When is expectation not a confound? On the necessity of active controls.
Learning and plasticity are hot topics in neuroscience. Whether exploring old world wisdom or new age science fiction, the possibility that playing videogames might turn us into attention superheroes or that practicing esoteric meditation techniques might heal troubled minds is an exciting avenue for research. Indeed findings suggesting that exotic behaviors or novel therapeutic treatments might radically alter our brain (and behavior) are ripe for sensational science-fiction headlines purporting vast brain benefits. For those of you not totally bored of methodological crisis, here we have one brewing anew. You see the standard recommendation for those interested in intervention research is the active-controlled experimental design. Unfortunately in both clinical research on psychotherapy (including meditation) and more Sci-Fi areas of brain training and gaming, use of active controls is rare at best when compared to the more convenient (but causally ineffective) passive control group. Now a new article in Perspectives in Psychological Science suggests that even standard active controls may not be sufficient to rule out confounds in the treatment effect of interest.
Why is that? And why exactly do we need active controls in the first place? As the authors clearly point out, what you want to show with such a study is the causal efficacy of the treatment of interest. Quite simply what that means is that the thing you think should have some interesting effect should actually be causally responsible for creating that effect. If you want to argue that standing upside down for twenty minutes a day will make me better at playing videogames in Australia, it must be shown that it is actually standing upside down that causes my increased performance down under. If my improved performance on Minecraft Australian Edition is simply a product of my belief in the power of standing upside down, or my expectation that standing upside down is a great way to best kangaroo-creepers, then we have no way of determining what actually produced that performance benefit. Research on placebos and the power of expectations shows that these kinds of subjective beliefs can have a big impact on everything from attentional performance to mortality rates.
Typically researchers attempt to control for such confounds through the use of a control group performing a task as similar as possible to the intervention of interest. But how do we know participants in the two groups don’t end up with different expectations about how they should improve as a result of the training? Boot et al point out that without actually measuring these variables, we have no idea and no way of knowing for sure that expectation biases don’t produce our observed improvements. They then provide a rather clever demonstration of their concern, in an experiment where participants view videos of various cognition tests as well as videos of a training task they might later receive, in this case either the first-person shooter Unreal Tournament or the spatial puzzle game Tetris. Finally they asked the participants in each group which tests they thought they’d do better on as a result of the training video. Importantly the authors show that not only did UT and Tetris lead to significantly different expectations, but also that those expectation benefits were specific to the modality of trained and tested tasks. Thus participant who watched the action-intensive Unreal Tournament videos expected greater improvements on tests of reaction time and visual performance, whereas participants viewing Tetris rated themselves as likely to do better on tests of spatial memory.
This is a critically important finding for intervention research. Many researchers, myself included, have often thought of the expectation and demand characteristic confounds in a rather general way. Generally speaking until recently I wouldn’t have expected the expectation bias to go much beyond a general “I’m doing something effective” belief. Boot et al show that our participants are a good deal cleverer than that, forming expectations-for-improvement that map onto specific dimensions of training. This means that to the degree that an experimenter’s hypothesis can be discerned from either the training or the test, participants are likely to form unbalanced expectations.
The good news is that the authors provide several reasonable fixes for this dilemma. The first is just to actually measure participant’s expectations, specifically in relation to the measures of interest. Another useful suggestion is to run pilot studies ensuring that the two treatments do not evoke differential expectations, or similarly to check that your outcome measures are not subject to these biases. Boot and colleagues throw the proverbial glove down, daring readers to attempt experiments where the “control condition” actually elicits greater expectations yet the treatment effect is preserved. Further common concerns, such as worries about balancing false positives against false negatives, are address at length.
The entire article is a great read, timely and full of excellent suggestions for caution in future research. It also brought something I’ve been chewing on for some time quite clearly into focus. From the general perspective of learning and plasticity, I have to ask at what point is an expectation no longer a confound. Boot et al give an interesting discussion on this point, in which they suggest that even in the case of balanced expectations and positive treatment effects, an expectation dependent response (in which outcome correlates with expectation) may still give cause for concern as to the causal efficacy of the trained task. This is a difficult question that I believe ventures far into the territory of what exactly constitutes the minimal necessary features for learning. As the authors point out, placebo and expectations effects are “real” products of the brain, with serious consequences for behavior and treatment outcome. Yet even in the medical community there is a growing understanding that such effects may be essential parts of the causal machinery of healing.
To what extent might this also be true of learning or cognitive training? For sure we can assume that expectations shape training outcomes, otherwise the whole point about active controls would be moot. But can one really have meaningful learning if there is no expectation to improve? I realize that from an experimental/clinical perspective, the question is not “is expectation important for this outcome” but “can we observe a treatment outcome when expectations are balanced”. Still when we begin to argue that the observation of expectation-dependent responses in a balanced design might invalidate our outcome findings, I have to wonder if we are at risk of valuing methodology over phenomena. If expectation is a powerful, potentially central mechanism in the causal apparatus of learning and plasticity, we shouldn’t be surprised when even efficacious treatments are modulated by such beliefs. In the end I am left wondering if this is simply an inherent limitation in our attempt to apply the reductive apparatus of science to increasingly holistic domains.
Please do read the paper, as it is an excellent treatment of a critically ignored issue in the cognitive and clinical sciences. Anyone undertaking related work should expect this reference to appear in reviewer’s replies in the near future.
Professor Simons, a co-author of the paper, was nice enough to answer my question on twitter. Simons pointed out that a study that balanced expectation, found group outcome differences, and further found correlations of those differences with expectation could conclude that the treatment was causally efficacious, but that it also depends on expectations (effect + expectation). This would obviously be superior to an unbalanced designed or one without measurement of expectation, as it would actually tell us something about the importance of expectation in producing the causal outcome. Be sure to read through the very helpful FAQ they’ve posted as an addendum to the paper, which covers these questions and more in greater detail. Here is the answer to my specific question:
What if expectations are necessary for a treatment to work? Wouldn’t controlling for them eliminate the treatment effect?
No. We are not suggesting that expectations for improvement must be eliminated entirely. Rather, we are arguing for the need to equate such expectations across conditions. Expectations can still affect the treatment condition in a double-blind, placebo-controlled design. And, it is possible that some treatments will only have an effect when they interact with expectations. But, the key to that design is that the expectations are equated across the treatment and control conditions. If the treatment group outperforms the control group, and expectations are equated, then something about the treatment must have contributed to the improvement. The improvement could have resulted from the critical ingredients of the treatment alone or from some interaction between the treatment and expectations. It would be possible to isolate the treatment effect by eliminating expectations, but that is not essential in order to claim that the treatment had an effect.
In a typical psychology intervention, expectations are not equated between the treatment and control condition. If the treatment group improves more than the control group, we have no conclusive evidence that the ingredients of the treatment mattered. The improvement could have resulted from the treatment ingredients alone, from expectations alone, or from an interaction between the two. The results of any intervention that does not equate expectations across the treatment and control condition cannot provide conclusive evidence that the treatment was necessary for the improvement. It could be due to the difference in expectations alone. That is why double blind designs are ideal, and it is why psychology interventions must take steps to address the shortcomings that result from the impossibility of using a double blind design. It is possible to control for expectation differences without eliminating expectations altogether.