Computational models of peripersonal space representation

Peripersonal space (PPS) is the region of space near the body, the multisensory interface where interactions with the environment predominantly occur. This space is represented by a specialized neural system that integrates tactile and external stimuli as a function of their distance from the body. Previous studies uncovered plastic and dynamical properties of PPS representation, links between PPS encoding and higher-level cognitive functions (e.g., social cognition), as well as its alterations in neurological and psychiatric disorders. These findings have expanded the definition of PPS and have led to the development of an array of computational models of PPS, addressing the why and how of PPS encoding. Although computational models are crucial for advancing our mechanistic and functional understanding of PPS representation, no prior work has reviewed these models. Here, we address this gap by analysing computational models of PPS, and proposing a taxonomy to classify them based on their level of description, capacity to reproduce empirical findings, and ability to generate novel predictions. This effort leads us to propose that PPS may be best understood as a system that detects spatiotemporal regularities in body-environment interactions, in order to predict potential future interactions. Hence, we suggest re-defining PPS as a unified spatiotemporal field that integrates not only spatial dimensions, but also temporal ones.