Bodily awareness is a central component of human sensation, action, and cognition. The human body is subject to profound changes over the adult lifespan. We live in an aging society: the mean age of people living in industrialized countries is currently over 40 years, and further increases are expected. Nevertheless, there is a lack of comprehensive knowledge that links changes in embodiment that occur with age to neuronal mechanisms and associated sensorimotor and cognitive deficits in older adults. Here, we synthesize existing evidence and introduce the NFL Framework of Embodied Aging, which links basic neuronal (N) mechanisms of age-related sensorimotor decline to changes in functional (F) bodily impairments, including deficits in higher-level cognitive functions, and impairments in daily life (L). We argue that cognitive and daily life impairments associated with old age are often due to deficits in embodiment, which can partly be linked to neuronal degradation at the sensorimotor level. The framework may encourage the development of novel approaches to improve autonomous living for older adults.

Older adults often experience serious problems in spatial navigation, and alterations in underlying brain structures are among the first indicators for a progression to neurodegenerative diseases. Studies investigating the neural mechanisms of spatial navigation and its changes across the adult lifespan are increasingly using virtual reality (VR) paradigms. VR offers major benefits in terms of ecological validity, experimental control and options to track behavioral responses. However, navigation in the real world differs from navigation in VR in several aspects. In addition, the importance of body-based or visual cues for navigation varies between animal species. Incongruences between sensory and motor input in VR might consequently affect their performance to a different degree. After discussing the specifics of using VR in spatial navigation research across species, we outline several challenges when investigating age-related deficits in spatial navigation with the help of VR. In addition, we discuss ways to reduce their impact, together with the possibilities VR offers for improving navigational abilities in older adults.

A large body of evidence suggests that action execution and action observation share a common representational domain. To date, little is known about age-related changes in these action representations that are assumed to support various abilities such as the prediction of observed actions. The purpose of the present study was to investigate (a) how age affects the ability to predict the time course of observed actions; and (b) whether and to what extent sensorimotor expertise attenuates age-related declines in prediction performance. In a first experiment, older adults predicted the time course of familiar everyday actions less precisely than younger adults. In a second experiment, younger and older figure skating experts as well as age-matched novices were asked to predict the time course of figure skating elements and simple movement exercises. Both young age and sensorimotor expertise had a positive influence on prediction performance of figure skating elements. The expertise-related benefit did not show a transfer to movement exercises. Together, the results suggest a specific decline of action representations in the aging mind. However, extensive sensorimotor experience seems to enable experts to represent actions from their domain of expertise more precisely even in older age.

Generating predictions during action observation is essential for efficient navigation through our social environment. With age, the sensitivity in action prediction declines. In younger adults, the action observation network (AON), consisting of premotor, parietal and occipitotemporal cortices, has been implicated in transforming executed and observed actions into a common code. Much less is known about age-related changes in the neural representation of observed actions. Using fMRI, the present study measured brain activity in younger and older adults during the prediction of temporarily occluded actions (figure skating elements and simple movement exercises). All participants were highly familiar with the movement exercises whereas only some participants were experienced figure skaters. With respect to the AON, the results confirm that this network was preferentially engaged for the more familiar movement exercises. Compared to younger adults, older adults recruited visual regions to perform the task and, additionally, the hippocampus and caudate when the observed actions were familiar to them. Thus, instead of effectively exploiting the sensorimotor matching properties of the AON, older adults seemed to rely predominantly on the visual dynamics of the observed actions to perform the task. Our data further suggest that the caudate played an important role during the prediction of the less familiar figure skating elements in better-performing groups. Together, these findings show that action prediction engages a distributed network in the brain, which is modulated by the content of the observed actions and the age and experience of the observer.