The evaluative conditioning of well-established liked, disliked, and neutral brands. A new, multidimensional

approach to understanding consumer behaviour.

Shannon Stefan Bosshard

BPsych (Newcastle)

School of Psychology

Faculty of Science and Information Technology

University of Newcastle

This thesis is presented for the degree of

Doctor of Philosophy

July 2016

Declaration

Originality

I hereby certify that to the best of my knowledge and belief this thesis is my own work and contains no material previously published or written by another person except where due references and acknowledgements are made. It contains no material which has been previously submitted by me for the award of any other degree or diploma in any university or other tertiary institution.

Thesis by Publication

I hereby certify that this thesis is in the form of a series of a book chapter and an additional three papers. I have included as part of the thesis, a written statement from my co-authors, endorsed in writing by the Faculty Assistant Dean (Research Training), attesting to my contribution to any jointly authored papers. (*Refer to clause 39.2 of the Rules Governing Research Higher Degrees for acceptable papers).

Shannon Bosshard

Student number 3092496

July 2016

Acknowledgements

I would like to firstly thank my Supervisor, Professor Peter Walla. Peter’s funny, outgoing, and overall, positive approach to life is contagious. Without his support throughout the past four years, I would not have been capable of achieving such an amazing body of work. Allowing support practically 24/7, although making his life more difficult, it made the goal of graduating seem obtainable during the times that the end felt like an eternity away. I will never forget what you have done for me. Thank you for coming on this journey with me. We did it Peter! You were right! Patience brought roses! We have finally put together some ‘really sexy stuff’.

To my wonderful family, a great big thank you! Thank you to my parents for reading documents that you couldn’t understand. I’m sure that there will be plenty more in the future. Thank you for the constant calls asking whether I would ever finish. Well, now I can finally say that I’m done!

Without your support, this great achievement would never have been possible. To my wife,

Kaylie, thank you for keeping me level headed throughout the past four years and making sure that I didn’t give up. Your support has been invaluable. You are the most compassionate person that I know and I am so glad to have you in my life.

To those that at UoN, thank you for the time that we spent together. You all truly made it an unforgettable journey and I hope that the future will see us remain friends. Finally, I am also very grateful for the time and effort devoted to my research by Dr Ross Fullham and Tony

Kemp, who volunteered their time to learn about my research paradigms to help create and test analysis methods for me.

List of publications

Book Chapter: Walla, P., Mavratzakis, A., & Bosshard, S. (2013). Neuroimaging for the

Affective Brain Sciences and Its Role in Advancing Consumer Neuroscience. In K. N. Fountas

(Ed.), Novel Frontiers of Advanced Neuroimaging (pp. 119-140). Croatia: Intech.

Paper One: Bosshard, S. S., Bourke, J. D., Kunaharan, S., Koller, M., & Walla, P. (2016).

Established liked versus disliked brands: brain activity, implicit associations and explicit responses. Cogent Psychology, 3: 1176691. doi: 10.1080/23311908.2016.1176691

Paper Two: Bosshard, S. S., Kunaharan, S., Koller, M., & Walla, P. (Unpublished Manuscript).

Can we change your opinion towards your most favourite and most hated brands? Your brain says yes even if your mouth says no.

Paper Three: Bosshard, S. S., Kunaharan, S., Koller, M., & Walla, P. (Unpublished

Manuscript). Can we condition familiar, neutral brands. Can we condition well-established, familiar, neutral brands? A new, implicit approach to understanding neutral brand attitudes.

I warrant that I have obtained, where necessary, permission from the copyright owners to use any third party copyright material reproduced in the thesis (e.g. questionnaires, artwork, unpublished letters), or to use any of my own published work (e.g. journal articles) in which the copyright is held by another party (e.g. publisher, co-author).

Additional output

Journal Articles

Walla, P., Rosser, L., Scharfenberger, J., Duregger, C., & Bosshard, S. (2013). Emotion

ownership: Different effects on explicit ratings and implicit responses. Science

Research

Conference Abstracts

Bosshard, S., Bourke, J., Koller, M., Meier, J., & Walla, P. (2014). Like it or Not: Physiological

correlates of brand attitudes, NeuroPsychoEconomics Conference 2014, Munich,

Germany

Bosshard, S., Bourke, J., Koller, M., Meier, J.L., & Walla, P. (2014). Applying

Electroencephalography to Study Brand Attitude, Annual Conference of the Society for

NeuroEconomics, 26-28 September 2014, Miami, USA

Bosshard, S. S., & Walla, P. (2013). Objective Measures Within Consumer Neuroscience. Front.

Hum. Neurosci. Conference Abstract: ACNS-2013 Australasian Cognitive

Neuroscience Society Conference.

http://dx.doi.org/10.3389/conf.fnhum.2013.212.00082

Bosshard, S., & Walla, P. (2015). Evaluative conditioning of liked and disliked brands. accepted

for the Conference "ASP2015 - 25th Annual Conference of the Australasian Society for

Psychophysiology". Frontiers.

Bosshard, S., & Walla, P. (2012, December). Objective measures within consumer neuroscience.

Poster presented at the annual meeting of Australasian Cognitive Neuroscience Society,

Brisbane, Australia.

Bosshard, S., & Walla, P. (2013, December). Objective measures within consumer neuroscience.

Poster Presented at the Annual Priority Research Centre for Translational Neuroscience

and Mental Health Postgraduate and Postdoctoral Conference, Newcastle, Australia.

Walla, P., Koller, M., Brenner, G., & Bosshard, S. (2014). Evaluative Conditioning: Different

Methods different Insights?, Annual Conference of the Society for NeuroEconomics,

26-28 September 2014, Miami, USA

Walla, P., Koller, M., & Bosshard, S. (2014). Truth detection: unbiased brain responses

reflecting brand attitude, Gmunden Retreat on NeuroIS 2014 Conference and

Proceedings

Walla, P., Koller, M., & Bosshard, S. (2016, May). Evaluative conditioning of brand attitude –

comparing explicit and implicit measures. EMAC Conference 2016. Oslo, Norway

Contents Declaration ...... 3 Acknowledgements ...... 5 List of publications ...... 7 Abstract ...... 17 Thesis Summary ...... 19 General Introduction ...... 21 Introduction: Book Chapter ...... 41 4.1. Branding ...... 47 4.2. Package design and labelling ...... 51 4.3. Final statement ...... 57 References ...... 59 Paper 1: Established liked versus disliked brands: brain activity, implicit associations and explicit responses ...... 63 Abstract ...... 67 1. Introduction ...... 69 1.1. Background ...... 69 1.2. Implicit Measurements ...... 71 1.3. The Present Study...... 73 2. Methods ...... 74 2.1. Participants ...... 74 2.2. Stimuli ...... 74 2.3. Individual pre-assessment of brand attitudes ...... 75 2.3.2. Lab experiment ...... 75 2.4. Data Recording and Processing...... 76 3. Results ...... 78 3.1. Self-report at pre-testing...... 78 3.2. Self-report during the lab experiment ...... 79 4. Discussion ...... 83 4.1. Self-report and IAT ...... 83 4.2. Event related potentials ...... 85 4.3. Conclusions ...... 87 References ...... 89 Paper 2: Can we change your opinion towards your most favourite and most hated brands? Your brain says yes even if your mouth says no...... 97 1. Introduction ...... 103 Current study ...... 109 2. Methods ...... 110 2.1 Participants ...... 110 2.2 Stimuli...... 110 2.2.1 Conditioning stimuli ...... 110 2.3 Individual pre-assessment of brand attitudes...... 111 2.4 Lab Experiment ...... 111 2.5 Data Recording and Processing ...... 113 3. Results ...... 115 3.1 Self Report ...... 115 3.2 IAT ...... 116 3.3 Event related potentials ...... 118 3.4 Conditioning ...... 118 4. Discussion ...... 128 4.1 Self Report and IAT ...... 129 4.2 Event Related Potentials ...... 130 4.3 Conclusions and Implications ...... 133 References ...... 137 Paper 3: Can we condition well-established, familiar, neutral brands? A new, implicit approach to understanding neutral brand attitudes...... 149 Abstract ...... 153 1. Introduction ...... 155 Current study ...... 160 2. Methods ...... 161 2.1 Participants ...... 161 2.2 Stimuli...... 161 2.2.1 Conditioning stimuli ...... 162 2.3 Procedure ...... 162 2.3.2 Lab Experiment ...... 163 2.4 Data Recording and Processing ...... 164 3. Results ...... 167 3.1 Self Report ...... 167 3. 2 IAT ...... 167 3.3 Event Related Potentials ...... 169 4. Discussion ...... 176 4.1 Self Report and IAT ...... 176 4.2 Event Related Potentials ...... 177 4.3 Conclusions...... 180 References ...... 183 General Discussion & Conclusion ...... 195 1.1 Explicit responses ...... 196 1.2 Implicit Association Test ...... 196 1.3 Electroencephalography ...... 198 2. Implications ...... 201 3. Final comments ...... 203 References ...... 205

