The Nexus of Neuroscience and User Research: A New Dawn in Mining Deeper Insights into User Behaviour
There’s an unexplored terrain that could revolutionize user research and, ultimately, the UX industry for the better. Where we can not only understand what individuals do but also answer the age-old question why. This article aims to converge on the no-man’s land that is the relationship between user research and neuroscience, taking you on a journey through my understanding of both fields and where I believe neuroscientific applications can be best applied.
Businesses have already started to incorporate user analytics into their practices and are now looking towards user research to provide more contextual answers. This has caused a surge in demand for user researchers, to help businesses employ a top-down approach to efficiently meet market demands. User research refers to the study of user behaviour in relation to processes and products. It prides itself on putting users first — integral to product development from conception. Insights are mostly derived from usability studies, interviews, and surveys, which are fed directly into design for development. This creates an eventual go-to-market product that suits the needs and desires of the target user. Essentially, user research aims to bridge the gap between software and satisfaction, playing an increasingly important role in the digital age we find ourselves in.
Neuroscience, too, seeks to uncover human behaviour, but through alternative means (that we’ll discuss later), and aims to understand how memories, attention, emotion, and cognition are formed, allocated, felt, and processed. In part, these processes are all neural in nature and can be associated with the different aspects of a user’s experience:
- Emotion: user satisfaction and engagement
- Attention & Memory: product interaction and recall
- Cognition: decisions to be made
The field of design already makes use of neuroscientific findings, where all good designers rely upon Design Principles. These principles help guide designers in crafting a user experience, that accounts for patterns of processing in the brain, and serves as the foundation for effective design. They cover areas such as simplicity, balance, alignment, contrast, repetition, proximity, and hierarchy — all pivotal in emotion, attention, and cognition. In understanding these principles, designers have been able to harness the power to create harmony, direct the eye, and communicate messages — enhancing both functionality and user experience in their creations.
The partnership between neuroscience and design has been well-trodden, but in this article, I want to showcase how we can closer align user research with neuroscience.
Attention
Attention is critical to designing a product that captures and retains user focus and much research has centred on uncovering what and where draws attention. Functional Magnetic Resonance Imaging (fMRIs) plays a key role in understanding this by allowing researchers to identify regions of the brain that activate when exposed to a visual stimulus, as well as provide the strength of said activity within the brain.
Eye-tracking technology, either in conjunction with fMRIs or as a standalone method, can uncover precisely where users are directing their gaze and help identify patterns of fixation and dwell times, as well as highlight elements of interest to users. One such application of this is in security warning pop-ups. We are all routinely exposed to some variation of warnings on our screens and failure to heed these warnings make us a weak link in computer security. We can determine when users fail to understand a warning and work has been done to improve the timing of these security messages (Anderson et al., 2015), but Vance et al. (2018) took it one step further and conducted two experiments to pinpoint why users fail. They used eye tracking and fMRI data to examine the development of habituation (a decreased response to repeated stimulation) to security warnings. They found a general decline in participants’ attention to warnings over time. However, updating the appearance of the warning (through polymorphic design) reduced the habituation of attention.
These findings help shed light on the problem of habituation to important information in design and demonstrate the use of polymorphic designs to improve adherence. Depending on your business and product objectives, you can use fMRIs and eye-tracking information to influence behaviour towards a particular action and understand which features enhance or detract precision of attention to elements of interest.
Memory
fMRIs may also be used to study memory encoding and retrieval by providing insight on which regions of the brain are activated during specific tasks. Another method frequented by neuroscientists is electroencephalography (EEGs, for those who gave up halfway through reading the word). This method monitors electrical activity and event-related potentials in the brain, and unlike fMRIs, EEGs are known to used in real-time experimentation. This allows for direct evaluation of memory encoding, retention, and retrieval, as well as help to assess the timing of responses.
