Gibsonian Information: an agent-based paradigm for quantitative information


Conference paper


Bradly Alicea, Daniela Cialfi, Avery Lim, Jesse Parent
Studies in Computational Intelligence, V.V. Klimov, D.J. Kelley, Biologically Inspired Cognitive Architectures 2021, BICA 2021, vol. 1032, Springer, 2021


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APA   Click to copy
Alicea, B., Cialfi, D., Lim, A., & Parent, J. (2021). Gibsonian Information: an agent-based paradigm for quantitative information. In V. V. Klimov & D. J. Kelley (Eds.), Biologically Inspired Cognitive Architectures 2021 (BICA 2021, Vol. 1032). Springer. https://doi.org/10.1007/978-3-030-96993-6_2


Chicago/Turabian   Click to copy
Alicea, Bradly, Daniela Cialfi, Avery Lim, and Jesse Parent. “Gibsonian Information: an Agent-Based Paradigm for Quantitative Information.” In Biologically Inspired Cognitive Architectures 2021, edited by V.V. Klimov and D.J. Kelley. Vol. 1032. BICA 2021. Studies in Computational Intelligence. Springer, 2021.


MLA   Click to copy
Alicea, Bradly, et al. “Gibsonian Information: an Agent-Based Paradigm for Quantitative Information.” Biologically Inspired Cognitive Architectures 2021, edited by V.V. Klimov and D.J. Kelley, BICA 2021, vol. 1032, Springer, 2021, doi:10.1007/978-3-030-96993-6_2.


BibTeX   Click to copy

@inproceedings{bradly2021a,
  title = {Gibsonian Information: an agent-based paradigm for quantitative information},
  year = {2021},
  edition = {BICA 2021},
  journal = {Biologically Inspired Cognitive Architectures 2021},
  publisher = {Springer},
  series = {Studies in Computational Intelligence},
  volume = {1032},
  doi = {10.1007/978-3-030-96993-6_2},
  author = {Alicea, Bradly and Cialfi, Daniela and Lim, Avery and Parent, Jesse},
  editor = {Klimov, V.V. and Kelley, D.J.}
}

Abstract

We propose a new way to quantitatively characterize information: Gibsonian Information (GI). This framework is relevant to both the study of cognitive agents and single cell systems that exhibit cognitive behaviors. GI provides a means to characterize how agents extract information from direct perceptual signals. This differs from existing information theories in two ways. The first involves an emphasis on sensory processing, engagement in collective behaviors, and the dynamic evolution of such interactions. GI is useful for understanding information in terms of agent interactions with naturalistic contexts, and also allows us to distinguish the role of first-order sensory inputs in the context of higher-order representations. This allows us to extend GI to cybernetic and other types of symbolic systems representations. As an alternate way to look at information in nervous systems, GI also emphasizes the role of information content in the relationship between ecology and nervous systems. Along with direct sensory input and simple internal representations, statistical affordances are an important component of transforming environmental signals into GI. Statistical affordances, defined as clustered information that is spatiotemporally dependent perceptual input, facilitates the extraction of GI from the environment. Quantitatively accounting for perceptual information, GI provides a means to measure spatial concentration in addition to being a generalized indicator of nervous system input. To better understand GI as a measurable phenomenon, we characterize three model scenarios: disjoint distributions, contingent action, and coherent movement. All of these cases provide a means to create a differential system between both motion (information) and random noise/stasis (non-information) and between the active sensory channel and information derived from mental representations. By applying this framework to a variety of specific contexts, including a four-channel model of multisensory embodiment, we demonstrate how GI is essential to understanding the full scope of cognitive information processing.