Reversal learning in young and middle-age neurotypicals: Individual difference reaction time considerations. Issue 9 (20th October 2020)

• Record Type: Journal Article
• Authors: Osmon, David C.; Leclaire, Kaitlynne N.; Driscoll, Ira; Zolliecoffer, Chandler J.
• Is Part Of: Journal of clinical and experimental neuropsychology. Volume 42:Issue 9(2020)
• Page Start: 902
• Page End: 913
• Publication Date: 2020-10-20
• Subjects: Reversal learning -- reaction time -- ex-Gaussian; Neuropsychology -- Periodicals; Psychophysiology -- Periodicals; Neurosciences -- Periodicals; Psychophysiology -- Periodicals; Societies, Medical -- Periodicals; 616.89
• DOI: 10.1080/13803395.2020.1825635
• Languages: English
• ISSNs: 1380-3395

Abstract

ABSTRACT: Reversal learning is frequently used to assess components of executive function that contribute to understanding age-related cognitive differences. Reaction time (RT) is less characterized in the reversal learning literature, perhaps due to the daunting task of analyzing the entire RT distribution, but has been deemed a generally sensitive measure of cognitive aging. The current study extends our prior work to further characterize distributional properties of the reversal RT distribution and to distinguish groups of individuals with fractionated profiles of performance, which may be of clinical importance within the context of cognitive aging. Participant sample included young ( n = 43) and community-dwelling, healthy, middle-aged ( n = 139) adults. To explore individual differences, recursive partitioning analysis achieved a high classification rate by specifying decision tree rules that split participants into young and middle-aged groups. Mu (μ, efficient RT) was the most successful parameter in distinguishing age groups while sigma ( σ ) and tau ( τ, ex-Gaussian indices of intra-individual variability) revealed more subtle individual differences. Accuracy measures did not contribute to separating the groups, suggesting that fractionated components of RT, as opposed to accuracy, can distinguish differences between young and middle-aged participants.