Neuroscience Research Methods
predict consumer behavior and decision-making preferences, helping companies optimize promotion and product strategies.
The origins of neuromarketing
The brain doesn't lie.
Companies study consumer brain activity to understand consumer needs and behavior drivers. Although many consumers do not believe that they make mindless purchases, research shows that consumers often make irrational decisions and have little understanding of the factors that influence their behavior. However, consumers' brains do not lie.
New tool predicts decision making.
With the help of brain imaging technology, neuroscience has been used since the early 20th century to understand and predict consumer behavior and preferences in decision-making.
Researchers use a variety of technologies such as electroencephalography (EEG), eye tracking, heart rate and skin conductance to study consumers’ cognitive activity, emotional responses, and subconscious reactions.
For over 20 years, neurophysiological technology has been applied in neuromarketing, helping companies optimize branding, pricing, packaging, and advertising strategies while saving on marketing resources.
20th Century: Early Cosumer Studies
Psychologists began studying consumer behavior since the early 20th century, but without involving brain activity.
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Early 20th Century, researchers focused on observable behavior as a way to study consumers, but did not delve into brain activity.
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Mid-20th Century, with the rise of cognitive psychology, researchers focused on consumers' memory, attention, and decision-making processes, but still couldn't directly observe brain activity.
Late 20th Century: Cosumer Brain Observation
In the late 20th century, researchers could observe consumers' brain activity under the help of brain imaging technology.
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Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and Electroencephalography (EEG) made it possible to observe brain activity linked to consumer behavior.
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In 1999, the Marketing Science journal published the first fMRI research paper on consumer behavior, marking the beginning of neuromarketing.
Early 21st Century: Neuromarketing Emergence
Research institutions begun to study brand impact in the early 20th century, from which neuromarketing emerged as a field.
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In 2002, Dutch psychologist Ale Smidts coined the term "neuromarketing".
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Companies like BrightHouse, NeuroFocus (later acquired by Nielsen), and EmSense emerged, helping businesses apply neuromarketing research to advertising and branding.
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In 2003, Read Montague conducted the famous "Pepsi Challenge" using fMRI, highlighting the brain's role in studying brand preference.
Neurophysiological Application
Neuromarketing research utilizes various physiological measurements to expand its application across multiple fields.
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A variety of neurophysiological measurement techniques are widely used in neuromarketing research, including near-infrared spectroscopy (fNIRS), galvanic skin response (GSR), and eye-tracking.
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The application of neuromarketing has expanded to areas such as brand perception, product pricing, packaging, and advertising design.
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There is growing overlap between academic research and commercial applications. Many universities now offering neuromarketing courses and research programs.
Future: Integration
In the future, the combination of neuromarketing with big data and AI will further enhance the accuracy of consumer predictions and drive personalized marketing.
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Machine learning and data analysis have enhanced the accuracy of predicting consumer behavior and improving product experiences.
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Neuromarketing enables precise personalized marketing by analyzing consumers' neurophysiological responses, optimizing ad content, product recommendations, and user experience.
What can neurophysiology bring to consumer research?
First-Person View
We present consumers’ first-hand perspective to companies that want to understand consumers.
Sensitive
Neurological tests can detect psychological changes in real-time, allowing analysis of unconscious perceptions and emotions.
Objective
Neurological tests are not subject to consumers' expressed wishes and correspond most directly to consumers' psychological activities.
Dynamic Feedback
Physiological signals offer real-time, high-resolution feedback, turning consumers' emotions and cognitive responses into tangible insights.
EEG signals
In the 19th century, bioelectric signals in nerves and muscles were discovered. In 1924, German psychiatrist Hans Berger first recorded human brain activity, leading to the creation of the "electroencephalogram" (EEG) in 1929.
Since then, we've learned that the brain's millions of neurons communicate through electrical signals, deepening our understanding of brain waves. Electroencephalogram (EEG) signals are a reflection of neuronal activity and can monitor changes in brain activity in real time and continuously. Non-invasive EEG devices record scalp signals, converting them into digital data to analyze brain activity.
