Formalizing psychological interventions through network control theory

Abstract

Despite the growing deployment of network representation throughout psychological sciences, the question of whether and how networks can systematically describe the effects of psychological interventions remains elusive. Towards this end, we capitalize on recent breakthrough in network control theory, the engineering study of networked interventions, to investigate a representative psychological attitude modification experiment. This study examined 30 healthy participants who answered 11 questions about their attitude toward eating meat. They then received 11 arguments to challenge their attitude on the questions, after which they were asked again the same set of questions. Using this data, we constructed networks that quantify the connections between the responses and tested: 1) if the observed psychological effect, in terms of sensitivity and specificity, relates to the regional network topology as described by control theory, 2) if the size of change in responses relates to whole-network topology that quantifies the “ease” of change as described by control theory, and 3) if responses after intervention could be predicted based on formal results from control theory. We found that 1) the interventions that had higher regional topological relevance (the so-called controllability scores) had stronger effect (r > 0.5), the intervention sensitivities were systematically lower for the interventions that were “easier to control” (r = -0.49), and that the model offered substantial prediction accuracy (r = 0.36). Finally, we compared results based on subjective as well as data-driven models and found that, across all tests, the results from the data-driven approach explained a larger size of effect. Taken together, our results suggest that psychological interventions can be studied using network control theory and that data-driven models provide a reliable medium.

Integrating multiple time-scales to advance relationship science

“…However, there is still room for even more creativity in the systems-level and multiscale approaches that can be used to further advance the field of relationship science. For inspiration, it may be that analogies can be formed by looking to past and cutting-edge models in other scientific fields (see e.g., Benson et al, 2018; Boker, 2015; Deboeck et al, 2009; Ram et al, 2014; Reeves et al, 2020; Stocker et al, 2022).…”

Conceptualizing psychological well‐being as a dynamic process: Implications for research on mobile health interventions

“…Capturing PWB change as a process over short period of time (proximal outcomes) and over long periods of time In order to comprehensively understand the dynamics of PWB, it is imperative to employ a statistical model that is capable of capturing both rapid timescale fluctuations and slower, long-term changes (Stocker et al, 2023). For addressing rapid timescale dynamics, Autoregressive (AR) models or Continuous-Time Stochastic DE models (e.g., Oravecz et al, 2011) are generally considered.…”

Novel Approaches towards Studying Change: Implications for Understanding and Treating Psychopathology

“…[47][48][49] It enables the quantitative assessment and prediction about the reaction of the entire network and its nodes to both endogenous and exogenous input signals, dynamics over time, and identifies nodes that control the organization of the system. 50,51 The controllability of a network refers to the ability to manipulate network components in order to control the system along a desired trajectory, i.e. to bring it to a desired target state.…”

“…Therefore, it allows for the investigation and evaluation of the effects of psychological interventions on the overall condition of an individual. 51 The ability of a network node to control the rest of the system can be described by the metrics average controllability, 52 modal controllability, 53 and boundary controllability. 50 Control theory combined with longitudinal data not only provides a comprehensive understanding of the changes in networked systems but also identifies the mechanisms of change driving psychological interventions and their relation to system changes.…”

“…As an example, this methodology has been used to study the effect of an attitude modification intervention through a dynamic network and control approach. 51 Using Dynamic Network Modeling the relationship between topological network features and alterations in the network were examined over time. This approach allowed the researchers to test the effect of the intervention in terms of its sensitivity and specificity and their relationship to the response trajectories that control theory predicts.…”