Modeling the resituation of memory in neurobiology and narrative

In narrative and neurobiological processes, a common response to an unexpected event can be observed: retroactive reinterpretation. In this activity, an established state of knowledge is restructured so that its ability to interpret causal consequences changes. We present a new graphical knowledge modeling technique to track the stages of retroactive reinterpretation in both narrative and biological domains. This method is based on situation-theoretic foundations, which have been extended using narrative devices to capture elusive properties of everyday reasoning, such as context and causal anticipation. The method and its accompanying visual model enables us to experiment with representational reasoning about cause, shifts in influence between distinct systems and implicit knowledge. This work-in-progress indicates that cross-system, multi-ontology intelligence processes can be modeled using narrative mechanisms. A future goal is to use this method to address problems in ontological interoperability for predictive, multi-system neurobiological modeling. Capturing implicit causal agency in biology using a narrative-based model is thus feasible but comes with challenges in graphical display, which are discussed.