EEGs can also be used to measure cognitive load (which may be related to memory performance) by directly measuring the changing patterns of brainwaves while a user is performing a complex task (Antonenko et al., 2010). Schmutz et al. (2010) conducted research examining the effects of the presentation of cognitive load on consumer decisions and is a great example of the positive longer-term economic impact of using neuroscientific techniques to reduce cognitive load. In comparison to a matrix design, findings suggest that information presented in a list form is associated with lower cognitive load and a higher number of product selections. They suggest that lists may trigger comparison processes within the brain, which could explain the differences found.
User researchers and designers can use this information to decide on which elements to keep and where to best place them to optimise retention, as well as understand the importance of layout in design. Making a design memory friendly.
Emotion
The impact of emotion on user experience is profound and cannot be overstated. Understanding the role of emotion in digital interaction is critical to designing products that deeply resonate. But emotion is intrinsically complex. For decades, psychologists have tried to unravel the intricate tapestry that is human emotion, but the difficulty lies in the interpretation of results as emotion can be easily influenced by the forces of nature and experiences of nurture.
We may not ever fully understand the qualitative intricacies of the mind, but we can take steps to try and quantitatively measure emotional engagement through several physiological measures, such as: heart rate, skin conductance, and facial electromyography (EMG). All of which monitor delicate changes in biological states of the heart, skin, and facial muscle activity in response to stimuli. An increased heart rate may suggest a heightened response or cognitive strain toward certain tasks. Increased sweating could reflect excitement or stress. And activation of the zygomaticus major muscle (typically involved in smiling) could indicate a positive emotional response. These methods go beyond the simple ‘how do you feel?’ question user researchers love to hate (and hate to love). It digs deep at a cellular level and gives close to an unbiased view, as users cannot instruct their body to produce more sweat or heart to pump faster, nor can they escape the natural muscular responses on their face (no matter how convincing we think our acting skills are when faced with another pair of socks for Christmas!).
In understanding emotion to this extent, reveals unconscious responses that usually lead to a truer understanding of a participant’s thoughts. It also allows researchers to ascertain the strength and emotional intensity of a response — going beyond what the humble Likert scales have to offer.
Cognition
Cognition encapsulates a wide range of processes underpinning our day-to-day behaviour, such as attention, memory, perception, problem-solving, and decision-making. In understanding the underlying systems and how our brain processes that information, we learn more about the how to improve interface design and user journeys. Work here also contributes further to the field of human-computer interaction (a multidisciplinary discipline that seeks to improve how we interact with technology).
MRIs can be used to explore the neural correlates of these processes and provide a more objective assessment of a user’s cognitive state, nullifying the need for more inaccurate traditional self-reporting methods. Information can then be used to amend designs to boost cognitive engagement with the product interface. It can also uncover individual differences that can help tailor designs to those that exhibit unique neural patterns — especially important when designing for those with cognitively accessible needs.
Ethical Considerations
User researchers are the custodians of user-centric design. This means we have an ethical responsibility to users who participate in our studies. This responsibility goes beyond corporate compliance and legal regulation, and it is our moral obligation to safeguard the emotional well-being and strict privacy of users. Whatever biometrics or personal metadata we hold needs to be handled with care and consent. It is suggested that researchers should also incorporate data encryption and secure storage to protect user data from unauthorized external access.
Transparency is key when talking to users about research objectives, data collection, and how their data will be used. Failure to comply with these basic privacy standards could lead to reputational damage or land you in legal difficulty.
Challenges
Through the many benefits and applications discussed, traversing the neuroscience and user research nexus brings on its own set of unique challenges that must be strategically addressed when planning research of this nature.
Although neuroimaging techniques can provide detailed information, the costs associated with hiring, running, and analysing these results may outweigh the presumed benefits. Purchasing a machine can incur costs in the hundreds of thousands, and although renting is more budget-friendly, it may leave many wondering if it’s a worthwhile endeavour. Many companies are only starting to find the budget for their first user researcher, let alone investing in an entire neuroscientific suite. Running such complex machinery also requires a high level of expertise and someone who has the knowledge and skill necessary to ensure the quality and accuracy of the data collected. A similar disadvantage is present when conducting physiologically based methods. One potential option for those considering this could be to partner with a university; this would also help towards solving the problem of hiring an expert to run the machine and decipher the results.
