Using Citation Network Analysis to Examine the State of Peer-Reviewed Perceptual-Cognitive Training Literature with a Research Focus on Neurotracker

Abstract

Computer based perceptual-cognitive training is an increasingly popular tool in performance improvement applications yet the academic link between computer training and real-world performance results is tenuous at best. Many commercially available products forge ahead without academic research supporting their product. One perceptual-cognitive training program, NeuroTracker, has differentiated itself by basing its development in academic research. This has resulted in wide use of the program around the world. NeuroTracker holds a leading role in perceptual-cognitive training and this thesis examines the peer-reviewed NeuroTracker literature through citation network analysis to discover the current state of the research. Citation network analysis is a process used to measure and visually represent the interconnectivity of published material through citations. A comprehensive list of journal articles was collected, coded using content analysis, and examined using citation network analysis and exploratory data analysis. Using a network analysis software program to generate graphic visualizations, visual attributes were assigned to specific attributes of the data and the data were filtered to only include the articles that match specific criteria with the purpose of focusing discovery and removing information irrelevant to the research question being examined. The key finding of this thesis is that the NeuroTracker literature is well connected through foundational articles that are awarded a disproportionate amount of influence in the literature. It is recommended that more diverse peer-reviewed research is completed outside of the foundational and established research to truly solidify NeuroTracker as a scientifically validated tool.