Abstract
This paper proposes a formal reconstruction of the script construct by leveraging the active inference framework, a behavioral modeling framework that casts action, perception, emotions, and attention as processes of (Bayesian or variational) inference. We propose a first principles account of the script construct that integrates its different uses in the behavioral and social sciences. We begin by reviewing the recent literature that uses the script construct. We then examine the main mathematical and computational features of active inference. Finally, we leverage the resources of active inference to offer a formal model of scripts. Our integrative model accounts for the dual nature of scripts (as internal, psychological schema used by agents to make sense of event types and as constitutive behavioral categories that make up the social order) and also for the stronger and weaker conceptions of the construct (which do and do not relate to explicit action sequences, respectively).
Highlights
How are humans able to navigate social situations? As social agents, we take for granted that we can and do modulate our behavior as a function of what is socially acceptable in certain kinds of situations
We show that the modeling resources of active inference can be used to derive a formal construct of script that encompasses the various readings in the literature
Gagnon and Simon’s sexual script theory addresses the multiple scales at which scripts are enacted, which is in line with out model of scripts based on active inference
Summary
How are humans able to navigate social situations? As social agents, we take for granted that we can and do modulate our behavior as a function of what is socially acceptable in certain kinds of situations. Active inference says that perception, learning, cognition, and action all function to minimize an information theoretic quantity called variational free-energy (Friston et al, 2017a; Friston, 2019). This variational free-energy was first developed in the context of complex statistical inference to finesse intractable inference problems (Feynman, 1972). Instead of computing the distribution directly, variational inference allows us to write down a guess about this distribution (the variational or recognition model); variational inference methods to finesse this guess by changing its parameters (i.e., its shape) until it becomes close enough to the target distribution This closeness is obtained by minimizing a variational (free-energy) bound on the evidence for our (the brain’s) models or hypotheses about how (sensory) data were caused
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