erps_brand

COGNITIVE NEUROSCIENCE OF BRAND LOGO RECOGNITION

Late Positive Potential in brand logo recognition: relationship between emotional response and self-‐reported preference

Christa Villari, Nina Underman, Maya Reiselbach, Julia Gomez

Abstract

Previous market research suggests that many factors, like familiarity and emotional response,

influence consumer decision-making. The present study looked at a neural correlate of emotional

response, the Late Positive Potential (LPP), and surveys of self-reported brand preference in

order to better understand how advertising efforts may influence emotional processing and

potentially affect purchasing behavior. The hypothesis was that self-reported preference of a

brand would correlate with its LPP response, and that familiarity would increase the LPP

amplitude and strength of self-reported ratings. Participants were shown images of familiar and

unfamiliar brand logos as ERPs were recorded, then completed a survey rating their preference

for the brands. Survey ratings of familiar logos were rated significantly more positively and

strongly than unfamiliar logos. There was no significant difference in mean amplitude of LPP

response to familiar versus unfamiliar logos in the centroparietal region. However, there was a

trending, positive correlation between difference in strength of preference and difference in mean

LPP amplitude when comparing responses to familiar versus unfamiliar logos. The findings

support the principle that advertising is an important influence on consumer psychology,

increasing brand familiarity in ways that may strengthen a consumer’s emotional response to a

brand and possibly affect purchase behavior.


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Introduction

Consumers are confronted every day with the decision to purchase just one product over

a large number of equally viable alternative products that perform the same task. Understanding

the consumer decision process that leads to the purchase of a certain brand over others is

valuable information for marketers and companies that allocate millions of dollars towards

advertising each year. The emphasis of the present study was to explore how advertising efforts

that increase a consumer’s familiarity with a brand may influence emotional processing, which

plays a crucial role in consumer decision-making.

The generalized and widely accepted model for decision making involves the process of

examining possible options, comparing the possible outcomes, and then choosing a course of

action, such as purchasing a certain product (Perreau, 2014). However, this method can be

inefficient. If a consumer follows this model every time he or she is bombarded by, for example,

the many different laundry detergents at the supermarket, then this shopper could spend hours

simply weighing the options of which detergent to buy. Given the number of options and the

similar functions of the products, the cognitive load of this basic decision would be tremendous.

Researchers have therefore come to support a simplified model of decision making for this basic

consumer choice task that minimizes cognitive demand and maximizes the potential for a

satisfying outcome of this purchase decision (Hoyer, 1984). This model is based upon the

understanding that decision makers apply simple heuristics, adjusting the rules to these heuristics

based on the feedback they receive from their environment (Hogarth, 1981). In the context of

consumer decision making, past interactions with a brand/product can result in a positive,

negative, or neutral experience. These interactions can occur in various ways, such as by first-

hand use of the product, or through advertising and marketing efforts. Upon evaluating this


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experience, the consumer begins to form opinions about whether this brand satisfies (or does not

satisfy) individual needs better than existing alternatives. This phenomenon, experts believe, may

drive “brand loyalty,” the tendency for some consumers to continue to purchase the same brand

of items rather than competing brands (Bettman et. al, 1991).

This simplified decision-making, heuristic driving product choice is validated by a

particular study in which participants’ most “beloved” brands resulted in reduced activation of a

region within the dorsolateral prefrontal cortex (Schaefer et. al, 2007). Because the dorsolateral

prefrontal cortex is associated with decision-making, this study shows that the beloved brand has

the ability to reduce strategic reasoning and therefore result in more “impulsive” decision

making during the path to purchase. This simplified decision-making heuristic and the concept of

brand loyalty built on past experience are also consistent with Thorndike’s theory of

reinforcement learning. This theory, which is implicated in many decision making paradigms,

states that human beings learn from the consequences of their actions (Thorndike, 1970),

therefore suggesting that the previous interactions a consumer has with a brand’s product play a

crucial role in associating positive or negative emotions and attributes to the product.

Many studies on consumer behavior have attributed increased purchase behavior to this

heuristic model: consumers are more likely to purchase a product from a brand that they are

familiar with and feel positively about as opposed to an unfamiliar brand that they feel neutrally

towards (Whan et. al, 2010). This suggests that the decision whether to buy a product or not may

be influenced by emotional associations made as soon as the consumer views a characteristic

logo or brand name and before a consumer consciously strategizes about the features or functions

of the product. While the overall consumer decision-making process does involve other factors

besides brand loyalty, the general path-to-purchase for many typical consumers is believed to be


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a stepwise process that integrates primary sensory information with emotional associations as

well as cognitive decision-making (Perreau, 2014).

