Abstract
Path integration plays a vital role in navigation: it enables the continuous tracking of one's position in space by integrating self-motion cues. Path integration abilities vary widely across individuals, and tend to deteriorate in old age. The specific causes of path integration errors, however, remain poorly characterized. Here, we combine tests of path integration performance in participants of different ages with an analysis based on the Langevin equation for diffusive dynamics, which allows us to decompose errors into distinct causes that can corrupt path integration computations. We show that, across age groups, the dominant error source is unbiased noise that accumulates with travel distance not elapsed time, suggesting that the noise originates in the velocity input rather than within the integrator. Age-related declines are primarily traced to a growth in this noise. These findings shed light on the contributors to path integration error and the mechanisms underlying age-related navigational deficits.
Highlights
Path integration plays a vital role in navigation: it enables the continuous tracking of one's position in space by integrating self-motion cues
We developed a powerful analysis approach based on stochastic differential equations to decompose path integration errors into temporally resolved gain, leak, bias, as well as noise terms, and to estimate, on a trial-to-trial basis at different times along the path, how these different sources of error contribute to the location estimation error
We show that path integration computations are mainly corrupted by accumulating noise that mainly originates in the velocity input to the path integrator, and that an increase in this noise with age accounts for the majority of agerelated path integration deficits
Summary
Path integration plays a vital role in navigation: it enables the continuous tracking of one's position in space by integrating self-motion cues. After being processed in their respective low-level sensory systems, these cues are integrated in brainstem nuclei, as well as cortical structures, to allow for an overall estimation of angular and linear movement velocity[6,7,8,9,10,11,12]. The integration of these cues is an error-prone process, and previous studies have demonstrated that path integration abilities vary largely across individuals[13,14,15]. One final error arises when a downstream neural circuit or the human experimenter attempt to obtain a readout or report of the internal state of the integrator
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