The Costs of Taking It Slowly Fast and Slow Movement Timing in Older Age

We investigated adult age differences in timing control of fast vs slow repetitive movements using a dual task approach Twenty two young (M = 24 23 yr) and 22 older adults (M = 66 64 yr) performed three cognitive tasks differing in working memory load and response production demands and they tapped series of 550 ms or 2100 ms target Intervals Single task timing was comparable in both groups Dual task timing was characterized by shortening of produced intervals and increases in drift and variability Dual task costs for both cognitive and timing performances were pronounced at slower tapping tempos an effect exacerbated in older adults Our findings implicate attention and working memory processes as critical components of slow movement timing and sources of specific challenges thereof for older adults

Working Memory and Postural Control: Adult Age Differences in Potential for Improvement, Task Priority, and Dual Tasking

We investigate dynamic posture control and working memory (NBack) retest practice in young and older adults, focusing on older adults' potential for improvement in the component tasks but more importantly in dual-task performance. Participants performed the 2 tasks in 11 sessions under single- and dual-task conditions. Posture improvement was observed with retest practice for both groups. Increase in cognitive load after initial practice led to greater dual-task costs in both tasks in older adults and higher costs in memory in young adults. With continued practice, costs were reduced by both groups; however, the 2 groups focused improvement on different tasks: Older adults focused on posture but young adults on cognition. These results emphasize older adults' potential for improvement in dual-task performance and their flexibility to utilize the practice gains in posture to optimize cognitive performance.

Flexible paleoclimate age-depth models using an autoregressive gamma process

Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called “Bacon”) that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.