Consumer neuroscience, or “neuromarketing,” is a developing field that uses knowledge

about neuroscience and human psychology as a market research strategy aiming to understand

the source and strength of consumer behavior via neural mechanisms. Within this field, the use

of electroencephalogram recording (EEG) has often been used to study and analyze the cognitive

processes underlying the consumer experience (Morin, 2011). Consumer-research EEG studies

are often concerned with studying the Late Positive Potential (LPP) event-related potential (ERP)

waveform. The LPP is a positive wave that arises about 300 ms after a stimulus is presented and

generally peaks about 400-600 ms post-stimulus over the centroparietal scalp region (Cuthbert

et. al, 2000). The LPP is thought to be a component strongly modulated by the emotional

intensity of a stimulus, regardless of the valence of this emotion (Brown et. al, 2012). This is

further confirmed by studies, for example, by Liu et. al (2012) that have shown a larger LPP

component for emotional images versus neutral images. Therefore, the LPP is a component of

crucial interest for exploring emotional processing in response to visual perception of brand

logos, a part of the consumer decision-making heuristic described above.

The purpose of this present study was to examine the emotional responses elicited by

various stimuli representative of specific brands (i.e. brand logos) to better understand the forces

that influence a consumer to remain loyal to or trust one brand over another. The perception of

brands and their logos, the identifying images tied to a specific company or product, are crucially

important in distinguishing equivalent products. Brands are often marketed in ways that try to

elicit specific emotional reactions in potential customers or cause a certain target demographic to

self-identify with specific qualities the brand claims to represent (de Azevedo, 2010). Given the


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importance of establishing this connection between brand and consumer, marketing of the brand

logo is often repetitive or themed during advertising campaigns to make sure consumers are

aware of and familiar with the feelings or image the company is trying to convey. This fits in

with the simplified decision-making model described above. From a marketing perspective,

repetition of an advertising campaign that emphasizes a specific quality of a brand builds

familiarity and a specific emotional association that consumers hopefully recall as soon as they

see the brand logo in the store and use to influence purchase behavior. From a consumer

perspective, the emotional ties established between the consumer and the brand allow for more

efficient decision-making.

This study used self-report preference surveys and LPP ERP waveforms to explore the

influence that advertising efforts may have in creating emotional associations to brand images.

The self-report preference surveys in this study were used as a representation of the valence that

a consumer might feel towards a particular brand logo he or she sees on the store shelves or in a

television commercial. LPP ERP waveforms were averaged from EEG recordings to provide

insight into the neural basis of emotional intensity elicited by certain brand logos. We combined

these two experimental measures in an attempt to examine LPP responses to both familiar and

unfamiliar brand logos and to determine whether such responses were correlated with self-

reported ratings of these logos. Based on the assumption that familiarity and past experience with

a product create stronger emotions for consumers than an unfamiliar product with which they

have little or no experience with, the overall hypothesis of the present study was that familiar

brands would receive more positive LPP mean amplitudes and stronger self-reported preference

ratings, regardless of valence, and that differences in the emotional strength of these ratings

would correlate with differences of mean LPP response for unfamiliar and familiar stimuli.


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Methods

Participants

Twenty participants (nineteen undergraduate students and one professor) provided EEG

and behavioral data for this study as part of course credit. Participants were 35% (7 out of 20)

male and 65% (13 out of 20) female, ranging from ages 20 to 34. Mean age of participants was

21.6. All participants were right-handed. The majority of participants were native English

speakers and free of neurological conditions or major head injuries; 19 out of 20 participants

qualified for each factor.

Stimuli

Eighty images, each a unique brand logo without letters or numbers, were used as target

stimuli in the experiment. Stimuli were pre-divided by experimenters into familiar and

unfamiliar categories, and these categories were confirmed by 25 peers, all of whom were

Bowdoin students not participating in the course. Stimuli were sized to fill the whole screen

during testing and were given white backgrounds on a computer monitor’s white screen.

Presentation of stimuli was created using E-Studio from E-Prime 2.0 for Windows, and then

presented to participants using E-Run from the same software suite.

