Next Article in Journal
Unifying Aspects of Generalized Calculus
Next Article in Special Issue
Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding
Previous Article in Journal
Innovativeness of Industrial Processing Enterprises and Conjunctural Movement
Previous Article in Special Issue
The Convergence of a Cooperation Markov Decision Process System
Article

Space Emerges from What We Know—Spatial Categorisations Induced by Information Constraints

School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL109AB, UK
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(10), 1179; https://doi.org/10.3390/e22101179
Received: 16 September 2020 / Revised: 12 October 2020 / Accepted: 14 October 2020 / Published: 19 October 2020
Seeking goals carried out by agents with a level of competency requires an “understanding” of the structure of their world. While abstract formal descriptions of a world structure in terms of geometric axioms can be formulated in principle, it is not likely that this is the representation that is actually employed by biological organisms or that should be used by biologically plausible models. Instead, we operate by the assumption that biological organisms are constrained in their information processing capacities, which in the past has led to a number of insightful hypotheses and models for biologically plausible behaviour generation. Here we use this approach to study various types of spatial categorizations that emerge through such informational constraints imposed on embodied agents. We will see that geometrically-rich spatial representations emerge when agents employ a trade-off between the minimisation of the Shannon information used to describe locations within the environment and the reduction of the location error generated by the resulting approximate spatial description. In addition, agents do not always need to construct these representations from the ground up, but they can obtain them by refining less precise spatial descriptions constructed previously. Importantly, we find that these can be optimal at both steps of refinement, as guaranteed by the successive refinement principle from information theory. Finally, clusters induced by these spatial representations via the information bottleneck method are able to reflect the environment’s topology without relying on an explicit geometric description of the environment’s structure. Our findings suggest that the fundamental geometric notions possessed by natural agents do not need to be part of their a priori knowledge but could emerge as a byproduct of the pressure to process information parsimoniously. View Full-Text
Keywords: spatial cognition; geometric rate-distortion; information theory; information bottleneck; successive refinement spatial cognition; geometric rate-distortion; information theory; information bottleneck; successive refinement
Show Figures

Figure 1

MDPI and ACS Style

Catenacci Volpi, N.; Polani, D. Space Emerges from What We Know—Spatial Categorisations Induced by Information Constraints. Entropy 2020, 22, 1179. https://doi.org/10.3390/e22101179

AMA Style

Catenacci Volpi N, Polani D. Space Emerges from What We Know—Spatial Categorisations Induced by Information Constraints. Entropy. 2020; 22(10):1179. https://doi.org/10.3390/e22101179

Chicago/Turabian Style

Catenacci Volpi, Nicola, and Daniel Polani. 2020. "Space Emerges from What We Know—Spatial Categorisations Induced by Information Constraints" Entropy 22, no. 10: 1179. https://doi.org/10.3390/e22101179

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop