A distributional and theoretical analysis of reaction time in the reversal task across adulthood. Issue 2 (7th February 2020)

• Record Type: Journal Article
• Authors: Leclaire, Kaitlynne N.; Osmon, David C.; Driscoll, Ira
• Is Part Of: Journal of clinical and experimental neuropsychology. Volume 42:Issue 2(2020)
• Page Start: 199
• Page End: 207
• Publication Date: 2020-02-07
• Subjects: Normal aging -- reversal learning -- reaction time -- Diffusion model -- non-Gaussian; Neuropsychology -- Periodicals; Psychophysiology -- Periodicals; Neurosciences -- Periodicals; Psychophysiology -- Periodicals; Societies, Medical -- Periodicals; 616.89
• DOI: 10.1080/13803395.2019.1703909
• Languages: English
• ISSNs: 1380-3395

Abstract

ABSTRACT: Reversal learning assesses components of executive function important for understanding cognitive changes with age. Extant reversal learning literature has largely assessed measures of accuracy, but reaction time (RT) has not yet been well characterized, perhaps due to the daunting task of analyzing non-normal RT distributions. The current study contributes to the literature by examining distributional and theoretical aspects of the entire RT distribution in addition to accuracy. Participant sample included young (N = 43) and community-dwelling, healthy, middle-aged (N = 139) adults. Results showed a Normal-3 Mixture distribution best fits the sample as a whole, with the ex-Gaussian distribution passing visual inspection. Age related significantly to various measures of RT (p's < 0.5); older age was associated with higher both efficient and overall RT, perhaps due to a more conservative criterion of decision-making. In a generalized adaptive elastic net regression, RT explained age-related differences in performance while accuracy did not contribute. Specifically, middle-aged adults were slower in efficient RT and had increased intra-individual variability which has been previously linked to poorer frontal lobe processes and age-related cognitive decline. Overall, these findings highlight the importance of examining the entire RT distribution and measuring RT as a fractionated construct to further explain age-related differences in reversal learning, even in middle-aged individuals.