Design

During the EEG-recorded task portion of the experiment, each participant was presented

with all 80 stimuli, with the order of stimuli and length of interstimulus screen presentation

randomized across the participants. During the self-report preference survey portion of the

experiment, participants received one of four versions of the survey that presented the images in


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different orders. This was done to prevent bias of response based on how early or late in the

survey a participant was asked to rate a specific brand image.

Procedure

Each two-hour session was run through the neuroscience program in the psychology

department at Bowdoin College. Participants consented to the study and provided anonymous

demographic information prior to beginning EEG and survey data collection. Participants

secured the EEG cap onto their heads and two experimenters filled the electrodes with gel, while

checking that impedances were sufficiently low (25 kΩ). Participants provided data for 5 studies,

always in the same order, with the present study being 2nd of these 5.

In the present study, all participants viewed a randomized order of 80 stimuli: 40

unfamiliar and 40 familiar. Prior to viewing stimuli, participants were asked to be attentive to the

presentation. Stimuli were presented for 1500 ms each. In between stimuli, a white screen with a

fixation cross at the center was presented for a variable length of time between 900-1500ms. This

screen was intended to provide a baseline to viewing stimuli, as well as to prevent habituation to

the presentation. No behavioral response was required from participants during the EEG

recording. After finishing the 5th and final study, participants filled out a paper survey in which

they rated each brand on a valence scale of 1 to 5, with 1 being “strongly dislike”, 3 being

“neutral”, and 5 being “strongly like.” In subsequent analysis, we re-coded survey scores to

reflect strength of preference. A score of 3 remained “3”, whereas the average of the 2 and 4 was

coded as “4” and the average of 1 and 5 was coded as “5.” Thus, the higher the strength score,

the stronger the preference of the brand.


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EEG/ERP Recording and Analysis

EEG was recorded from 64 electrode channels using Brain Amp. Impedances were

checked to be below 25 kΩ before testing and in between studies. Recordings were filtered to be

between 0.1-100 Hz and sampling was conducted at a 250 Hz rate. Recordings were referenced

to the Cz electrode site and pre-processed using EEG Lab with MatLab. Data was imported from

the vhdr file, and stimulus events were defined and binned as either familiar or unfamiliar. Any

bad channels were identified and corrected using spherical interpolation. A low pass filter of 40

Hz was applied. Recordings were then segmented from -200 ms pre-stimulus to 1000 ms post-

stimulus, with a baseline correction using the -200 ms pre-stimulus interval. Artifacts were

detected with a simple voltage threshold from -100 to 100 uV, and a sample-to-sample threshold

difference of 50 uV. Data was re-referenced to the class average of all channels. ERP waveforms

for each channel were averaged and plotted.

The LPP waveform was generated from all subjects’ centroparietal channel recordings:

Pz, CPz, FCz, CP1, and CP2 (Figure 1). Specifically, LPP was measured as the mean amplitude

at these sites from 400 to 1000 ms post-stimulus. These channel locations and time frame were

selected based on previous literature (Weinberg & Hajcak, 2010). In follow-up analysis, LPP

was re-measured as the mean amplitude from the same sites occurring 400 to 800 ms post-

stimulus.

Statistics

Statistical analysis was conducted on Microsoft Excel, SPSS, and GraphPad Prism 6.

Two-tailed, paired t-tests were used to assess any differences between familiar and unfamiliar

logos with regards to LPP mean amplitude, mean survey scores coded by strength, and mean


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survey scores coded by valence. Significance was assigned at a p-value less than 0.05, and

significance was considered “trending” at a p-value less than 0.1. Average differences between

unfamiliar and familiar logo images were calculated for LPP mean amplitude, survey scores

coded by strength, and survey scores coded by valence. Two correlations of differences were

conducted, comparing LPP mean amplitude to survey scores coded by strength or valence.

Results

Survey Results

Survey results were coded either by valence or strength of the ratings (Figure 2). The

original hypothesis focused on looking at the overall strength of self-preference ratings based on

previous evidence that both positive and negative emotional images show a larger LPP than

neutral stimuli (Brown et. al, 2012). When coded by strength, a paired, two-tailed t-test showed,

as expected, that familiar logos were rated significantly higher (i.e. more strongly) than

unfamiliar ones (t(19) = 7.34, SEM = 0.06, p < 0.01). Additionally, a paired, two-tailed t-test for

mean survey scores coded by valence showed that familiar logos were rated significantly higher

(i.e. more favorably) than unfamiliar ones (t(19) = 17.59, SEM = 0.09, p < 0.01). Survey results

and statistics are summarized in Figure 3.