Abstract

Traditional marketing research, up until recently, has seen an over reliance on surveys and questionnaires to gain an insight into consumer preferences. Although these explicit methods were once thought to provide a comprehensive insight into the true attitudes of the consumer, recent literature suggests otherwise. The assertion that explicit measures are polluted by cognitive processes is becoming more common. As a result, the future of marketing is one that sees marketers and advertisers assess consumer attitudes via the use of tools which are unaffected by cognitions and thus, do not require a verbal or cognitive response. To test the notion that implicit measures are a more reliable and a more sensitive means to assessing attitudes and attitude changes than traditional explicit measures, the current project involved the simultaneous collection of electroencephalography (EEG) data and self-report data whilst participants were presented with familiar liked, disliked, and neutral brands. In addition, further implicit insight was provided via the implementation of the implicit association test (IAT). The collective results of the present research indicate that whilst self-report and the IAT are sensitive to baseline like and dislike, conditioning resulted in no changes in attitudes. In contrast, EEG appeared to not only be capable of distinguishing between liked, disliked, and neutral brands at baseline, but also after subsequent conditioning. Taken together, the findings of the current thesis not only suggest that neural activity may provide an insight into the affective nature of brand attitudes, but also that an implicit approach may allow for an accurate prediction of future purchase behaviour.

Thesis Summary

The current thesis aims to serve several purposes. The general introductory section outlines the issues with current approaches to understanding consumer behaviour. Traditional measures of attitudes, although most commonly utilised within consumer research, appear to be vulnerable to cognitive processing. That is to say, such approaches are thought to be inaccurate.

In contrast, implicit measures, those that assess non-conscious components of attitudes, are thought to be far more capable of assessing true attitudes. Although the acknowledgement of such measures is constantly made, many researchers continue to show preference for more traditional, albeit often less suitable methods.

The book chapter aims to establish the contribution that neuroscience has made to marketing thus far and the issues associated with the application of such findings to real world marketing scenarios. Up until this point, the application of neuroscience to marketing has lacked applicatory relevance. Neuroimaging techniques, although interesting when applied to understanding consumer behaviour, provide little opportunity for utilisation within real world scenarios. By drawing attention to such flaws, the following sections of this thesis provide a strong message that a multidimensional approach to understanding consumer behaviour is imperative and research findings should allow for application within real marketing scenarios.

In paper one, baseline attitudes were collected via both traditional methods (i.e., a survey) in addition to novel approaches (i.e., electroencephalography & implicit association test). Results provide evidence that objective measures such as EEG and IAT are capable of reflecting explicit like and dislike, even though those measures have been shown to often lead to discrepant findings. Moreover, the results obtained at baseline using implicit measures match those collected via explicit measures. In sum, these findings suggest that liked and disliked brands were in fact processed as established like and dislike at all levels of information processing. In contrast to paper one, paper two addresses whether well-established like and disliked brand attitudes are susceptible to conditioning. Although the IAT and explicit responses were unable to detect changes in attitude as a result of conditioning, EEG was consistently sensitive to conditioning effects and thus, able to detect changes in attitude toward well established brands.

In sum, we report discrepancies between implicit and explicit measures, thus our findings may reflect that evaluative conditioning does not happen on all levels of respective information processing.

Similarly to paper two, paper three assesses conditioning effects; however it focuses on well-established, neutral brands. Again, the IAT and explicit measures, although able to detect baseline affect, neither were sensitive to the effects of conditioning. In contrast, EEG was consistently sensitive to conditioning effects which again, may suggest that evaluative conditioning does not occur at all levels of respective information processing.

Together, the contents of the current thesis promote the notion that explicit and implicit attitudes occur as a result of different processes, and thus require differing approaches to measurement. In sum, the use of a multidimensional approach to understanding brand attitudes is imperative. As a result, the concluding sections of the current thesis outline the potential applications of the current findings within the domains of marketing and advertising.

General Introduction

It should come as no surprise that the goal of marketing is to create loyalty between consumers and brands. Ultimately, businesses strive to engage the consumer so that a purchase is made, but when consumers are presented with upwards of 1000 advertisements on a daily basis

(Marsden, 2006; Martin and Smith, 2008; Pappas, 2000), it is imperative that the advertisement not only draws attention, but creates a positive memory within the consumer’s mind. This being said, the most efficient method of building successful relationships between consumers and brands has, and will continue to be a contentious topic among researchers and marketers.

