A normative model of peripersonal space encoding as performing impact prediction

Zdenek Straka, Jean-Paul Noel and Matej Hoffmann

PLOS Computational Biology, 2022, vol. 18, issue 9, 1-23

Abstract: Accurately predicting contact between our bodies and environmental objects is paramount to our evolutionary survival. It has been hypothesized that multisensory neurons responding both to touch on the body, and to auditory or visual stimuli occurring near them—thus delineating our peripersonal space (PPS)—may be a critical player in this computation. However, we lack a normative account (i.e., a model specifying how we ought to compute) linking impact prediction and PPS encoding. Here, we leverage Bayesian Decision Theory to develop such a model and show that it recapitulates many of the characteristics of PPS. Namely, a normative model of impact prediction (i) delineates a graded boundary between near and far space, (ii) demonstrates an enlargement of PPS as the speed of incoming stimuli increases, (iii) shows stronger contact prediction for looming than receding stimuli—but critically is still present for receding stimuli when observation uncertainty is non-zero—, (iv) scales with the value we attribute to environmental objects, and finally (v) can account for the differing sizes of PPS for different body parts. Together, these modeling results support the conjecture that PPS reflects the computation of impact prediction, and make a number of testable predictions for future empirical studies.Author summary: The brain has neurons that respond to touch on the body, as well as to auditory or visual stimuli occurring near the body. These neurons delineate a graded boundary between the near and far space. Here, we aim at understanding whether the function of these neurons is to predict future impact between the environment and body. To do so, we build a mathematical model that is statistically optimal at predicting future impact, taking into account the costs incurred by an impending collision. Then we examine if its properties are similar to those of the above-mentioned neurons. We find that the model (i) differentiates between the near and far space in a graded fashion, predicts different near/far boundary depths for different (ii) body parts, (iii) object speeds and (iv) directions, and (v) that this boundary scales with the value we attribute to environmental objects. These properties have all been described in behavioral studies and ascribed to neurons responding to objects near the body. Together, these findings suggest why the brain has neurons that respond only to objects near the body: to compute predictions of impact.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010464

DOI: 10.1371/journal.pcbi.1010464

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