Dear colleagues,
let me invite you to a short seminar presenting the results published in this
article: https://doi.org/10.1371/journal.pcbi.1010464

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.