Cognitive, Systems, and Computational Neurosciences of the Self in Motion

Abstract

Navigating by path integration requires continuously estimating one's self-motion. This estimate may be derived from visual velocity and/or vestibular acceleration signals. Importantly, these senses in isolation are ill-equipped to provide accurate estimates, and thus visuo-vestibular integration is an imperative. After a summary of the visual and vestibular pathways involved, the crux of this review focuses on the human and theoretical approaches that have outlined a normative account of cue combination in behavior and neurons, as well as on the systems neuroscience efforts that are searching for its neural implementation. We then highlight a contemporary frontier in our state of knowledge: understanding how velocity cues with time-varying reliabilities are integrated into an evolving position estimate over prolonged time periods. Further, we discuss how the brain builds internal models inferring when cues ought to be integrated versus segregated-a process of causal inference. Lastly, we suggest that the study of spatial navigation has not yet addressed its initial condition: self-location.

Keywords: Bayesian inference; body; multisensory; navigation; peri-personal space; population probabilistic coding.

Figure 1.. Visual and vestibular pathways leading to allocentric coding in parahippocampal formation.

Vestibular-only (VO) cells in the vestibular nuclei project via the anterior vestibulo-thalamic pathway to hippocampal formation, first reflecting an egocentric code – given the idiothetic nature of the vestibular system – and ending in an allocentric code (e.g., place fields). Via the posterior vestibulo-thalamic pathway, vestibular signals permeate much of the posterior parietal cortex. The exact nature and strength of the message-passing across much of this schematic network remain to be fully described, and this schematic coalesces evidence from a number of species; macaques, rodents, and fruit-flies. Thus, there are likely species-specific variations (e.g., head-direction cells exist in retrospenial cortex (RSC) in rodents, Keshavarzi et al., 2021, yet this is unknown in macaque). Nonetheless, overall area 7a and RSC seem to be strong points of contact between egocentric coding in cortex and allocentric coding in the hippocampal formation (e.g., Whitlock et al., 2008; Keshavarzi et al., 2021).

Figure 2.. Causal Inference.

Our sensory periphery redundantly samples from the environment (empty circles, step 1). Based on these samples, we build an internal model of the potential causal structure of the world that may have given rise to the observed sensory data (Eq. 5, step 2). In the first hypothesis illustrated here (Hypothesis 1), the two senses index a common object in the environment (purple). As such, the samples that best reflect the state of affairs is the middle sample for sense 1, and the right-most for sense 2 (color-coded, darker = sample falling closer to the mean of the inferred distribution). Since signals from both sense 1 and 2 are taken to come from the same source, we may integrate this information, together with a prior, according to maximum-likelihood estimation, Eq. 2, step 3). Conversely, we may hypothesize that the two senses reflect different objects in the external environment (a red one and a blue one). If this were the case, the central sample, both for sense 1 and 2 (darkest red and blue respectively), is best aligned with the mean of the inferred distribution (again, stronger hue indicating the sample closest to the mean of its distribution). Under this hypothesis, we would not integrate the different signals (step 3). Lastly, we may combine (or not) world views (i.e., hypotheses) in acting on the external world (step 4). Two potential solutions are illustrated here. In a model selection strategy (left), we would commit to the most likely hypothesis. In this example, we assume hypothesis 1 is most likely, and thus the final estimates correspond to the estimates from this model. In a model averaging strategy (right), observers may weigh estimates according to the relative certainty of the hypothesis. Again, hypothesis 1 is most likely in this example. Thus, the final estimates (empty triangles) will fall somewhere in between the estimates derived from hypothesis 1 (purple) and hypothesis 2 (blue and red), but closer to the former.