Date of Award

August 2019

Degree Type


Degree Name

Master of Science



First Advisor

Ira Driscoll

Committee Members

David C Osmon, Raymond Fleming


Diffusion model, normal aging, reaction time, reversal learning


The frontal lobes are known to atrophy with age (Lockhart & DeCarli, 2014) and integrity of this region has been implicated in maintaining executive functioning (Chayer & Freedman, 2001). Reversal learning tasks are frequently used in experimental paradigms to assess components of executive function. Extant reversal learning literature has largely assessed measures of accuracy, but reaction time (RT) has not yet been well characterized. The current study examines the empirical RT distribution of the reversal task by utilizing distributional and theoretical analyses to better characterize performance and how it changes with age. Participants included 43 young (ages 18-30; M = 21.76, SD = 2.85) and 139 community dwelling middle-aged adults (ages 40-61; M = 49.96, SD = 6.14). Results showed a Normal-3 Mixture distribution best fit the sample as a whole, with the ex-Gaussian distribution passing visual inspection. This suggests both models and their parameters should be considered to evaluate group differences for this task. Correlation results showed RT and accuracy are distinct components of reversal learning. Age related significantly to RT and more so to efficient RTs (Mu) than overall RT. A generalized regression further revealed that RT adds unique variance to explaining age-related differences in performance. Specifically, middle-aged adults showed slower, efficient RT and increased intra-individual variability which has been previously linked to poorer frontal lobe processes and age-related cognitive decline (MacDonald, Nyberg, & Backman, 2006). Lastly, four RT-based factors were identified (Mental Efficiency, Intra-Individual Variability, Mental Speed, Inattention) that successfully distinguished groups with fractionated profiles of performance and should be further investigated to explore potential clinical implications in the context of cognitive aging. Overall, these findings highlight the importance of examining the RT distribution and measuring RT as a fractionated construct to further explain age-related differences in reversal learning.