Marketing literature constantly stresses the importance of brand identity on the consumer’s perception and decision making process (Dotson et al., 2012). Extant research shows that consumers use brand names as an indicator of performance and quality (Poulsen et al.,

1996). In addition, research shows that brand perceptions can even affect sensory processing

(e.g. taste; Fornerino and d’Hautville, 2010; Rossi, Borges & Bakpayev, 2015). Such findings emphasise the importance of developing a strong brand. As a result of such research, many companies utilise conditioning paradigms to modify the attitudes of their current and future consumers. Conditioning involves repeated pairings of a conditioned stimulus (CS; e.g. a brand name) with an unconditioned stimulus (US; e.g. affective sound or picture). Eventually, the repeated pairings result in a transfer of affect from the US to the CS. Within advertising, the most common forms of conditioning involve exposing the consumer to repeated pairings of a brand and pleasant music, images, and celebrities. This being said, although research suggests that evaluative conditioning can lead to increases in brand awareness (Du Plessis, 1994; Hollis,

1995), sales (Haines, Chandran & Parkhe, 1989; Haley and Baldinger, 1991) and brand positivity

(Smith, Feinberg & Burns, 1998), there is debate as to whether it can reliably change well established attitudes towards familiar brands (Smith et al., 1998; Gresham &Shimp, 1985).

There is a general consensus that stimuli that are highly familiar and well established are resistant to conditioning. Latent inhibition (LI) is the process by which people’s attitudes towards a stimulus are resistant to change as a result of previous exposure to that stimulus. Such findings have been reiterated within the domains of psychology, marketing, and advertising (Gorn, 1982;

Gresham and Shimp, 1985; Stuart, Shimp & Engle, 1987). According to Stammerjohan et al.

(2005), highly familiar brands are characterized by well-established, relatively stable attitudes that are somewhat resistant to the effects of advertising. Although these findings have been repeatedly presented, businesses continue to spend billions of dollars on advertising each year to elicit positive changes towards their brands (“Advertisers will spend nearly $600 Billion worldwide in 2015”, 2014). Furthermore, it is common knowledge that a change in attitude can be derived without an individual having ever engaged with the brand. For instance, being informed that a company regularly engages in slave or child labour could potentially initiate a negative perception of the brand, even though the individual has never purchased or engaged with the brand. Such discrepant findings present evidence that more research needs to be conducted before conclusions can be drawn regarding the ability to changes one’s brand attitudes.

Although the content, as well as the approach to advertising has dramatically improved since their first appearance in the early 1800s as a result of technological advancements, the overreliance on traditional, self-report measures is evident. Traditional approaches, although seemingly adequate, rely on the ability of the consumer to accurately evaluate a stimulus however, recent research has questioned the reliability and validity of such approaches. This comes as a result of a large volume of research that has suggested that people either cannot, or do not want to fully explain their preferences (Babiloni, 2012; Greenwald & Banaji, 1995). These findings are in line with a growing body of more recent literature that not only supports the notion that non-conscious processes govern human behaviour, but that a large portion of decision making occurs outside of conscious awareness (Bargh, 2002; Hassin, Uleman and Bargh, 2005,

Zaltman, 2000). “According to Zaltman (2003) most estimates suggest that up to 95 percent of thought, emotion, and learning occur without our awareness. Given these consistent reports, it is crucial that alternative measures of attitude be investigated.

The early search for alternative measures of attitudes first revealed that attitudes may be comprised of more than just a conscious component (Fazio et al., 1986; Neely, 1977). In three experiments, Fazio et al. provided evidence which suggested that attitudes are comprised of an automatic component. Using a prime, it was reported that the latency of participant’s responses could be altered. For example, the presentation of a stimulus that participants evaluated as negative (e.g. cockroach) reduced the latency in which participants would respond to a negative adjective (e.g. disgusting) that followed than when a positive stimulus was presented. In a similar manner, when a stimulus that participants rated as pleasant was presented, this facilitated a reverse pattern whereby reaction times were faster when a positive adjective followed compared to a stimulus that was of a negative valence. Although a fairly primitive study, Fazio et al.’s findings have since been applied within marketing contexts to assess individual’s attitudes towards political parties (Hickfeldt, Levine, Morgan, & Sprague, 1999), advertising (Yi, 1990), and products (Adaval and Monroe, 2002; Braun, 1999).