ERP Results

LPP was measured from five centroparietal channels Pz, CPz, FCz, CP1, and CP2, which

are circled in red in Figure 1. First, mean LPP amplitude from 400-1000ms was compared for

unfamiliar and familiar brand logos. It was expected that familiar logos would have an increased

LPP mean amplitude compared to that for unfamiliar ones. However, a paired, two-tailed t-test


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showed no significant difference in mean LPP amplitude from 400-1000ms when comparing

unfamiliar and familiar logos (t(19) = 0.81, SEM = 0.35, p = 0.43). Based upon the average LPP

waveform (Figure 4) for this time frame, it was thought that mean LPP amplitude from 400-800

ms might significantly differ based on familiarity of logos. Yet, a paired, two-tailed t-test showed

no significant difference in mean LPP amplitude from 400-800 ms between familiar and

unfamiliar logos (t(19) =1.48, SEM = 0.36, p = 0.16). Mean LPP amplitude results are

summarized in Figure 5.

Correlation Results

It was predicted that, when comparing familiar to unfamiliar logos, differences in mean

survey scores coded by strength and LPP mean amplitude would be positively correlated with

each other. Average difference between unfamiliar and familiar LPP ERP amplitudes was

correlated with average difference between unfamiliar and familiar self-reported surveys.

Overall, survey results coded by strength produced stronger correlation values and a p-value of

the correlation that was closer to statistical significance than survey results coded by valence

(Figure 6). The correlation of differences in unfamiliar and familiar logo preference strength and

LPP response showed a weak, positive correlation (r(18) = 0.30, p = 0.10). The correlation of

differences in unfamiliar and familiar logo preference valence and LPP response showed a weak,

negative correlation (r(18) = -0.22, p = 0.17).


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Discussion

Implications of results

These findings have interesting and relevant implications for the field of neuromarketing.

Whether coded by valence or strength, self-reported responses rated familiar brands significantly

higher (i.e. participants felt more positively and strongly about familiar brands) than unfamiliar

brands. There are two possible explanations for this result. Firstly, this could indicate that

familiar logos are inherently more preferred than unfamiliar logos; if one recognizes the brand,

one tends to rate it more highly.

The second possible conclusion is that the selection of brand stimuli content was

unintentionally skewed, such that there was not an equal distribution of stimuli for which it was

expected that subjects would feel either negatively or positively about. If this study were to be

replicated, conscious effort should be made to vary the brands represented so that more logos

would be rated negatively, creating an equal distribution of positive, negative and neutral brand

logos, especially in the familiar category. The current study used only 80 logos as stimuli, so

perhaps simply increasing this number could help eliminate a potential bias in selecting

positively regarded logos. Further studies could include a category of response on the self-report

survey to distinguish between mixed feelings that consumers may feel about specific brands and

neutral feelings. Additionally, further studies could include both images of brand logos and

branded products in an attempt to distinguish potential differences in feelings consumers may

have towards a brand as a whole versus the products it includes.

Though there was no statistical significance for the difference in the average LPP mean

amplitudes elicited by familiar and unfamiliar stimuli from 400-800 ms, it is worth noting that

the p-value for this comparison was closer to significance when compared to the results for mean


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LPP from 400-1000 ms. The standard error of the mean for this analysis was large, indicating

that variability was so high that the difference between mean LPP amplitude in familiar and

unfamiliar logos was not significant. These results support the hypothesis that there is a greater

neural correlate for emotional response to familiar brands than unfamiliar ones. In the context of

consumer decision-making and brand loyalty, this result makes sense: because subjects likely

have no prior experience with the unfamiliar brands, it would not be expected for them to have

any kind of emotional association with these brand images.

There was also a weak, positive correlation between differences in self-reported strength

of preference for familiar and unfamiliar logos and differences in LPP responses to familiar and

unfamiliar logos. This finding indicates that, consistent with the original hypothesis, if

participants have a stronger self-reported response to a brand, regardless of response valence,

they also elicit a greater LPP in response to that brand. Since the LPP reflects a neural correlate

of emotional response, a higher self-reported response to a logo appears to be associated with a

greater emotional response. This correlation is not enough to predict actual consumer purchase

behavior, but confirms previous market research assumptions that emotional strength may be

involved in consumer decision-making and brand loyalty (Perreau, 2014).

