Brain Scans, Cultural Popularity And Sample Sizes
Editor’s Note: The business of market research is changing rapidly, and nonconscious measurement techniques are growing ever more important as a class of consumer insight techniques. Nonconscious methods are an area exhibiting rapid cycles of innovation and growth. Certainly adoption has been slow for a variety of reasons, but the impact across many categories has been high. In today’s post one of the preeminent applied neuroscientists in the world today, Dr. Thomas Ramsoy, gives a bit more insight into why this is such an important and topic and what traditional market researchers need to understand about the science behind the techniques.
We think this is such an important topic that we’re launching a unique one day event in November that is designed to advance the conversation and increase collaboration among corporate clients, market research consultants and technology providers.
Save the date for the Nonconscious Impact Measurement Forum (#NIMF) on November 6th! Brought to you by GreenBook and the Burke Institute, NIMF is an intensive day of learning and collaboration around the business impact generated by the dynamic and growing field of nonconscious measurement. You will learn the business case for and be inspired by nonconscious measurement methods, including applied neuroscience, implicit approaches, behavioral economics techniques, biometric measurement and holistic nonconscious models, and have the opportunity to test out many methods being discussed for yourself!
Today’s post is a great lead in for the type of bigger conversations that will be happening at NIMF.
By Thomas Ramsoy
One of the key criticisms and concerns one hears towards applied neuroscience and neuromarketing is the sample size. Is it really valid, let alone representative, to test 40 people? In traditional methods, such as surveys, interviews and even focus groups, the number of test persons tested usually runs in the hundreds or thousands. In applied neuroscience tests we usually see test of around 100 people in nation-wide samples. In studies in mobile settings, we can see test with as few as 40 people. Surely, that is a problem?
Maybe the reason that applied neuroscience is not testing hundreds or thousands of people is that it will be too expensive or too hard to scale? Testing a single person with functional Magnetic Resonance Imaging (or fMRI) often costs something like $2,000. Even with other methods such as EEG (electroencephalography), although the price can be down to $100 per person or lower, it may be hard to scale, since every person you test simultaneously requires a full hardware and software setup.
Things have changed dramatically. In several neuroscience studies, it has been found that neuroscience measures can predict behaviour. Even in studies using very small samples, neuroscience can predict not only individual choice, but effects at a cultural level. For example, in a recent study by Dmochowski and colleagues (Dmochowski et al., 2014), brain responses of only 16 participants could predict effects at the cultural level. By using EEG, the researchers developed a novel method that assessed how consistently the brain activation was across the group. Higher group consistency to TV series and commercials was related to both higher social activation (the number of Tweets) as well as stated reference (Nielsen rating).
Other studies have found a similar pattern. In a study by Berns and Moore (2012) the researchers went back to older fMRI data in a study of responses to music. Here, they found that activation of a deep brain structure – the nucleus accumbens – predicted whether the music had become a massive cultural hit, far better than self-reports from the same people. These and other studies have hinted that there may be some common brain activations in a group that is representative for how a whole culture will respond.
So the criticism towards applied neuroscience on sample size is erroneous, and the concerns are unwarranted. One part of the problem with these attitudes is that they assume that traditional research methods are the golden standard. Think of it this way: in traditional methods, each person contributes with only a few data points, as given by their answers. Furthermore, traditional methods only assess measures that a person is privy to, and is willing and able to share. In neuroscience measures, an fMRI scan divides the brain into 3D boxes (voxels) of an approximate size of 3x3x3 millimeters, thus measuring in several thousands of voxels across the brain. Each voxel value is assessed about every other second, and thus a test lasting 20 minutes provides a raw data value of 2.1 million data points per person. In EEG, we see that each electrode samples the signal with a millisecond resolution. This signal is then divided into several frequencies (e.g., alpha, beta, delta, gamma, theta), and therefore a single person can contribute with an order of magnitude more in data power than in fMRI, with 60 million data points or more per person. By merely contrasting the data load of traditional methods and applied neuroscience, we can see that we’re in a completely different ball game.
Crucially, neuroscience measures do not only assess a limited set of responses that a person has conscious access to. These measures provide deep insights into what people are attending and missing; how consumers’ emotional responses occur within milliseconds after seeing a message, a brand or a product; and how particular motivational responses drive attention, desire and ultimately choice.
Today, the scientific foundation of applied neuroscience is unparalleled, and continues to grow both in our insights and application. For those who employ and adapt these methods (with the premise that they are used correctly) consistently see an improved understanding of their consumers, increase their communication effects, and thereby increased their revenue.
Today, it is fair to claim that the obstacles for adopting applied neuroscience now lies not in the science or the methods, but within the market research industry.
Berns, Gregory S, and Sara E Moore. “A Neural Predictor of Cultural Popularity.” Journal of Consumer Psychology 22, no. 1 (2012): doi:10.1016/j.jcps.2011.05.001.
Dmochowski, Jacek P, Matthew A Bezdek, Brian P Abelson, John S Johnson, Eric H Schumacher, and Lucas C Parra. “Audience Preferences Are Predicted by Temporal Reliability of Neural Processing.” Nature communications 5 (2014): doi:10.1038/ncomms5567.