Effects of older age on visual and self-motion sensory cue integration in navigation

Abstract

Older adults demonstrate impairments in navigation that cannot be explained by general cognitive and motor declines. Previous work has shown that older adults may combine sensory cues during navigation differently than younger adults, though this work has largely been done in dark environments where sensory integration may differ from full-cue environments. Here, we test whether aging adults optimally combine cues from two sensory systems critical for navigation: vision (landmarks) and body-based self-motion cues. Participants completed a homing (triangle completion) task using immersive virtual reality to offer the ability to navigate in a well-lit environment including visibility of the ground plane. An optimal model, based on principles of maximum-likelihood estimation, predicts that precision in homing should increase with multisensory information in a manner consistent with each individual sensory cue's perceived reliability (measured by variability). We found that well-aging adults (with normal or corrected-to-normal sensory acuity and active lifestyles) were more variable and less accurate than younger adults during navigation. Both older and younger adults relied more on their visual systems than a maximum likelihood estimation model would suggest. Overall, younger adults' visual weighting matched the model's predictions whereas older adults showed sub-optimal sensory weighting. In addition, high inter-individual differences were seen in both younger and older adults. These results suggest that older adults do not optimally weight each sensory system when combined during navigation, and that older adults may benefit from interventions that help them recalibrate the combination of visual and self-motion cues for navigation.

Keywords: Aging; Sensory integration; Spatial navigation; Spatial updating; Virtual environment.

Fig. 1

A schematic of the environment layout and homing task. The target and the first waypoint appeared in 1 of 4 locations, represented by the green and red circles, respectively. Waypoint 2, the blue circle, was always in the same location and defined the origin of the coordinate system

Fig. 2

A render of the virtual environment used during the task. The duplicate set of partially transparent landmarks show how the landmarks would shift during the conflict trials. Only one set of landmarks was shown at a time (none during the self-motion condition), and participants were not aware of the conflict at the time of testing. Only the green target and red waypoint were initially visible to participants. Once participants walked to the green target, the blue waypoint (waypoint 2) appeared in the environment

Fig. 3

A bar chart of mean homing error in cm for younger and older participants. Error bars denote SEM. ** denotes p < 0.01, * denotes p < 0.05, ns denotes non-significance with p > 0.05

Fig. 4

A bar chart of mean variability as defined by standard deviation in cm for younger and older participants. Error bars denote SEM. *** denotes p < 0.001, ** denotes p < 0.01, * denotes p < 0.05, ns denotes non-significance with p > 0.05

Fig. 5

Single cue weights are shown for both younger and older participant groups. Predicted weights (green diamonds) are calculated according to equations 1 and 2. Observed weights (brown circles) are derived from the spatial conflict according to equations 7 and 8. Visual cue weights are plotted on the left y-axis and self-motion cue weights are on the right y-axis (single cue weights sum to 1). The horizontal lines on plot denote the mean and the error bars denote the SEM

Fig. 6

Predicted visual weights are correlated with Observed visual weights for each participant split across the younger (left) and older (right) participant groups. The solid line shows the best-fit line of the correlation and the dotted lines demonstrate the 95% confidence intervals of the best-fit lines. R² and significance values are shown in the bottom right of each plot