Limitations of experimental design

Although the results of this study do support original hypotheses and are relatively

consistent with past research, there were a number of limitations in the study design. A larger

sample size could have produced more significant results. Additionally, 25% of the subjects (5

out of 20) had prior exposure to the brand stimuli used; for those 5 subjects, unfamiliar logos

may have become familiar due to the time spent collecting the stimuli and designing the study.


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Because this study was presented in a block with other experiments, it is possible that the

experiment that occurred immediately prior to the present one could have affected fatigue and

attention during this study. Lack of attention could also have occurred due to the fact that the

experimental design required no motor response, leading to disengagement with the stimuli.

Despite these potential opportunities for fatigue/boredom, raw EEG data did not have many

alpha waves, suggesting the data was not too affected by potential participants’ boredom.

A major obstacle in regards to experimental stimuli was the basis on which the

experimenters determined what constituted a “familiar” versus an “unfamiliar” logo. We did

make efforts to accurately categorize the stimuli by first dividing the logos based on prior

knowledge and experience with them, then presenting the lists to a sample of individuals who did

not participate in the study. Feedback from this focus group was used to alter final

categorizations of stimuli. However, there was no way of determining whether the categorization

of stimuli was accurate for the specific participants. For instance, participants may have had

geographic differences in exposure to certain brand logos depending on where they grew up.

Another obstacle in regards to the post-experiment survey is whether the survey

measurement is an accurate reflection of preference. Although the survey was anonymous, there

is always a potential bias in self-perception that may not reflect neural correlates as strongly.

There also may have been differences in subjects’ interpretation of how the survey prompt was

worded. Participants rated brand logos based on a valence scale, but perhaps their responses

would have differed if the scale was based on strength or included an option for mixed feelings

towards a brand. Experimental design could have asked participants to indicate familiarity and

preference of each image immediately after presentation, though this would have led to


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additional complexities in averaging ERP data and running statistical tests that this study sought

to avoid.

One major limitation concerns interpretation of the ERP waveform data. While we

assumed the waveform we analyzed from 400-1000 ms in the centroparietal region was the LPP,

a neural correlate of emotional intensity, there are other possibilities that could lead to different

interpretations and applications of these results. For example, previous research showed that an

increased LPP response to visual stimuli corresponded to increased attention for those stimuli,

suggesting that the LPP may also be a neural correlate for attention or recognition memory

(Pastor et al., 2008; Finnigan et. al, 2002). While it logically makes sense that familiarity,

attention, and recognition memory are needed to produce emotional responses to brand logos, it

is possible that the ERP results reflect one of these other neural correlates instead of emotional

response. Our weak, positive correlation of observed ERP waveforms and emotional strength of

preference provides loose support that the neural component analyzed was a reflection of

emotional intensity of a brand logo, as originally hypothesized. However, these alternative

possibilities should be considered, especially since the time interval and channel locations of the

averaged LPP mean amplitude waveform analyzed by this study could instead be the LPP of the

parietal old/new effect, which reflects recognition memory.

For the parietal old/new effect, old/repeated items elicit more positive LPPs than

new/unrepeated items (Finnigan et al., 2002). Studies examining the LPP in the context of the

parietal old/new effect have demonstrated that LPP waveforms taken from centroparietal

channels 500-800 ms after stimulus presentation represent decision making accuracy; correct

decisions in a word discrimination task produced greater LPP amplitudes than inaccurate

decisions (Finnigan et al., 2002). Additionally, this study did not find that the LPP corresponded


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to emotional strength; at certain centroparietal electrodes, the peak LPP amplitudes were

equivalent for new/unfamiliar words and emotionally strong words (Finnigan et al., 2002).

Taken in the context of marketing, one could imagine that when individuals are faced

with numerous brand choices, they will be more likely to attend to a brand that is familiar or

triggers a recognition memory, without necessarily increasing strength of emotional response.

However, like the factor of emotional strength, factors of increased attention or awareness of

increased familiarity when a brand logo is viewed may not necessarily dictate consumer decision

to buy that specific brand’s product, though they may be more inclined to heavily weigh it as an

option.

