These are slides of a talk given at the Aalto University Complex Systems seminar. Contrasts two views to changing behaviour; the pathway view and the complexity view, the latter being at its infancy. Presents some Secret Analysis Arts of Recurrence, which Fred Hasselman doesn’t want you to know about. Includes links to resources. If someone perchance saw my mini-moocs (1, 2) and happened to find them useful, drop me a line and I’ll make one of this.
Lifestyle factors are hugely relevant in preventing disease in modern societies; unfortunately people often fail in their attempts to change health behaviour – both their own, as well as that of others’. In recent years, behaviour change design has been conceived as a process where one identifies deficiencies in factors influencing the behaviours (commonly called “determinants”). Complexity thinking suggests putting emphasis on de-stabilisation instead.
The perspective taken here is mostly at the idographic level. At the time of writing, we have behaviour change methods to affect e.g. skills, perceived social norms, attitudes and so forth – but very little on general de-stabilisation of the motivational system as an important predictor of change.
Perspectives are welcome!
ps. Those of you to worry about brainwashing and freedom of thought: Chill. Stuff that powerful doesn’t really exist, and if it did, marketers would know about it and probably rule the world. [No, they don’t rule the world, I’ve been there]
pps. Forgot to put it in the slides, but this guy Merlijn Olthof will perhaps one day tweet about his work about destabilisation in psychotherapy contexts. Meanwhile, you can e.g. be his 10th Twitter follower, or keep checking his Google Scholar profile, as there’s a new piece coming out soon!
For some years, I’ve been partly involved in the Let’s Move It intervention project, which targeted dysfunctional physical activity and sedentary behaviour patterns of older adolescents, by affecting their school environment as well as social and psychological factors.
I held a talk at the closing seminar; it was live streamed and is available here (on stage starting from about 1:57:00 in the recording). But if you were there, or are otherwise interested in the slides I promised, they are now here.
For a demonstration of non-stationary processes (which I didn’t talk about but which are mentioned in these slides), check out this video and an experimental mini-MOOC I made. Another blog post touching on some of the issues is found here.
It was recently brought to my attention that there exist such things as time and context, the flow of which affects human affairs considerably. Then there was this Twitter conversation about what habits actually are. In this post, I try to make sense of how to view health behavioural habits from the perspective of dynamical systems / complexity theory. I mostly draw from this article.
Habits are integral to human behaviour, and arguably necessary to account for in intervention research 1–3. Gardner 1 proposes a definition of habit as not a behaviour but “a process by which a stimulus generates an impulse to act as a result of a learned stimulus-response association”. Processes being seldom stable for all eternity, a complex dynamical systems perspective would propose some consequences of this definition.
What does it mean, when a process—such as habit—is stable? One way of conceiving this is considering the period of stability as a particular state a system can be in, while being subject to change. Barrett 4 proposes four features of dynamic system stability, in which a system’s states depend on the interactions among its components, as well as the system’s interactions with its environment.
First of all, stability always has a time frame, and stabilities at different time frames (such as stability over a month and a year) are interdependent. We ought to consider, how these time scales interact. For example, some factors which determine one’s motivation to go to the gym, such as mood, fluctuate on the scale from minutes to hours. Others may fluctuate on the daily level, and can be influenced by how much one slept the previous night or how stressful one’s workday was, whereas others fluctuate weekly. Then again, some—which increasingly resemble dispositions or personality factors—may be quite stable across decades. When inspecting a health behaviour, we ought to be looking at minimum the process which takes place on a time scale one level faster, and one lever slower than the one we are purportedly interested in 4. For example, how do daily levels of physical activity relate to weekly ones, and how do montly fluctuations affect the weekly fluctuations? Health psychologists could also classify each determinant of a health behaviour, based on the time scale it is thought to operate on. For example, if autonomous forms of motivation 5 seem to predict physical activity quite well cross-sectionally, we could attempt to measure it for a hundred days and investigate what the relevant time-scales of fluctuations are, in relation to those of the target behaviour. Such an exercise could also be helpful for deciding on the sampling frequency of experience sampling studies.
Second, processes in systems such as people have their characteristic attractor landscapes, and these landscapes can possibly be spelled out, along with the criteria associated with them. By attractors I mean here behaviours a person is drawn to, and an attractor landscape is the conglomerate of these behaviours. The cue-structure of the behaviours can be quite elaborate. For example, a person may smoke only, when they have drank alcohol (1) in a loud environment (2), among a relatively large group (3) of relatively unfamiliar people (4), one or two of whom are smokers (5); a situation where it is easier to have a private conversation if one joins another to go out for a cigarette. This highlights how the process of this person’s smoking habit can be very stable (mapping to the traditional conception of “habitual”), while also possibly being highly infrequent.
Note: Each of the aforementioned conditions for this person to smoke are insufficient by themselves, although all are needed to trigger smoking in this context. As a whole, they are sufficient to cause the person to smoke, but not always necessarily needed, because the person may smoke in some more-or-less limited other conditions, too. These conditions can also be called INUS (referring to Insufficient but Necessary criteria of an Unnecessary but Sufficient context for the behaviour) 6. Let that sink in a bit. As a corollary, if a criterion really is necessary, it may be an attractive target for intervention.
