Model of Narrative Nowness for Neurocinematic Experiments

Cognitive neurosciences have made significant progress in learning about brain activity in situated cognition, thanks to adopting stimuli that simulate immersion in naturalistic conditions instead of isolated artificial stimuli. In particular, the use of films in neuroscientific experiments, a paradigm often referred to as neurocinematics, has contributed to this success. The use of cinematic stimuli, however, has also revealed a fundamental shortcoming of neuroimaging studies: The lack of conceptual and methodological means to handle the viewers’ experience of narrative events in their temporally extended contexts in the scale of full cinematic narrative, not to mention life itself. In order to give a conceptual structure to the issue of temporal contexts, we depart from the neurophenomenological approach to time consciousness by neurobiologist Francisco Varela, which in turn builds on Husserl’s phenomenology of time. More specifically, we will discuss the experience of narrative tension, determined by backward-looking conceptualizing retention, and forward-looking anticipatory protention. Further, this conceptual structure is built into a preliminary mathematical model, simulating the dynamics of decaying and refreshing memory traces that aggregates a retentive perspective for each moment of nowness, which in turn may trigger anticipations for coming events, in terms of Varela and Husserl, protentions. The present tentative mathematical model is constructed using simple placeholder functions, with the intention that they would eventually be replaced by models based on empirical observations on the psychological capabilities that support narrative sensemaking. The final goal is a model that successfully simulates the way how the memory system maintains narrative tension beyond the transient nowness window, and thereby allows mappings to observed brain activity with a rich temporal system of narrative contexts. 1998 ACM Subject Classification G.1.7 Ordinary Differential Equations, I.2.7 Natural Language Processing, H.1.m Models and Principles: Miscellaneous