Reference : Attractor landscapes: a unifying conceptual model for understanding behaviour change ...
Scientific journals : Article
Social & behavioral sciences, psychology : Multidisciplinary, general & others
Physics and Materials Science
Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation
Heino, Matti [Tampere University]
Proverbio, Daniele mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience >]
Marchand, Gwen [University of Nevada]
Resnicow, Kenneth [University of Michigan]
Hankonen, Nelli [Tampere University]
Health Psychology Review
Routledge, Taylor & Francis Group
United Kingdom
[en] Attractor landscape ; Complexity ; Complex systems ; Behaviour change ; Dynamics
[en] 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.
FnR ; FNR10907093 > Jorge Gonçalves > CriTICS > Critical Transitions In Complex Systems: From Theory To Applications > 01/11/2016 > 30/04/2023 > 2015

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