Third, the path through which change happens matters, a lot. Even when all determinants of behaviour are at a same value, the outcome may be very different depending on previous values of the outcome. This phenomenon is known as hysterisis, and it has been observed in various fields from physics (e.g. the form of a magnetic field depends on its past) to psychology (e.g. once a person becomes depressed due to excess stress, the stress level must be much lower to switch back to the normal state, than was needed for the shift to depression; 7). As a health behaviour example, just imagine how much easier it is to switch from a consistent training regime to doing no exercise at all, compared to doing it the other way around. Another way to think about is to consider that systems are “influenced by the residual stability of an antecedent regime” 4. As a consequence of stability being “just” a particular type of a path-dependent dynamic process 4,8, we need to consider the history leading up to the period where a habit is active. This forces investigators to consider attractor patterns and sensitivity to initial conditions: When did this stable (or attractor) state come about? If interactions in a system create the state of the system, which bio-psycho-social interactions are contributing to the stable state in question?
Fourth, learning processes such as those happening due to interventions usually affect a cluster of variables’ stabilities, not just one of them. To change habits, we naturally need to consider which changeable processes should be targeted, but it is probably impossible to manipulate these processes in isolation. This has been dubbed the “fat finger problem” (Borsboom 2018, personal communication); trying to change a specific variable, like attempting to press a specific key on the keyboard with gloves on, almost invariably ends up affecting neighbouring variables. Our target is dynamic and interconnected, often calling for coevolution of the intervention and the intervened.
It is obvious that people can relapse to their old habitual (attractor) behaviour after an intervention, and likely that extinction, unlearning and overwriting of cue-response patterns can help in breaking habits, whatever the definition. But the complex dynamics perspective puts a special emphasis on understanding the time scale and history of the intervenable processes, as well as highlighting the difficulty of changing one process while holding others constant, as the classical experimental setup would propose.
I would be curious of hearing thoughts about these clearly unfinished ideas.
Gardner, B. A review and analysis of the use of ‘habit’ in understanding, predicting and influencing health-related behaviour. Health Psychol. Rev.9, 277–295 (2015).
Wood, W. Habit in Personality and Social Psychology. Personal. Soc. Psychol. Rev.21, 389–403 (2017).
Wood, W. & Rünger, D. Psychology of Habit. Annu. Rev. Psychol.67, 289–314 (2016).
Barrett, N. F. A dynamic systems view of habits. Front. Hum. Neurosci.8, (2014).
Ryan, R. M. & Deci, E. L. Self-determination theory: Basic psychological needs in motivation, development, and wellness. (Guilford Publications, 2017).
Mackie, J. L. Causes and Conditions. Am. Philos. Q.2, 245–264 (1965).
Cramer, A. O. J. et al. Major Depression as a Complex Dynamic System. PLoS ONE11, (2016).
Roe, R. A. Test validity from a temporal perspective: Incorporating time in validation research. Eur. J. Work Organ. Psychol.23, 754–768 (2014).
Slides below are a presentation of what I was up to in 2016.
Don’t pay too much attention, it’s not 2016 any more.
Currently (September 2019), I’m part of the Behaviour Change and Wellbeing group of the University of Helsinki, working on my doctoral dissertation on complex systems approaches to changing health behaviour, and planning a self-leadership intervention to improve occupational health.
In this post I try to answer the call for increased transparency in psychological science by presenting my master’s thesis. I ask for feedback about the idea and the methods. I’d also appreciate suggestions for which journal it might be wise to submit the paper I’m now starting to write with co-authors. Check out OSF for the Master’s thesis documents and a supplementary website for analyses in the manuscript in preparation (I presented the design analysis in a previous post).
In my previous career as a marketing professional, I was often enchanted by news about behavioral science. Such small things could have such large effects! When I moved into social psychology, it turned out that things weren’t quite so simple.
One study that intrigued me was done in the 70’s, and has since gained huge publicity (see here and here, for examples). The basic story is, that you could use the word because to get people to do things, due to a learned “reason → compliance” link.
Long story short, I was able to experiment in a within-trial setting of a health psychology intervention. Here’s a slideshow adapted from what I presented in the annual conference of the European Health Psychology Society:
Maintaining a Bayes Factor / p-value ratio of about 1:2. It’s not “a B for every p“, but it’s a start…
Learning basic R and redoing all analyses in the last minute, so I wouldn’t have to mention SPSS 🙂
Figuring out how this pre-registration thing works, and registering before end of data collection.
Using the word “significant” only twice and not in the context of results.
Things I’m not happy about:
Not having pre-registered before starting data collection.
Not knowing what I now know, when the project started. Especially about theory formation and appraisal (Meehl).
Not having an in-depth understanding of the mathematics underlying the analyses (although math and logic are priority items on my stuff-to-learn-list).
Not having the data public… yet. It will be in 2017 the latest, but hopefully already this autumn.
A key factor for fixing psychological science is transparency; making analyses, intentions and data available for all researchers. As a consequence, anyone can point out inconsistencies and use the findings to elaborate on the theory, making accumulation of knowledge possible.
Science is all about predicting, and everyone knows how anyone can say “yeah, I knew that’d happen”. The most impressive predictions are those made well before things start happening. So don’t be like me, and pre-register your study before the start of data collection. It’s not as hard as it sounds! For clinical trials, this can be done for free in the WHO-approved German Clinical Trials Register (DRKS). For all trials, the Open Science Framework (OSF) website can be used for pre-registering plans and protocols, as well as making study data available for researchers everywhere.There’s also an extremely easy-to-use pre-registration site AsPredicted.
One can also use the OSF website as a cloud server to privately manage one’s workflow (for free). As a consequence, automated version control protects the researcher in the case of accusations of fraud or questionable research practices.