Today we’re going to consider the synthesis of Assembly Theory and the HDML model of RFT as a robust framework for (1) analyzing the complexity, dynamics, and causality underlying relational processes, and (2) opening new avenues for theoretical and conceptual refinement and practical applications. When applying Assembly Theory to the Hyper-Dimensional, Multi-Level (HDML) model of Relational Frame Theory (RFT), it is essential to examine the interplay between the principles of complexity, compositionality (the principle that the meaning or function of a complex system, structure, or expression is determined by the meanings or functions of its constituent parts and the rules used to combine them), and causation in both frameworks. Below is a conceptual integration that respects those aspects of both theories:
Assembly Theory (AT) Overview
Assembly Theory proposes that complex objects or phenomena can be understood in terms of the sequence and number of steps required to assemble or evolve a particular complexity. Two central concepts of AT are compositional complexity and causal structure. In short, these are the (1) number of components and interactions required to create a structure, and (2) pathways through which components are combined and evolved over time.
HDML and Relational Frame Theory (RFT) Overview
The HDML model in RFT expands the traditional understanding of relational responding by introducing (1) Multiple Levels of dynamical processes that evolve with a context and across micro (momentary interactions), meso (extended sequences), and macro (developmental and historical) timescales; and (2) Hyper-Dimensional networks that define cognitive spaces, emphasizing the flexibility and variability of relational patterns.
Integrating Assembly Theory with the HDML Model
Compositional Complexity in Relational Frames. One central measure within Assembly Theory’s is a measure of the minimal steps required to construct a given entity; the "assembly index" (AI). Importantly, this measure can be mapped onto relational frames; where relational frames are complex, multi-component assemblies involving bidirectional, combinatorial, entailable, and contextually controlled relations. For instance, a derived relational network (e.g., "A is larger than B; B is larger than C; thus, A is larger than C") has a clear compositional structure that reflects both simplicity (core relational rules) and complexity (scale variance and interconnections).
Hyper-Dimensional Assembly Pathways. HDML’s notion of hyper-Dimensional relational networks aligns with Assembly Theory’s causal structure where the pathways for assembling complex relational networks in HDML can be viewed as assembly trajectories. These include the dynamic integration of sensory, symbolic, and contextual stimulus control (“information”) within continuous and across multiple timescales. In addition, HDML suggests that frames emerge dynamically depending on prior experiences, environmental contingencies, and symbolic (Verbal Behavioral) interactions. Assembly Theory models these as branching paths in a causal assembly space.
Quantifying and Measuring Assembly and Relational Complexity, The quantitative tools of AT, such as measuring the assembly index, can be adapted to quantify the density and depth of relational frames within and across the scale levels of the HDML model. For example, relational frame complexity (e.g., hierarchical relations vs. equivalence relations) could be assessed by calculating the number of steps and transformations needed to form such relations as both elemental and emergent structures.
Temporal Dynamics and Multi-Level Assembly. HDML’s multi-level nature maps onto Assembly Theory's emphasis on temporal dynamics. For example, at the micro-level, relational responding involves rapid, moment-to-moment assemblies (e.g., applying contextual cues to derive new relations). Where, at the meso-level, sequences of relational frames can be understood as extended assembly chains, reflecting causal trajectories. And finally, at the macro-level, developmental shifts (e.g., the emergence of abstract symbolic reasoning) emerge from cumulative assembly processes over time.
Causal Emergence in Relational Framing. Assembly Theory’s causal narrative supports the idea of emergence in HDML. Higher-order relational abilities (e.g., analogical reasoning) emerge as novel assemblies from simpler relational components, with the history of interaction functioning as the assembly scaffold.
Implications for Research and Application.
There are three immediate areas of application of AT to the advancement of HDML. The first involves the evolution of experimental studies: HDML dynamics could be studied using assembly measures to quantify how relational complexity evolves across different tasks or contexts. The second moves our focus to the advancement of behavioral Interventions, where insights from Assembly Theory could inform interventions by identifying and targeting key assembly steps that underpin relational complexity and other primary and higher level learning challenges and deficits. The third could promote the modeling “cognitive flexibility” where relational flexibility arises from the dynamic assembly of frames, offering new ways to simulate cognitive/verbal behavioral and complex stimulus control processes computationally.