Gibsonian Information: an agent-based paradigm for quantitative information

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 first-order sensory inputs in terms of agent interactions with naturalistic contexts and higher-order representations. This allows us to extend GI to cybernetic and other types of symbolic systems representations. 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 (clustered information that is spatiotemporally dependent perceptual input) facilitate extraction of GI from the environment. As a quantitative accounting of perceptual information, GI provides a means to measure a generalized indicator of nervous system input, and can be characterized by three 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). 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.