19th Century: Nervous System Signals
In the 19th century, scientists discovered that biological electrical signals are transmitted through the body's nervous system in response to external stimuli.
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In the late 18th century, Luigi Galvani discovered that frog legs twitch when exposed to electrical currents, laying the foundation for the study of bioelectricity.
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In the 19th century, Emil du Bois-Reymond conducted research on "nerve impulses", furthering the understanding of bioelectric signals in living organisms.
Early 20th Century: Brain Wave Recording
Electrical activity has been recorded since the early 20th century. Different brain waves were identified, such as α, β, and θ.
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Hans Berger recorded human brain waves for the first time at 1924, and coined the term “electroencephalogram (EEG).”
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Berger also discovered the first waveform in EEG history - alpha waves (8-12 Hz), which are most prominent when the eyes are closed and relaxed.
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In the early 1940s, Walter Grey Walter introduced ERP (Event-Related Potentials) for more in-depth analysis of brain activity.
1950s: EEG Applications
EEG began to be applied in research fields such as epilepsy, sleep, consciousness state, and brain damage.
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In the 1950s, EEG was applied to sleep studies, leading to the discovery of different sleep stages, such as REM (Rapid Eye Movement) and NREM (Non-Rapid Eye Movement) sleep.
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EEG technology has been widely used to explore different states of consciousness, including wakefulness, coma, and anesthesia.
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Doctors use EEG signals to record the electrical activity of the damaged area to assess the extent of the patient's brain damage and recovery.
1960s: Multichannel EEG
In the 1960s, EEG was combined with other physiological measurements to perform multi-channel recordings.
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In the mid-20th Century, EEG and other physiological measurements were integrated for sleep studies (e.g., EEG, heart rate, respiration, and eye movements), collectively known as PSG (Polysomnography).
1980s: Cognitive Process Insights
In the mid-20th century, ERP and digital signal processing enhanced EEG’s ability to reveal cognitive processes.
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Researchers found that analyzing EEG signals with precise timing could reveal cognitive processes, leading to the use of Event-Related Potentials (ERPs) for studying attention and perception.
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In the 1980s, computer advancements allowed for digital processing of EEG signals (e.g., FFT and wavelet transforms), enabling more accurate analysis of brain activity.
1990s: BCI Development
Brain-Computer Interface (BCI) technology developed rapidly since 1990s, initially for medical and neurotechnology purposes.
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Since 1990s, EEG signals began to be directly linked with computers, accelerating the development of Brain-Computer Interface (BCI) technology, used in areas like medical rehabilitation.
21st Century: Consumer Neuroscience
Various companies have begun utilizing neuroscience to analyze consumer emotions and experiences since 21st century.
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Case Study 1: Coca-Cola Brand Influence Experiment (2004)
Signal Recording: fMRI
Findings: Participants preferred Pepsi when unaware of the brand. However, knowing they were drinking Coca-Cola led to significant brain activation in areas related to memory and emotion, highlighting the brand’s influence.
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Case Study 2: Campbell Soup Ad Optimization (2009)
Initial Testing: Neuroscience techniques revealed weak emotional responses and attention to brand elements.
Design Iteration: Multiple ad and packaging designs were evaluated with EEG.
Final Design: An optimized design improved brand logo visibility and focused consumer attention on key elements.
Nowadays: EEG and Big Data
Highly accurate EEG and big data analysis have expanded the precision and application of modern EEG research.
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The development of portable, high-density EEG collection devices has enabled researchers to study brain activity with greater resolution.
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Big data and machine learning are now used to identify patterns in complex EEG signals, advancing cognitive neuroscience and clinical practices.
EEG signals are applied to detect consumers’ emotional responses and analyze changes in preferences for products, advertisements, and other content.
We use EEG combined with big data models to predict consumer purchasing decisions .
