lower expected memory performance. This trend was most significant when impact intensity was measured through a novel technique, applying principal component analysis to boxer motion. Principal component analysis measures also captured more distinct impact information than seven traditional impact measures also tested.

Age-Related Differences in Gait Kinematics, Kinetics, and Muscle Function: A Principal Component Analysis

2016 ◽  
Vol 45 (3) ◽  
pp. 695-710 ◽  
Author(s):  
Sarah A. Schloemer ◽  
Julie A. Thompson ◽  
Amy Silder ◽  
Darryl G. Thelen ◽  
Robert A. Siston
Keyword(s):  
Principal Component Analysis ◽  
Muscle Function ◽  
Principal Component ◽  
Component Analysis ◽  
Gait Kinematics ◽  
Age Related

scholarly journals Investigating the age-related “anterior shift” in the scalp distribution of the P3b component using principal component analysis

2014 ◽  
Vol 51 (7) ◽  
pp. 620-633 ◽  
Author(s):  
Brittany R. Alperin ◽  
Katherine K. Mott ◽  
Dorene M. Rentz ◽  
Phillip J. Holcomb ◽  
Kirk R. Daffner
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Scalp Distribution ◽  
Age Related
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