Since the findings presented by Fazio et al., many researchers have supported the notion that attitudes are comprised of at least two components: one that is implicit and another that is explicit. Implicit attitudes are associations that are automatically activated in the presence of relevant stimuli without any conscious awareness of evaluation (Cunningham, Raye, & Johnson,

2004). In contrast, explicit attitudes are deliberate and contemplative evaluations formulated through reasoning (Gawronski & Bodenhausen, 2006). More specifically, explicit attitudes are those that occur as a result of cognitive processing and are affected by higher order cognitive processes thus can be verbally expressed. Within marketing, many researchers have indicated that attitudes are multidimensional and as a result require a multidimensional approach however, despite progression within this area of research; many researchers continue to show preference for more traditional, albeit often less suitable methods. The most compelling evidence for the inclusion of implicit measures comes from the constant discrepancies that have been reported when comparisons between implicit and explicit outcomes are made. According to several authors, implicit measures of attitudes have proven to be capable of predicting a range of important phenomena (Greenwald, Nosek, & Banaji, 2003;

Gregg, Seibt & Banaji, 2006; Maison, Greenwald, & Bruin, 2001; Nosek, Banaji, & Greenwald,

2002; Teachman & Woody, 2003), including spontaneous behaviour that explicit measures fail to predict (Dovidio, Kawakami, & Gaertner, 2002; Dovidio, Kawakami, Johnson, Johnson, &

Howard, 1997; Fazio et al., 1995; Neumann, Hu¨lsenbeck, & Seibt, 2004; Spalding & Hardin,

1999). Dovidio et al., (1997) reported, in a study investigating racial prejudices, that implicit measures predicted the amount of blinking and duration of gaze of white participants whilst viewing images of blacks, whereas explicit ratings did not. Similarly, within marketing contexts, the use of a multidimensional approach has shown discrepancies between implicit and explicit attitudes. For instance, Gibson (2008) reported that when participants familiar with Coke and

Pepsi were presented images of these two brands simultaneously with either positive or negative words, repeated trials saw no changes in explicit attitudes however, attitudes collected via implicit measures were seen to change in the direction of which they were conditioned.

Moreover, Walla et al. (2010) found that when eating ice cream, chocolate or yoghurt, although participants had no stated preference for a particular food item, implicit responses showed that ice cream was preferred.

As a result of these discrepancies, three implicit measures have been constantly utilised with marketing literature. Of the three, the IAT (Implicit Association Test; Greenwald et al.,

1998) is the most commonly cited. The IAT assumes that there is an associated network whereby concepts of memories, attitudes, and valence are linked within memory (Anderson & Bower,

1973; Collins & Loftus, 1975). More specifically, it is assumed that all stimuli that are liked by an individual are in some way intrinsically linked, as are stimuli that are disliked. The application of the IAT involves individuals being presented with pairs of stimuli, and being asked to respond as quickly as possible to each pair. The faster that an individual responds to the presented pair, the more intrinsically linked the pair is thought to be, as is the strength of the implicit association. For example Olson and Fazio (2001) used the IAT to assess the effects of conditioning. During their task, participants were presented with Pokemon characters intermixed with positive, negative, and neutral pictures, and words. The results revealed that participants responded to the stimuli faster during the compatible conditions than the incompatible conditions

(for methods, see Olson and Fazio). Similar findings have been presented in several other social phenomena including prejudice (e.g., McConnell & Liebold, 2001), self-esteem (e.g., Greenwald

& Farnham, 2000), and social identity (e.g., Greenwald et al., 2002; for reviews, see Fazio &

Olson, 2003; Greenwald & Nosek, 2001).

Although it appears to be the case that the IAT is a useful means of determining implicit attitudes within various contexts, it has not been without controversy. It has been proposed that the effects of the IAT can be affected by cognitive processes (Blair, 2002). Mitchell, Nosek, and

Banaji (2003) revealed that participants not presented with valenced words, responded more favourably towards black individuals when compared to white individuals. However, when the race of the individuals was revealed to participants, a trend towards favouring white people emerged. In addition to these findings, Dasgupta and Greenwald (2001) found reduced race IAT effects when participants were shown favoured black people and disliked white people. These results cast doubt on the IATs ability to exclusively measure, non-conscious, affective components related to attitudes.