Horr, N. K., Mousavi, B., Han, K., Li, A., and Tang, R. (2023). Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters. Frontiers in Neuroscience. 17:1191213.
Eye Tracking
"The eyes are the windows to the soul." The Eye-Brain Theory suggests that while the eyes capture visual information, the brain interprets it and makes decisions. Understanding this process in consumer research can enhance product appeal and boost brand recognition.
Since the 19th century, people have explored the link between eye movement and brain processing. In the 20th century, non-invasive eye-tracking technology made this research easier. Now in the 21st century, eye-tracking is more accurate and portable, allowing for research in real-world settings. With big data and AI, eye-tracking analysis is smarter, often combined with other physiological signals for a fuller understanding of consumer behavior.
Late 19th Century: Early Eye Movement Study
The study of eye movements during reading began in the late 19th century, leading to devices that recorded these movements by attaching to the cornea.
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French physician Émile Javal observed saccades and fixations in reading.
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American psychologist Edmund Huey invented a device to record eye movements using small levers on the cornea.
Mid-20th Century: Non-Invasive Tracking
In the mid-20th century, non-invasive, imaging-based eye-tracking devices were developed, enabling real-time recording of eye movements.
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Alfred L. Yarbus and other scientists used lenses and optical systems for more precise tracking.
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In the 1970s-1980s, non-invasive eye-tracking systems became common, allowing real-time analysis and widespread research.
Late 20th Century: Computing Advancements
In the late 20th century, advances in computing significantly improved the processing of eye-tracking data.
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In the 1980s-1990s, advanced computer technology enhanced data processing, boosting research in cognitive science.
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As consumer behavior research grew, eye-tracking was applied in brand studies, advertising analysis, packaging design, and user experience.
Nowadays: Integrated Analysis
Eye-tracking analysis is now smarter, integrating big data and AI with multiple technologies for comprehensive insights.
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Eye-tracking systems are now more compact and precise, being capable to work at various situations.
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Data analysis is more intelligent and automated, offering quicker insights and better consumer behavior predictions.
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In neuromarketing, eye-tracking is often combined with EEG, fMRI, and EDA for a deeper understanding of consumer responses.
Electrodermal activity
This response is known as electrodermal activity (EDA), a sign of emotional arousal. In 1849, Emil Du Bois-Reymond first linked changes in skin conductance to nervous system responses.
EDA is connected to mental states like stress and anxiety and was once used in lie detection. Today, it's widely used to measure stress, emotion arousal, and cognitive load. Short-term reactions (e.g., being startled, seeing unexpected information) are measured with galvanic skin response (SCR), while long-term emotional states (e.g., the emotional arousal of consumers during a product experience) are assessed with galvanic skin level (SCL).
Although EDA indicates emotional intensity, it can't distinguish specific emotions and is easily influenced by external factors like temperature and humidity. To better analyze emotion types and intensity, SCL is often combined with EEG signals.
Multimodal observation and analysis
EEG signals, eye movement trajectories, and skin electrical activity each have their own advantages and limitations in revealing consumers' emotional responses and states of consciousness.
Advantages
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High temporal resolution allows instant capture of consumers' brain activities at the millisecond level.
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Emotional and cognitive analysis identifies the types of emotions consumers experience in response to specific stimuli and evaluates cognitive load and attention during decision-making.
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Applicable to real scenarios, this method records consumers' genuine reactions in real-world situations.
limitations
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Motion interference: EEG signals can be affected by factors like head muscle activity, eye movements, and external noise.
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Due to its richness and noise, EEG data requires advanced signal processing techniques and expertise for accurate analysis and interpretation.
Multimodal neurophysiological observations can integrate multiple physiological measurement technologies.
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EEG is used to capture consumers’ brain responses in real time.
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Eye tracking records consumers’ visual trajectory.
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External cameras record the consumers’ experience and behavior.
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Interview helps to understand consumers’ subjective feelings.
