Feature: Neuroscience Techniques and Best Practice #1

Continuing our focus on 'Measuring Brains and Bodies', HCD Research's Michelle Niedziela runs through the key techniques in 'neuro', with advice on where to use them. The second part of the article, looking at Best Practices, will appear in DRNO when the pdf special issue is published early next week.


Michelle Murphy Niedziela, PhD is a behavioral neuroscience expert in neuropsychology, psychology and consumer science. Experienced from academia (Monell Chemical Senses Center) and industry (Johnson & Johnson, Mars Chocolate) in R&D of innovation technologies and methodologies for consumer research. As VP of Research and Innovation at HCD Research, Michelle focuses on integrating applied consumer neuroscience tools with traditional methods used to measure consumer response.


Neuroscientific approaches have become increasingly important in understanding how our bodies respond emotionally and physically to experiences while helping researchers better understand unconscious motivators and emotional reactions. Real and thoughtful applied neuroscience is about using the right combination of sensitive measures from psychology and neuroscience in the most appropriate ways.

Consumer neuroscience, also known as neuromarketing, has seen its fair share of difficulties, ranging from a push of pseudoscientific claims to outlandish, unrealistic costs. Most of its troubles can be attributed to a misunderstanding of the science combined with a reliance on trusting researchers who push the limits of the tools and technologies. In this article, we hope to give some guidance on what consumer neuroscience is (and isn't) as well as best practices for adding it to consumer research.

Each tool within consumer neuroscience has its strengths and weaknesses depending on where and when it is applied, and leads to different understanding of the consumer experience . The key to using them successfully lies in using the right tool for the right research question.


The Neuro-Toolbox

Researchers interested in using neuro-tools are often looking to uncover implicit or non-conscious emotional reactions from consumers. Given the complex nature of emotion, finding a comprehensive methodology to measure this phenomenon is challenging. Although the literature lacks a definition of emotion, multiple components such as physiological arousal, motivation, expressive motor behavior, action tendencies and subjective feelings have widespread acceptance (Scherer, 2005). Yet, the information collected from these tools, especially when used as a singular measurement, is limited and can only emphasize specific components of the overall experience which result in an emotion.

Certain consumer experiences may be better captured by physiological and behavioural measures of the autonomic nervous system (ANS) than by traditional sensory surveys. Physiological measures have been used extensively to capture responses of the ANS to various types of stimuli such as film clips, personalized recall of specific situations, and odours. Among these, the 'gold standard' techniques - fEMG (facial electromyography), HRV (heart rate variability) and GSR (galvanic skin response) - have this title due to their simplicity and direct correlation to what they measure. One regular use of such measures is to examine the processes related to cognition and emotion evoked during media exposure (Bolls et al., 2019; Potter & Bolls, 2012; Ohme, et al., 2011); while dynamic physiological responses measured over time have been studied widely for their role in in the emotional experience (Ellsworth & Scherer, 2003). For example, increases in GSR are directly and positively correlated to increases in arousal, HRV is directly correlated to changes in attention and relaxation, and fEMG is directly correlated to changes in emotional valence (positive or negative emotional response).

Two other techniques, EEG (electroencephalography) and fMRI (functional magnetic resonance imaging), are more consistent with stereotypical neuroscience research, measuring brain activity more directly. An EEG is a non-invasive method to record the electrical activity along the scalp for measuring brain states (Nunez and Srinivasan, 2006). fMRI is a neuroimaging procedure which is frequently associated with exploring memories through brain activity, and works by measuring changes in magnetization between oxygen-rich and oxygen-poor blood (Singleton, 2009). While these tools have been wonderful in academia, their application in industry research is often plagued with improper research design. For example, extrapolating emotional conclusions from EEG or fMRI work typically requires evoking the reactions, not passive measurement. This step has often been skipped in industry, making the conclusions hazy at best and totally false at worst. Further, fMRI studies are notoriously expensive and difficult to perform in the confines of consumer research. Additionally, product quality varies, mostly differing in the number and quality of electrodes used. Cheaper EEG headsets can be exceedingly unreliable, usually because of poorer signal, and thus make it more challenging to analyze already difficult-to-interpret results. Research on cognition within neuroscience, whether using fMRI or an EEG, does not have the capabilities of peering into an individual's thoughts. Like most neurotechnology, information or scans from an fMRI are not to blame for the exaggerated findings researchers are reporting. Researchers, as well as those peer-reviewing new studies, must be held accountable for ensuring limitations and improper use of certain tools are highlighted so readers have clarity on every method's purpose and value.

More behavioural measures such as eye tracking (direct measure of gaze behaviour), and implicit reaction measures are directly correlated with association and may be useful to explore reactions that consumers have difficulty self-reporting (i.e., what visual is most attractive or what concepts fit the brief best). However, eye tracking and implicit reaction are slightly less reliable because of misinterpretation and misuse. Far too often, eye tracking behavior is attributed to attention, even 'though it is possible to look at something but not be paying attention. Similarly, improper design in implicit reaction studies also makes results less reliable.

Another popular neuro- tool, facial coding, is easy and cheap to use, but not nearly as useful as it is sold to be. Proponents often neglect to reveal facial coding's limitations, like socially driven reactions, dropout rates, interpretations, etc. A study from Soussignan & Schall (1996) revealed facial responses are flexible and able to reorganize to accommodate different situations and support the emotional and communicative functions of human facial behaviour. Meaning it is not always clear whether you are measuring a true emotional reaction or simply a mirrored response to some other influencing factor.

Once again, it's important to note that any shortcomings of the approach are not the fault of the measures. It is perfectly reasonable to use any one of these measures as long as you are clear on all the limitations AND use them properly. Ultimately, no one tool will cover all research; therefore, we must be willing to accept that certain tools are better at collecting specific types of information over other tools. Different research questions and settings require different methodologies and technologies. However, the research marketplace for applied neuroscience can be a murky place.


Next Week: Best Practice Guidelines

Citations:

Bolls, P. D., Weber, R., Lang, A., & Potter, R. F. (2019). Media psychophysiology and neuroscience: Bringing brain science into media processes and effects research. Media effects: Advances in theory and research, 195-210.
Ellsworth, P. C., & Scherer, K. R. (2003). Appraisal processes in emotion. Oxford University Press.
Nunez, P. L., & Srinivasan, R. (2006). Electric fields of the brain: the neurophysics of EEG. Oxford University Press, USA.
Ohme, R., Matukin, M., & Pacula-Lesniak, B. (2011). Biometric Measures for Interactive Advertising Research. Journal of Interactive Advertising, 11(2), 60-72.
Potter, R., & Bolls, P. (2012). Psychophysiological measurement and meaning cognitive and emotional processing of media. Routledge
Scherer, K. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695-729.
Singleton M. J. (2009). Functional Magnetic Resonance Imaging. The Yale Journal of Biology and Medicine, 82(4), 233.
Soussignan, R., & Schall, B. (1996). Children's facial responsiveness to odors: Influences of hedonic valence of odor, gender, age, and social presence. Developmental Psychology, 32(2), 367-379.


SEE ALSO other pieces from this supplement published as articles on DRNO:

Neuro Best Practice Guide - March 1 2022

Podcast / video interview: Neuro-Guru Dr Michelle Niedziela - February 3 2022

Using EDA to Track Response to SquidGame - February 2 2022

Wearable Tech - Fit for MRX? - January 18 2022

Myths and Realities in Neuromarketing - January 11 2022

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