This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
There is a growing realization within the behavioral and brain sciences that the embodiment and situatedness of intelligent agents plays an essential role in their behavior. However, it is still a significant open challenge to understand the complex interactions between an agent's nervous system, its body and its environment. This project focuses on mathematical methods for analyzing the flow of information in models of situated and embodied cognitive agents. Specifically, the investigator will (1) develop new information-theoretic tools that characterize the flow of information over time throughout the system, (2) test and refine these methods on evolved model brain-body-environment systems, and (3) use these techniques and models to explore the unique advantages of embodiment and situatedness for a cognitive agent.
The new analysis techniques build on the notion of conditional mutual information between random variables at specific points in time. For example, conditioning the mutual information between a stimulus feature and a state variable at time t on the information that state variable contains about the stimulus feature at time t-1 allows one to compute a measure of information gain. Similar measures can be used to compute the information loss or retention. This basic approach will be extended in several different directions. Information factoring, which builds on the notion of transfer entropy, will allow the interactions between system components to be characterized in terms of directional information flow. Information backtracking will offer a refined portrait of the structure of these interactions by tracing backwards in time from particular informational configurations of the system to determine the flows that produce them. Specific information spectra will be used to explore the informational relationships between particular values taken on by components of the system, allowing the structure of their interactions to be probed at a finer level of detail. These methods will be applied to evolved models of relational categorization, referential communication, and visually guided behavior, allowing the following questions to be explored: How do embodied agents extract, store, and suppress information? How do embodied agents integrate information about multiple features? What is the relationship between informational and dynamical properties? Finally, these techniques will be used to characterize the capabilities of embodied and situated agents, including information self-structuring, information offloading, and embodied information transfer. These analysis methods are expected to have applications not only to brain-body-environment systems, but also to other complex biological and social networks.
