Behavioral data analysis
CF, fatigue
EVENTS
SPSS statistical software (v. 28.0; IBM Corp., Armonk, NY) and RStudio (v. 4.1.0; R Foundation for Statistical Computing; Vienna, Austria) were used for behavioral data analysis. Independent-samples t tests were used to check for pre-existing group differences in age and education level, while a chi-square test was used to check the same for sex. To confirm that MS participants experienced significantly greater trait fatigue than NT participants, we used an independent-samples To examine group differences in learning across the three feedback conditions, while also controlling for any influences of state fatigue, we employed linear mixed effects modeling using the We also tested for differences in subsequent memory performance during Phase 3 as a function of Phase 2 feedback valence (i.e., positive feedback events on correct trials and negative feedback events on incorrect trials), and whether these differences interacted with group. We employed an additional linear mixed effects model with the percentage of Phase 3 correct responses as the outcome variable. Phase 2 feedback valence outcome was dummy-coded (positive as 1, negative as 0) and entered as a fixed effect, as was group and a group × Phase 2 feedback valence interaction term. We also entered state fatigue ratings for the monetary and non-monetary feedback conditions as covariate fixed effects to adjust for state fatigue. Post-hoc pairwise comparisons applying the Bonferroni adjustment were used to identify significant mean differences. See A Spearman correlation analysis of EDSS scores and delta performance was used to evaluate whether disease-related neurological and functional impairment were associated with MS participants’ learning.Given MS participants’ susceptibility to fatigue, we also analyzed changes in their state fatigue throughout the task and whether they differed from NT participants. We entered state fatigue rating as the outcome variable. Group and feedback condition were entered as fixed effects. Furthermore, since the order of feedback condition blocks was randomized, we estimated changes in state fatigue across the duration of Phase 2 (i.e., 15 blocks) as a fixed effect. This allowed for the control of cumulative time-on-task on state fatigue ratings. We also included a participant random effect. We modeled both random intercepts and random slopes to account for differences in trait CF at the start of the task, as well as differences in the rate at which state CF occurred throughout the task. See We also measured possible associations between trait and state CF by conducting Pearson correlation analyses between FSS scores and mean VAS-F ratings within each group for each feedback condition.Independent-samples t tests using neuropsychological test variables were conducted to assess differences in cognitive ability between MS and NT participants.