Daily news from online research no. 32875
Dossier: Techniques and best practices in neuroscience #1
February 24, 2022
Continuing our focus on “measuring brains and bodies,” Michelle Niedziela of HCD Research reviews key “neuro” techniques, with tips on where to use them. The second part of the article, dedicated to best practices, will appear in DRNO when the special pdf issue is published early next week.
Michelle Murphy Niedziela, PhD is a behavioral neuroscience expert in neuropsychology, psychology, and consumer science. Academia (Monell Chemical Senses Center) and industry (Johnson & Johnson, Mars Chocolate) experience in R&D of innovation technologies and methodologies for consumer research. As Vice President of Research and Innovation at HCD Research, Michelle focuses on integrating consumer-applied neuroscience tools with traditional methods used to measure consumer response.
Neuroscientific approaches have become increasingly important in understanding how our bodies react emotionally and physically to experiences while helping researchers better understand unconscious motivations and emotional responses. Real and thoughtful applied neuroscience is about using the right combination of sensitive measures of psychology and neuroscience in the most appropriate way.
Mainstream neuroscience, also known as neuromarketing, has had its fair share of struggles, from a surge of pseudoscientific claims to outlandish and unrealistic costs. Most of its problems can be traced to a misunderstanding of science combined with a belief in the trust of researchers who push the limits of tools and technologies. In this article, we hope to provide 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 a different understanding of the consumer experience. The key to using them successfully is using the right tool for the right research question.
The Neuro Toolbox
Researchers interested in using neurotools often seek to uncover consumers’ implicit or unconscious emotional responses. Given the complex nature of emotion, it is difficult to find a comprehensive methodology to measure this phenomenon. Although the literature lacks a definition of emotion, multiple components such as physiological arousal, motivation, expressive motor behavior, action tendencies, and subjective feelings are widely accepted (Scherer, 2005). Yet the information collected from these tools, especially when used as a singular measure, is limited and can only highlight specific components of the overall experience that translate into emotion.
Some consumer experiences may be better captured by physiological and behavioral measurements of the autonomic nervous system (ANS) than by traditional sensory surveys. Physiological Measurements have been widely used to capture ANS responses to various types of stimuli such as movie clips, personalized recall of specific situations, and smells. Of these, the “gold standard” techniques – fEMG (facial electromyography), HRV (heart rate variability) and GSR (galvanic skin response) – bear this title because of their simplicity and direct correlation with what they measure. A regular use of such measures is to examine cognition- and emotion-related processes evoked during media exposure (Bolls et al., 2019; Potter and Bolls, 2012; Ohme, et al., 2011); while dynamic physiological responses measured over time have been widely studied for their role in emotional experience (Ellsworth and Scherer, 2003). For example, increases in GSR are directly and positively correlated with increases in arousal, HRV is directly correlated with changes in attention and relaxation, and fEMG is directly correlated with changes in emotional valence (positive emotional response or negative).
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 noninvasive method of recording electrical activity along the scalp to measure brain states (Nunez and Srinivasan, 2006). fMRI is a neuroimaging procedure that is frequently associated with exploring memories through brain activity, and works by measuring changes in magnetization between oxygen-rich blood and oxygen-poor blood (Singleton, 2009). While these tools have been wonderful in academia, their application in industrial research is often plagued by inappropriate research design. For example, extrapolation of emotional conclusions from EEG or fMRI work typically requires evoking reactions, not passive measurement. This step has often been skipped in the industry, rendering conclusions at best fuzzy and at worst totally wrong. Additionally, fMRI studies are notoriously expensive and difficult to perform within the confines of consumer research. In addition, the quality of the products varies, differing mainly in the number and quality of the electrodes used. Cheaper EEG headsets can be extremely unreliable, usually due to a weaker signal, and thus make it more difficult to analyze results that are already difficult to interpret. Cognition research in neuroscience, whether using fMRI or an EEG, lacks the ability to scrutinize an individual’s thoughts. Like most neurotechnology, an fMRI’s information or scans aren’t to blame for the exaggerated results researchers report. Researchers, as well as those who peer review new studies, should be held accountable for ensuring that the limitations and inappropriate use of certain tools are highlighted so that readers have a clear idea of the purpose. and the value of each method.
Continued behavioral measures such as eye tracking (a direct measure of gaze behavior) and implicit reaction measures are directly correlated with the association and can be useful in exploring reactions that consumers have difficulty self-reporting (i.e. i.e., which visual is more appealing or which concepts fit the brief better). However, eye tracking and implied reaction are slightly less reliable due to misinterpretation and misuse. Far too often, eye tracking behavior is attributed to attention, even though it is possible to look at something without paying attention. Similarly, poor design of implicit response studies also makes the results less reliable.
Another popular neurotool, facial coding, is easy and cheap to use, but not as useful as claimed. Proponents often neglect to reveal the limitations of face coding, such as social reactions, dropout rates, interpretations, etc. A study of Soussignan & Schall (1996) revealed facial responses are flexible and able to reorganize to adapt to different situations and support the emotional and communicative functions of human facial behavior. This means that it’s not always clear if you’re measuring a true emotional response or just a mirror response to another influencing factor.
Again, it is important to note that the shortcomings of the approach are not the fault of the measurements. It’s perfectly reasonable to use any of these metrics as long as you’re clear about any limitations AND you’re using them correctly. Ultimately, no one tool will cover all searches; therefore, we must be prepared to accept that some tools are more effective at collecting specific types of information than other tools. Different research questions and contexts require different methodologies and technologies. However, the applied neuroscience research market can be a murky place.
Next week: best practice guidelines
Bolls, PD, Weber, R., Lang, A. and Potter, RF (2019). Media Psychophysiology and Neuroscience: Integrating Brain Science into Research on Media Processes and Effects. Media Effects: Advances in Theory and Research, 195-210.
Ellsworth, PC and Scherer, KR (2003). Emotional evaluation process. Oxford University Press.
Nunez, PL and Srinivasan, R. (2006). Electrical fields of the brain: the neurophysics of the EEG. Oxford University Press, USA.
Ohme, R., Matukin, M. and Pacula-Lesniak, B. (2011). Biometric measurements for interactive advertising research. Journal of Interactive Advertising, 11(2), 60-72.
Potter, R., & Bolls, P. (2012). Psychophysiological measurement and significance of cognitive and emotional media processing. Routledge
Scherer, K. (2005). What are emotions? And how can we measure them? Social Science Information, 44(4), 695-729.
Singleton MJ (2009). Functional magnetic resonance imaging. The Yale Journal of Biology and Medicine, 82(4), 233.
Soussignan, R., & Schall, B. (1996). Children’s facial reactivity to odors: influences of hedonic valence of odor, gender, age and social presence. Developmental Psychology, 32(2), 367-379.
All articles 2006-22 written and edited by Mel Crowther and/or Nick Thomas, unless otherwise noted.