Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation

ABSTRACT

Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.

KEYWORDS:

• Attractor landscape
• complexity
• complex systems
• behaviour change
• dynamics

Acknowledgments

We would like to thank Fred Hasselman for helpful methodological discussions, and Merlijn Olthof for the idea of visualising PCA scores in two-dimensional space.

Disclosure statement

The authors report there are no competing interests to declare.

Data availability statement

Data is available on the GitHub repository online: https://github.com/heinonmatti/attractor-manuscript.

Supplementary materials

The supplementary website describing the methodology to reproduce Figure 5, as well as introducing adaptive trial designs, is at: https://heinonmatti.github.io/attractor-manuscript/

Reporting

All analyses and code are available on the GitHub repository online: https://github.com/heinonmatti/attractor-manuscript.

Author contributions following CRediT

Conceptualization: MTJH & DP & NH & GM & KR, Methodology: MTJH & DP, Software: MTJH, Validation: MTJH, Formal analysis: MTJH, Investigation: MTJH & DP, Resources: MTJH, Data Curation: MTJH, Writing – Original Draft: MTJH & NH, Writing – Review & Editing: MTJH & DP & NH & GM & KR, Visualization: MTJH & DP, Supervision: NH, Project administration: MTJH & NH, Funding acquisition: NH & MTJH.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

M.T.J.H. was supported Academy of Finland (grant number 295765 and 346702) and by Gyllenberg Foundation (grant number 5177). D.P. was supported by the Luxembourg National Research Fund (FNR) PRIDE DTU CriTiCS (10907093), G.M. was supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health: #P20GM109025, K.R. was supported by the US NIH grant 5-P30-CA-046592. N.H. was supported by Academy of Finland (grant number 285283).