There are also cons for the participants taking part in those studies. Generally, fMRIs are notorious for being uncomfortable, and movement can cause data loss (Peitek et al., 2018). So, it may not be the most practical option for those wishing to evaluate their colour-choice of ‘Cadmium Yellow’ on their webpage.
The results, although quantitative in nature, are open to great interpretation. If it didn’t come straight from the user’s mouth, can it be trusted? Furthermore, neuroscientists and psychologists themselves subscribe to different schools of thought, and these factions have led to different theories being used to explain the same outcome. Coupled with low sample sizes, results using these methodologies are difficult to generalise across a larger population.
The Future
Several advancements will have a sizable impact on the field of neuroscience and, in turn, user research. One such advancement is the development of brain-computer interfaces (BCIs), which refer to technology that enables direct communication between computers and the brain, allowing users to interact with screens via brain activity (bypassing the need for the humble keyboard and mouse). Due to lack of research in the area, Rui & Gu (2021) conducted a review on BCIs and fMRIs, where they sought to better understand neural responses to UI and UX designs. Ultimately, they called for more work to be conducted in the neuro-aesthetic processing field.
Another promising development is emotion recognition technology for facial expression analysis. The underlying technology uses artificial intelligence, ideally trained on millions of datapoints, to make predictions of participants’ expressions. This builds on the work of Ekman (1992), who identified six basic human emotions: happiness, sadness, fear, disgust, anger, and surprise. Emotion recognition technology could be seen as a digital extension of the EMG technique and could be used in unison to help build a comprehensive view of emotion.
Closing thoughts
The role of a user researcher will evolve and play a significant role in bridging the gap between design, technology, neuroscience, business, and the user. With technology ever-changing, user researchers will have no choice but to stay at the forefront of neuroscientific medical research so as not to get left behind. Though this technology may not ever replace the humble interview or survey, it will help provide supplementary data that can be used as strength for a particular design choice or product decision.
Using neuroscientific techniques and principles, we can innovate on traditional methods and focus on crafting digital experiences that resonate on a neural level.
I urge colleagues in the user research, design, and neuroscience fields to unite and embark on this exciting journey together.
References
Anderson, Bonnie; Vance, Anthony; Kirwan, Brock; and Jenkins, Jeffrey, ““Not Now:” Using fMRI and Eye Tracking to Improve the Timing of Security Messages” (2015). WISP 2015 Proceedings 23.
Antonenko, P., Paas, F., Grabner, R. and Van Gog, T., 2010. Using electroencephalography to measure cognitive load. Educational psychology review, 22, pp.425–438.
Ekman, P., 1992. An argument for basic emotions. Cognition & emotion, 6(3–4), pp.169–200.
Peitek, N., Siegmund, J., Parnin, C., Apel, S., Hofmeister, J.C. and Brechmann, A., 2018, October. Simultaneous measurement of program comprehension with fmri and eye tracking: A case study. In Proceedings of the 12th ACM/IEEE international symposium on empirical software engineering and measurement (pp. 1–10).
Rui, Z. and Gu, Z., 2021. A review of EEG and fMRI measuring aesthetic processing in visual user experience research. Computational Intelligence and Neuroscience, 2021.
Schmutz, P., Roth, S.P., Seckler, M. and Opwis, K., 2010. Designing product listing pages — Effects on sales and users’ cognitive workload. International journal of human-computer studies, 68(7), pp.423–431.
Vance A., Jenkins, J. L., Anderson, B. B., Bjornn, D. K.*, & Kirwan, C. B. (2018). Tuning out security warnings: A longitudinal examination of habituation through fMRI, eye tracking, and field experiments Management Information Systems Quarterly, 43(2), 1–26.
