Functional contextual implementation of an evolutionary, entropy-based, and embodied free energy framework: Utilizing Lagrangian mechanics and evolutionary game theory’s truth vs. fitness test of the veridicality of phenomenological experience - Edwards, Darren J. (Frontiers in Psychology, 2023)
The Bayesian approach of cognitive science largely takes the position that evolution drives perception to produce precepts that are veridical. However, some efforts utilizing evolutionary game theory have shown through evolutionary game theory simulations that perception is more likely based on a fitness function, which promotes survival rather than promoting perceptual truth about the environment. Though these findings do not correspond well with the standard Bayesian approach of cognition, they may correspond with a behavioral functional contextual approach that is ontologically neutral (a-ontological). This approach formalized through a post-Skinnerian account of behaviorism called Relational Frame Theory (RFT) can indeed be shown to correspond well with an evolutionary fitness function, whereby contextual functions form that corresponds to a fitness function interface of the world. This fitness interface approach therefore may help provide a mathematical description for a functional contextual interface of phenomenological experience. Furthermore, this more broadly fits with a neurological active inference approach based on the free-energy principle (FEP) and more broadly Lagrangian mechanics. These assumptions of how fitness beats truth (FBT) and FEP correspond to RFT are then discussed within a broader multi-dimensional and evolutionary framework called the extended, evolutionary, meta-model (EEMM) that has emerged out of the functional contextual behavioral science literature to incorporate principles of cognition, neurobiology, behaviorism, and evolution and are discussed in the context of a novel RFT framework called ‘Neurobiological and Natural Selection Relational Frame Theory’ (N-Frame). This framework mathematically connects RFT to FBT, FEP, and EEMM within a single framework that expands into dynamic graph networking. This is then discussed for its implications of empirical work at the non-ergodic process-based idiographic level as applied to individual and societal level dynamic modeling and clinical work. This discussion is framed within the context of individuals that are described as evolutionary adaptive and conscious (observer-self) agents that minimize entropy and can promote a prosocial society through group-level values and psychological flexibility.