A Primer on Gibsonian Information
<|reference_start|>A Primer on Gibsonian Information: Across the scientific literature, information measurement in the nervous system is posed as a problem of information processing internal to the brain by constructs such as neuronal populations, sensory surprise, or cognitive models. Application of information theory in the nervous system has focused on measuring phenomena such as capacity and integration. Yet the ecological perspective suggests that information is a product of active perception and interactions with the environment. Here, we propose Gibsonian Information (GI), relevant to both the study of cognitive agents and single cell systems that exhibit cognitive behaviors. We propose a formal model of GI that characterizes how agents extract environmental information in a dynamic fashion. GI demonstrates how sensory information guides information processing within individual nervous system representations of motion and continuous multisensory integration, as well as representations that guide collective behaviors. GI is useful for understanding first-order sensory inputs in terms of agent interactions with naturalistic contexts and simple internal representations and can be extended to cybernetic or symbolic representations. Statistical affordances, or 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. 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.<|reference_end|>
@article{alicea2024a,
    title={A Primer on Gibsonian Information},
    author={Bradly Alicea, Daniela Cialfi, Avery Lim, and Jesse Parent},
    journal={arXiv preprint arXiv:2403.18829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18829},
    primaryClass={q-bio.NC cs.HC}
}