Future directions

In addition to the suggestions made above to address specific limitations of this study,

different data analysis could be used to further explore the application of these results on

consumer neuroscience. Correlating differences of LPP amplitudes and survey responses was

done in this study for general simplicity of analysis. However, correlating by individual LPP

amplitude subcategories (familiar and unfamiliar brand logos) and survey response subcategories

(strong and neutral emotional response for familiar and unfamiliar brand logos) would provide

additional detail that could help confirm or alter interpretations of the results presented in this

study. Additionally, it would be interesting to code the data so that it could be analyzed by

individual brand image and by gender. In scoring the surveys, certain brand images, specifically

Playboy and Nike, produced opposite valence self-report preference ratings within the

participants. It would be interesting to see, for example, what the correlations between mean LPP

amplitude for the Playboy image and various categories of self-report preference ratings would


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look like and if these correlations are significantly different for male participants versus female

participants.

Future data analysis could also use mean LPP averages from different time windows.

While significance was not seen in the mean LPP averages for familiar and unfamiliar logos

when these averages were taken from 400-1000 ms or from 400-800 ms in the centroparietal

region, the p-value did get smaller as the time interval shortened. Perhaps significance would

have been seen if mean LPP averages were obtained from 400-600 ms in the same location.

Previous studies have shown the LPP to begin roughly 300-400 ms after the presentation of an

emotional picture and last through the duration of the picture presentation (Liu et. al., 2012).

This background information and the 1500 ms presentation of each image in this study explains

the reasoning for the original calculation of average mean LPP amplitudes from 400-1000 ms

after image onset. Though calculating average mean LPP amplitude from 400-600 ms may seem

like a small time window, other studies, including the study whose experimental design inspired

this one, have demonstrated significance between brand images that subjects indicated they

preferred or felt indifferent towards using this 400-600 ms time window, even when each image

was presented for 6 seconds (de Azevedoa, 2010). It is possible that this study would produce a

shorter LPP wave in response to a presented stimulus because brand images were not reinforced

via a motor response. Additionally, unlike images used in other ERP studies of emotion, brand

images in this study were not purposely selected from a database of pictures with established

valence values like the International Affective Picture System, which may have also contributed

to a shorter LPP waveform (Cuthbert et. al, 2000).

Finally, ERP significance for mean LPP amplitude averages of familiar and unfamiliar

brand logos may have been seen if a different region of channels was used. While previous


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research indicates that the LPP is maximal over centroparietal areas, other regions of the brain

have also shown an LPP response to emotional images, including images of brands (Cuthbert et.

al, 2000; de Azevedoa, 2010; Pastor et. al, 2008). Specifically, previous research using EEG to

explore perceptions of consumer brands showed that, while participants’ most preferred brands

had stronger LPP activations in the centroparietal regions from 400-600 ms compared to

indifferent and unknown brands, preferred brands were also more active in the frontal cortex

than indifferent brands during the same time window (de Azevedoa, 2010). In looking at the

grand average waveforms from this study, it does appear that the greatest LPP effect could have

been more frontal, though actual statistical analysis would need to be performed from an average

of frontal channels to confirm or deny this hypothesis (Figure 7).

Clearly, there are a plethora of future investigations necessary to fully and accurately

understand the results of this study. Overall, the results show that there may be a positive

correlation between LPP response and strength of self-reported preference, indicating that

marketing targeting consumer emotion should strive to build brand familiarity. However, the

relationship suggested by this correlation, that LPP response to a brand logo can impact actual

consumer decision-making, should be tested. If we assume, as our original hypothesis did, that

the LPP is a neural correlate reflecting emotion intensity, a logical future study could examine

the relationship between LPP response strength and likelihood of choosing to buy such a brand’s

product in a behavioral testing paradigm. This study could answer the question of whether, when

faced with choosing between an unfamiliar brand and a familiar but negatively perceived brand,

consumers will choose the brand with a greater emotional response, regardless of the valence of

that response, or be more likely to base purchase decision off of economic factors, for example.

The results of such a study would help to provide a complete picture of the consumer decision-


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making process, allowing companies to market products more effectively and providing

additional evidence to support the importance of the consumer neuroscience field.

