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Related Experiment Video

Updated: Sep 15, 2025

Observing the Transformation of Bodily Self-consciousness in the Squeeze-machine Experiment

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Published on: March 8, 2019

Computational models of peripersonal space representation.

Tommaso Bertoni¹, Ishan-Singh J Chauhan², Jean-Paul Noel³

¹MySpace Lab, Department of Clinical Neurosciences, University Hospital of Lausanne, University of Lausanne, Lausanne, Switzerland; Translational Neural Engineering Laboratory, Neuro-X Institute, Ecole Polytechnique Federale de Lausanne (EPFL) 1015 Lausanne, Switzerland.

Physics of Life Reviews

|July 16, 2025

Summary

Peripersonal space (PPS), the sensory buffer around the body, is analyzed through computational models. This review proposes a new definition of PPS as a spatiotemporal field for predicting interactions.

Keywords:

Body representation Computational model Peripersonal space Space and time Statisticalregularities

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Last Updated: Sep 15, 2025

Observing the Transformation of Bodily Self-consciousness in the Squeeze-machine Experiment

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Area of Science:

Neuroscience
Cognitive Science
Computational Modeling

Background:

Peripersonal space (PPS) is the near-body sensory buffer critical for environmental interaction.
PPS representation is dynamic, linked to cognition, and altered in disorders.
Existing research lacks a review of computational models explaining PPS encoding.

Purpose of the Study:

To review and classify existing computational models of Peripersonal space (PPS).
To propose a novel framework for understanding PPS based on computational analysis.
To advance mechanistic and functional insights into PPS representation.

Main Methods:

Systematic analysis of computational models of Peripersonal space (PPS).
Development of a taxonomy to classify PPS models based on descriptive level, empirical reproduction, and predictive power.
Synthesis of findings to propose a new conceptualization of PPS.

Main Results:

Identified diverse computational approaches to modeling Peripersonal space (PPS).
Proposed a classification system for PPS models.
Highlighted the need for models that capture the spatiotemporal dynamics of PPS.

Conclusions:

Peripersonal space (PPS) may be best understood as a system detecting spatiotemporal regularities for interaction prediction.
A redefinition of PPS as a unified spatiotemporal field integrating spatial and temporal dimensions is proposed.
Computational models are crucial for a deeper understanding of PPS representation and function.