Initial performance predicts improvements in computerized cognitive training: Evidence from a selective attention task

Abstract

Computerized cognitive training (CCT) has been found to improve a range of skills such as attention, working memory, inhibition control, and decision making. However, the relationship between the initial performance, amount of improvement, time constant, and asymptotic performance level in CCT is still unclear. In the current study, we performed selective attention training on college students and addressed this issue by mathematically modeling the learning curve with an exponential function. Twenty-nine students completed approximately 10 days of CCT. Presentation time served as the dependent variable and was measured by three-down/one-up adaptive algorithms. We fitted an exponential function to the estimated block thresholds during CCT and obtained three learning parameters (amount of improvement, time constant, and asymptotic performance level) for all subjects. The initial performance was defined by the sum of the amount of improvement and the asymptotic performance level. Pearson correlation analyses were conducted between the initial performance and the three leaning parameters. The initial performance was positively correlated with the amount of improvement and asymptotic performance level, but was negatively correlated with the time constant. The time constant was negatively correlated with the amount of improvement and asymptotic performance level. Poorer initial performance was linked to a larger amount of improvement, shorter time constant, and higher asymptotic threshold, which supported the compensation account. Our results may help improve the present understanding of the nature of the CCT process and demonstrate the advantages of using a customized training protocol to enhance the efficiency of cognitive training in practical applications.

Keywords: computerized cognitive training; initial performance; learning curve; selective attention.