Age-related changes to electroencephalographic markers of visuomotor error processing and learning in prism adaptation.

Aging is associated with changes in cognitive function, including declines in learning, memory, and executive function. Prism adaptation (PA) is a useful paradigm to measure changes in explicit and implicit mechanisms of visuo-motor learning with age, but the neural correlates are not well understood. In the present study, we used PA to investigate visuo-motor learning and error processing in older adults. Twenty older adults (56-85 yrs) and 20 younger adults (18-33 yrs) underwent a goal-oriented reaching task while wearing prism goggles as continuous EEG was recorded to examine neural correlates of error detection. We examined behavioural measures of PA, as well as ERP components previously found associated with the early and late phases of adaptation to visual distortion caused by the prism goggles. Our results indicate important age-related behavioural and neurophysiological differences. Older adults reached more slowly than younger adults but showed the same accuracy throughout the prism exposure. Older adults also displayed larger aftereffects, indicating preserved visuomotor adaptation. EEG results indicated similar initial error processing in older and younger adults, as measured by the feedback error related negativity (FRN). As seen previously in young adults, the P3a and P3b declined over the prism exposure phase in both groups. Older adults displayed reduced P3a amplitude compared to the younger group in the early phase of adaptation, however, suggesting reduced attentional orienting. Finally, the older group exhibited a greater P3b amplitude compared to the younger group in the later phases of adaptation, potentially a marker of enhanced context updating underlying spatial realignment, leading to their larger aftereffect. Implications for age-related learning differences and clinical applications are discussed.

Revisiting the Links-Species Scaling Relationship in Food Webs.

Predicting the number of interactions among species in a food web is an important task. These trophic interactions underlie many ecological and evolutionary processes, ranging from biomass fluxes, ecosystem stability, resilience to extinction, and resistance against novel species. We investigate and compare several ways to predict the number of interactions in food webs. We conclude that a simple beta-binomial model outperforms other models, with the added desirable property of respecting biological constraints. We show how this simple relationship gives rise to a predicted distribution of several quantities related to link number in food webs, including the scaling of network structure with space and the probability that a network will be stable.