Extracting Environmental Information: A Formal Model of Gibsonian Information Processing

Core Concepts

Gibsonian Information (GI) provides a quantitative framework for understanding how agents extract and process environmental information through active perception and interactions with their surroundings.

Abstract

The article proposes a formal model of Gibsonian Information (GI), which characterizes how agents extract environmental information in a dynamic fashion. GI is inspired by an ecological perspective and emphasizes the role of information content in the relationship between ecology, nervous systems, and naturalistic interactions.

Key highlights:

• GI assumes an embodied observer, alignment among multiple sensory modalities, and coordinated collective behavior.
• GI involves the evaluation of exponential spatiotemporal distributions (which reveal statistical affordances) by a mathematical or symbolic model.
• GI provides a means to measure a generalized indicator of nervous system input, characterized by three scenarios: disjoint distributions, contingent action, and coherent movement.
• GI can be applied to a variety of agents, from multicellular organisms with nervous systems to biological cells, computational agents, and multi-agent collectives.
• GI provides an alternative to Shannon information theory, emphasizing the observer-centric and affordance-based aspects of perceptual information.
• GI can be used to understand active perception as a universal phenomenon and apply it across different degrees of complexity.

Stats

"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."
"Yet the ecological perspective suggests that information is a product of active perception and interactions with the environment."
"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."
"Statistical affordances, or clustered information that is spatiotemporally dependent perceptual input, facilitate extraction of GI from the environment."

Quotes

"GI provides a unique interpretation of information theory in addition to an alternative framework that stresses the observer-centric and affordance-based aspects of perceptual information."
"GI is an inherently multisensory approach, and often relies upon synchronized multisensory integration."
"GI is broadly applicable to the sensory worlds of individuals and social groups. The influence of GI on social groups may also include the ability of agents to interpret their place in collectives such as schools, herds, flocks, and swarms."

Key Insights Distilled From

A Primer on Gibsonian Information

by Bradly Alice... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.18829.pdf

Deeper Inquiries

How can the principles of Gibsonian Information be applied to understand information processing in non-neuronal biological systems, such as single-celled organisms or plant life?

In non-neuronal biological systems, the principles of Gibsonian Information can be applied to understand how these organisms interact with and perceive their environment. For single-celled organisms, GI can help in analyzing how they respond to external stimuli and navigate their surroundings. By considering the organism as an embodied observer, GI can shed light on how these organisms extract information from their environment to make decisions and exhibit behaviors. For example, single-celled organisms may exhibit coherent movement in response to chemical gradients or light sources, demonstrating an active perception of their surroundings. In plant life, GI can be used to study how plants sense and respond to environmental cues such as light, gravity, and touch. By considering plants as agents that interact with their surroundings, GI can provide insights into how plants detect and utilize information for growth, development, and survival.

What are the potential limitations or drawbacks of the Gibsonian Information framework compared to traditional information theory approaches, and how might these be addressed?

One potential limitation of the Gibsonian Information framework compared to traditional information theory approaches is the lack of formal mathematical rigor in defining and quantifying information. While GI emphasizes the role of active perception and embodied interactions with the environment, it may face challenges in providing precise and quantitative measures of information content. To address this limitation, researchers could work on developing more robust mathematical models and metrics within the GI framework to quantify information processing accurately. Additionally, incorporating elements from traditional information theory, such as entropy and mutual information, into the GI framework could enhance its analytical capabilities and make it more comparable to established information theory approaches.

In what ways could the Gibsonian Information model be extended or adapted to better account for the role of attention, memory, and higher-level cognitive processes in shaping an agent's interactions with its environment?

To better account for the role of attention, memory, and higher-level cognitive processes in shaping an agent's interactions with its environment, the Gibsonian Information model could be extended in several ways. Firstly, incorporating mechanisms for selective attention within the GI framework would allow for the prioritization of relevant environmental information and the filtering out of irrelevant stimuli. This could involve introducing attentional weights or biases to different sensory inputs based on their salience or importance. Secondly, integrating memory processes into the GI model would enable agents to store and retrieve past experiences, influencing their current perception and behavior. By including memory mechanisms, agents could learn from previous interactions with the environment and adapt their responses accordingly. Lastly, expanding the GI framework to include higher-level cognitive processes such as decision-making, problem-solving, and planning would provide a more comprehensive understanding of how agents navigate and interact with complex environments. By incorporating these elements, the GI model can capture the dynamic and adaptive nature of information processing in cognitive agents.