Socio-Emotional Concern Dynamics in a Model of Real-Time Dyadic Interaction: Parent-Child Play in Autism

Abstract

We used a validated agent-based model—Socio-Emotional CONcern DynamicS (SECONDS)—to model real-time playful interaction between a child diagnosed with Autism Spectrum Disorders (ASD) and its parent. SECONDS provides a real-time (second-by-second) virtual environment that could be used for clinical trials and testing process-oriented explanations of ASD symptomatology. We conducted numerical experiments with SECONDS (1) for internal model validation comparing two parental behavioral strategies for stimulating social development in ASD (play-centered vs. initiative-centered) and (2) for empirical case-based model validation. We compared 2,000 simulated play sessions of two particular dyads with (second-by-second) time-series observations within 29 play sessions of a real parent-child dyad with ASD on six variables related to maintaining and initiating play. Overall, both simulated dyads provided a better fit to the observed dyad than reference null distributions. Given the idiosyncratic behaviors expected in ASD, the observed correspondence is non-trivial. Our results demonstrate the applicability of SECONDS to parent-child dyads in ASD. In the future, SECONDS could help design interventions for parental care in ASD.

Mind as “E”nteraction: Towards the Complexity Hypothesis

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Integrating Cybernetic Big Five Theory with the free energy principle: A new strategy for modeling personalities as complex systems

“…Generative modeling in FEP-AI can be complex (Hesp, Smith, et al, 2019;Hesp, Steenbeek, et al, 2019), but the basic setup involves specifying agents (and their situations) as systems of interacting variables and parameters. These variables take the form of mutually referencing matrices, whose entries specify probabilistic mappings between different portions of the generative model.…”

Integrating Cybernetic Big Five Theory with the Free Energy Principle: A new strategy for modeling personalities as complex systems

“…If the system has agentic properties, then these simulations typically take the form of Markov decision processes (MDPs), wherein agents (as generative models) make sequential decisions (i.e., policy selections), often under conditions of incomplete information (so constituting partially observable MDPs, or POMDPs). In this section we will review some of the major components of Generative modeling in FEP-AI can be complex (Hesp, Smith, et al, 2019;Hesp, Steenbeek, et al, 2019), but the basic setup involves specifying agents (and their situations) as systems of interacting variables and parameters. These variables take the form of mutually referencing matrices, whose entries specify probabilistic mappings between different portions of the generative model.…”