Jes Parent

Embodied & Diverse Intelligences: Development, Learning & Evolution across Biological, Cognitive, and Artificial Domains

Curriculum vitae

jeparent [@] ucsd.edu

1. Halıcıoğlu Data Science Institute, UCSD, San Diego CA

2. Cognition Futures, OREL, Boston MA

Allostasis Machines: a model for understanding internal states and technological environments

Unpublished

Bradly Alicea, C. D., A. Lim, Jesse Parent
2021

Semantic Scholar DOI

Cite

APA Click to copy
Alicea, B., D., C., Lim, A., & Parent, J. (2021). Allostasis Machines: a model for understanding internal states and technological environments.

Chicago/Turabian Click to copy
Alicea, Bradly, C. D., A. Lim, and Jesse Parent. “Allostasis Machines: a Model for Understanding Internal States and Technological Environments,” 2021.

MLA Click to copy
Alicea, Bradly, et al. Allostasis Machines: a Model for Understanding Internal States and Technological Environments. 2021.

BibTeX Click to copy

@unpublished{bradly2021a,
  title = {Allostasis Machines: a model for understanding internal states and technological environments},
  year = {2021},
  author = {Alicea, Bradly and D., C. and Lim, A. and Parent, Jesse}
}

Abstract

In the present paper we will approach enactivism from the perspective of internal regulation: while the environment shapes the organism, it is also true that organisms have complex internal states with regulatory machinery with a set of continuous phenotype-environment interactions. The aim of the present paper is to provide a visual means to analyze these interactions in individuals and computational agents alike. An essential component of our approach is the representation of continuous internal states through the usage of the single continuous indicator we call an Allostasis Machine (AM). Consequently, we consider potential perturbation regimes for both naturalistic and virtual environments: within the naturalistic cases, it is possible to observe the effects of perturbations in isolation, or as overlapping, multiplicative events. In virtual cases, we can observe perturbations as the outcome of both realistic and fantastical environments. To conclude, we discuss how AMs can be utilized to improve our understanding of both the theoretical basis of embodied interaction and the dynamic regulation of complex psychophysiological states.