Given the number of questions pertaining to the use of IAT as a reliable and accurate measure of attitude, the search for more sound approaches led to the inclusion of neuroscience to the field of attitude research. Electroencephalography (EEG), although used within relatively few marketing papers, has been promoted as a sound method of obtaining information pertaining to attitudes and motivation (Davidson et al., 1979; Ravaja, Somervuori and Salminen, 2013;

Cacioppo et al., 1993, 1994; Cuthbert et al., 2000). Specifically, event related potentials (ERPs) obtained via EEG has promoted the notion that this particular measure may be capable of identifying neural correlates of behaviour. In a two part study, Rugg et al., (1998) assessed the neural correlates of memory. During the initial task, participants were required to view single words within a shallow (asked to identify whether the first and last letters were in alphabetical order) or deep context (asked to use the word in a sentence). Following this, participants were then shown these words again however; they had been intermixed with additional words.

Participants were required to identify whether or not the presented words were new or whether they had been seen previously. The results revealed that ERPs across frontal electrode sites were more positive for correctly recognised words than for old words, or new words incorrectly identified as old words. Additionally, across parietal sites, old words produced more positive- going waveforms than did new words, regardless of the accuracy of recognition judgement.

More simply put, words that had been previously presented to participants elicited a more positive going ERP waveform, regardless of whether they had been recognised (at a conscious level) or not. According to Rugg et al., these findings promote the idea of a neural correlate of memory in the absence of conscious recognition. Such findings promote the notion of EGG as a measure sensitive to the non-conscious aspects related to behavioural output.

Although the findings of Rugg et al., (1998) seem to provide some insight into the sensitivity of EEG as a measure of non-conscious processing related to behavioural output, it is important to note that there is difficulty in determining what is actually being measured. The inability to definitively ascertain what EEG is specifically providing a measure of, occurs as a result of numerous factors including: the infancy of the field of neuroscience, the complexity of understanding behaviours which resultant of atomic/microbiological interactions and finally, the limits induced by technology. For instance, one example of a well-known, yet not well- understood ERP component is the P300. The P300 is an ERP component that occurs at approximately 300ms post stimulus onset and is typically elicited during the evaluation of a stimulus. Despite being the most researched component of the ERP waveform (Coull, 1997), the P300 signature is not fully understood (Rutiku, Martin, Bachmann & Aru, 2015). What is known however, is that the P300 seems to be modulated by attention (Coull, 1997). Such research emphasises the fact that EEG and ERPs, whilst informative, do not provide a full understanding of the neural correlates of the components elicited by an individual. A comprehensive understanding will require far more research. Irrespective, the exploratory nature of the current research aims to utilise EEG within marketing contexts to assist in the development of a more thorough understanding of the neural correlates of brand attitudes.

Of the papers seen to utilise EEG, many suggest that it is capable of differentiating between positive (approach) and negative (avoidance) affect (Davidson et al., 1979). According to Davidson et al., the left-anterior region of the brain is involved in the expression and experience of approach-related motivation and positive affect whereas the right-anterior region of the brain is conversely associated with the expression and experience of experience avoidance-related motivation and negative affect. The majority of literature surrounding the asymmetry model is derived from social psychology. Within this domain, the asymmetry model has proven useful for the understanding of judgements of social groups, depression (Henriques &

Davidson, 1990), panic disorder (Wiedemann et al., 1999), facial processing (Coan, Allen &

Harmon-Jones, 2001), and reward and punishment (Sobotka, Davidson & Senuli, 1992). Within marketing, although fewer findings are reported, they are no less informative. Rick, Wimmer,

Prelec, and Loewenstein (2007) showed that product preference resulted in increases in activation of the nucleus accumbens, and excessive pricing resulted in increases in activation of the right insula. In addition, reduced prices were seen to activate the mesial prefrontal cortex prior to a purchase decision. Further to these findings, Ravaja, Somervuori and Salminen (2013) asserted that independently, activity from each of these regions predicted future purchases.

Extending on these findings, Gable and Harmon-Jones (2008) showed that self-reported liking for dessert were associated with greater relative left frontal EEG activation whilst viewing images of dessert. The most pertinent findings for the current research were reported by Ravaja et al. who found the relatively greater left frontal activation was associated with an increased likelihood that a purchase be made. Moreover, Ravaja et al. reported that increased left frontal activation was associated with a purchase decision when price was below what participants had expected. Furthermore, Ravaja et al. reported relatively greater left frontal activation when a purchase for a national brand (one whose quality was perceived to be high) was made in comparison to when a purchase of a private-labelled (one whose quality was perceived to be low) brand was made. Finally, Ravaja et al. reported that relatively greater left frontal activation was related to a higher perceived need for the product prior to a purchase decision being made.