Conclusion

Overall, these results suggest that if marketers are aiming to target consumer emotion,

familiarity matters. This interpretation of the results provides better understanding as to why

companies spend millions of dollars every year to advertise their brands. By using advertising

repetition to boost consumer exposure to a brand, marketers can not only increase consumers’

overall familiarity with a brand but also dictate specific emotional associations or feelings of

identity that consumers perceive when viewing a brand logo. However, because it was not

possible to conduct EEG recording in an actual store as real consumers were making purchases,

we cannot predict how, if at all, the weak, positive correlation between LPP mean amplitudes

and self-report preference demonstrated in this study would actually translate into purchasing

behavior. Nevertheless, this study is a crucial first step for investigating the neural processes

behind consumer decision-making. Though the simplicity of the experimental design was

prioritized, leading to limitations discussed above, these findings are novel and relevant for

studying neuromarketing, providing a stepping stone for future exploration of the field.


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Figures

Figure 1: Electrode channel locations used during EEG recording. Cz (blue circle) served as the reference during EEG recording for all subjects. The red circles indicate the five centroparietal electrode channels averaged for ERP analysis of the LPP from 400-1000 ms, and also 400-800ms.

Figure 2: Coding of self-report surveys done by both valence (left) and strength (right) of preference response. For valence coding, surveys were scored on a scale of 1-5, with 1 representing strong dislike, 5 representing strong like, and 3 representing neutral preference for the brand image shown during experimental testing. Survey responses were also coded by strength. In this scale of 3-5, 3 represents a neutral response, 4 represents a medium-strength response (4’s and 2’s grouped together), and 5 represents a high-strength response (5’s and 1’s grouped together).


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Type of Coding (Scale) Average Survey Score: Unfamiliar Logos (SEM)

Average Survey Score: Familiar Logos (SEM)

Average Difference in Scores (p-value*)

Valence Coding (1-5)

3.01 (0.010)

3.65 (0.081)

0.64 (p < 0.001)

Strength Coding (3-5)

3.02 (0.009)

4.09 (0.061)

1.07 (p < 0.001)

*p-value from paired, two-tailed t-test Figure 3: Self-report survey results. Means and differences shown for unfamiliar and familiar logos coded by both strength and valence. Differences between unfamiliar and familiar logos were statistically significant in both coding conditions. Familiar logos were rated more positively and strongly compared unfamiliar ones.

Figure 4: ERP results. Average ERP mean waveforms for the LPP (centroparietal region, 400-1000 ms) for visualization of familiar and unfamiliar logos. Centroparietal region represented by an average of FCz, CP1, CP2, CPz, and Pz channels.


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LPP AMPLITUDES FOR CENTROPARIETAL REGION

400-1000 ms 400 - 800 ms

Average LPP amplitude: Unfamiliar Logos (SEM)

0.5853 µV (0.3707)

0.6761 µV (0.4165)

Average LPP Amplitude: Familiar Logos (SEM)

0.8658 µV (0.4695)

1.2089 µV (0.5061)

P-Value (paired 2-tailed t-test)

0.43 0.16

Figure 5: Average LPP mean amplitudes for centroparietal region from 400 -1000 and 400- 800 ms. Average mean amplitudes were higher for familiar compared to unfamiliar logos in both time frames. However, while both p-values were not statistically significant, measuring from 400-800 ms produced a p-value that was closer to statistically significance than in the 400-1000 ms time frame. 6A

CORRELATION OF LPP AND SELF-REPORT SURVEY DIFFERENCES

Survey Responses Coded by Strength Survey Responses Coded by Valence

Average LPP amplitude difference (Familiar - Unfamiliar)

1.25 1.25

Average self-report survey response difference (Familiar - unfamiliar)

1.07 0.64

Correlation (R value) between LPP and Self-report survey differences

0.2992 -0.2218

R2 Value of correlation 0.0895 0.0491

P- Value of correlation 0.0999* 0.1738

*no significant correlation, but trending towards significance


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6B

6C


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Figure 6: Correlation results. Average difference between unfamiliar and familiar LPP ERP amplitudes was correlated with average difference between unfamiliar and familiar self-report surveys. Survey results coded by strength produced stronger correlation values and a p-value of the correlation that was closer to statistical significance than survey results coded by valence. Statistical results (A) are shown graphically for strength coding (B) and for strength and valence coding (C). Figure 7: Further analysis frontal channel locations (left, circled in blue) and example average ERP waveforms from 400-1000 ms (right).

Familiar Unfamiliar


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Appendix 1: Stimuli Categorization Familiar:

Unfamiliar:


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Appendix 2: Survey Example

:

erps_brand

Documents

brand familiarity

unfamiliar brand logos

certain brand

influence consumer

unfamiliar logos

consumers emotional

consumer decision process

consumer decisionmaking