In full, these findings suggest the ability to not only utilise EEG asymmetry as a measure of attitude towards a product or brand, but potentially a measure of future purchase behaviour.

In addition to frontal asymmetry, the late positive potential (LPP) has provided further insight into the non-conscious processes pertaining to attitudes. The LPP is a slow positive deflection in the ERP that occurs approximately between 300-800ms post-stimulus onset however, such effects have been noted as late as 5000ms post-stimulus presentation. Enhanced

LPP effects are reported to be elicited in response to emotionally arousing and motivationally significant images (Cacioppo et al., 1993, 1994; Cuthbert et al., 2000; Schupp et al., 2000, 2003), as well as stimuli that occur less frequently within a task (eg. oddball tasks; refer to Squires &

Squires, 1975). The LPP has also gained psychometric endorsement. According to Moran et al.

(2013), the LPP has good-to-excellent reliability in addition to good internal consistency. Moran et al., also reported that only relatively few trials were needed in order to obtain a stable LPP effect.

Within marketing, the LPP has been used to investigate consumer herd behaviour

(making a purchase based on other’s reviews; Chen et el., 2009; Pozharliev et al., 2015), perception of luxury and non-luxury branded products (Pozharliev et al., 2015), effects of odour on the perception of advertisements (Lin, 2014), brand preference (de Azevedo, 2010), and brand attitude (Bosshard et al., 2016). More specifically, Chen et al., reported that when participants viewed a book that had a consistent review, that a larger LPP was elicited. Furthermore, behavioural data, according to Chen et al., revealed that a more consistent rating resulted in a higher herd rate. In addition, Lin reported that individuals classed as ‘sensitive’ to smell, elicited larger LPPs when viewing products that were simultaneously paired with unpleasant odours, rated these products more negatively, and were less likely to purchase these products. Finally,

Bosshard et al. reported that whilst passively viewing brand names, LPPs were larger for liked brands than disliked brands. It is clear from these pieces of research that the LPP is a useful means of providing insight into the processing of brands and products.

Together, all of the above findings present a picture in which the processing of attitudes, and thus, brand attitudes occur across a number of levels of information processing. Specifically, given that explicit and implicit measures seem to be differently sensitive to the effects of brand attitude as well as conditioning effects (Olsen & Fazio, 2001), it is possible that a multidimensional approach to assessing brand attitudes will allow for not only a greater understanding of the formation of attitudes, but provide an insight into future purchase behaviour. The following papers aim to further the understanding of non-conscious processing associated with brand attitudes. As a result, together, they will provide further evidence that brand attitudes are multidimensional constructs that require a multidimensional approach to their understanding.

At this point, it is important that a distinction be made between the measurement of attitudes and their formation. Although it is inferred that the tools utilised within the current study measure the results of the formation of an attitude and not the process of forming the attitude, this question falls outside the scope of the current thesis. Additionally, we maintain that attitude formation may very well involve a combination of implicit and explicit processes

(Bargh, 2002; Bargh, 1992). It is obviously the case that the formation of attitudes is an important and useful area of study however, the answer to this question falls outside the scope of the present research and as a result, holds no place within the current thesis. Furthermore, although the terms ‘implicit attitudes’ and ‘explicit attitudes’ are utilised throughout the thesis, no attempt is made to discern between the two. Instead, the primary aim of the thesis is to assess the sensitivity of the tools utilised to measure each of these components of attitudes. In doing so, the thesis aims to provide evidence in support of the notion that the measurement of attitudes can benefit from the inclusion of implicit/non-conscious measures.”

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Introduction: Book Chapter

The reference for this publication is:

Walla, P., Mavratzakis, A., & Bosshard, S. (2013). Neuroimaging for the Affective Brain

Sciences and Its Role in Advancing Consumer Neuroscience. In K. N. Fountas (Ed.),

Novel Frontiers of Advanced Neuroimaging (pp. 119-140). Croatia: Intech.

Co-author statements

By signing below I confirm that Shannon Bosshard contributed the majority of written content in

Section Four to the paper/publication entitled: Walla, P., Mavratzakis, A., & Bosshard, S. (2013).

Neuroimaging for the Affective Brain Sciences and Its Role in Advancing Consumer

Neuroscience. In K. N. Fountas (Ed.), Novel Frontiers of Advanced Neuroimaging (pp. 119-

140). Croatia: Intech.

22/07/2016

Peter Walla Date

07/06/2016

Aimee Mavratzakis Date

Faculty Assistant Dean Research Training Date

Over the last few decades the merging of marketing with neuroscience has captured the attention of both the academic and corporate world. Neuromarketing enables marketers and researchers to better understand what consumers react to and how intense their reaction is. What makes it more interesting is that these questions can be answered without the need to explicitly ask the consumer for their opinion. Neuromarketing is able to tap into one’s non-conscious and collect answers to questions such as: Is the colour, shape or smell of a particular product a good selling point? Although in its infancy, this new field has already had a major impact on the way many businesses market their products. With the formation of over 150 neuromarketing firms in the last 10 years, and almost 5000 times the number of Google searches between 2002 and 2004

(Figure 5; Plassmann, ZoëgaRamsøy & Milosavljevic, 2012) there is no surprise that this field has had such an impact across a vast number of disciplines.

Figure 5. Graphical depiction of the increase in Google searches and published articles relating to neuromarketing as well as the increase in neuromarketing companies (plassmann et al., 2012; with permission).

Although it is undeniable that neuromarketing is a useful field of study, along with its success, has come a major dilemma pertaining to the way that it is perceived by consumers and the media. Throughout this section, I will no longer refer to neuromarketing as such, but instead as consumer neuroscience. Since the formation of neuromarketing, consumers and those alike have held the opinion that the aim of this field was force consumers to buy things that they do not want nor need. It is a common misconception that neuromarkeitng aims to find the ‘buy button’ in the brain (provided one actually exists). This is neither the current aim of neuromarketing/consumer neuroscience nor should it ever be. Instead, the term consumer neuroscience emphasises that this field aims to study the interactions between products, the market and consumers rather than an attempt to coerce consumers into buying products.

Before we can appreciate the field of consumer neuroscience, we must have an understanding of what neuroscience is and what it can bring to the field of marketing.

Neuroscience, through studying the nervous system, seeks to better understand the biological basis of behaviour. However, according to Plassmann et al. (2012), due to the complex nature of consumer behaviour, it is essential that we focus specifically on systems neuroscience rather than cellular neuroscience. Systems neuroscience is a sub-discipline of neuroscience which focuses on how different neural circuits function, either together or separately. Rather than focusing on behaviour at a neuronal level, systems neuroscience focuses on both the cognitive and affective

(emotional) aspects of consumer behaviour. It is common knowledge that much of our behaviour is driven by our unconsciousness (Chartrand, 2005). For this reason, it is justified that neuroimaging be used to better understand consumer behaviour.

This section of the chapter will focus on how neuroimaging studies have identified specific neural circuits that are involved in the different aspects of the decision-making process. The figure above (Figure 6.) illustrates that the areas activated within the brain depending on the interaction that the consumer has with a brand. In many cases, several regions are responsible for the processing of a single cue. More specifically, the image gives a summary of the location of some of the processes that are involved in the psychology of brands (Plassmann et al., 2012).

Figure 6. An excerpt taken from Plassmann et al. (2012) (with permission) depicting the many regions involved in the processing of brand information.

In the following, we will focus on several current neuroimaging techniques and how their introduction into the field of marketing has influenced our understanding of consumer neuroscience. Branding, package design and labelling will be discussed because they are a major focus of a large number of studies. In addition, they are of particular interest to the marketing community because the results can be applied to the marketing of products and services.

4.1. Branding

When looking to purchase a product, brand name is an important factor, but plays only a partial role in the final decision made by the consumer. According to Keller and Lehmann

(2006), consumers rely on well-known brands because they know that these brands are either of a higher quality or that the performance of the product is superior to that of the competition.

Studies have shown that more often than not, it is only when a lesser-known brand is offered at a lower price, that they are chosen over well-known brands (Sethuraman & Cole, 1999).

In one of the most famous consumer neuroscience studies, McClure et al. (2004) revealed that in some cases, brand name is everything. In this study, a comparison between Coca Cola and

Pepsi was made. Prior to the commencement of the study, it was established that there was roughly an equal preference for both Coca Cola and Pepsi. During the second phase of the experiment participants were shown either a picture of a Coke can prior to receiving Coke or a

Pepsi can prior to receiving Pepsi. Participants that received Coke showed significant levels of activation in the dorsolateral prefrontal cortex (DLPFC), the hippocampus and midbrain. No such finding was reported when Pepsi was delivered after participants viewed a Pepsi can.

Furthermore, when the delivery was preceded with a light instead of a Coke can, significant differences in activation were seen between the two forms of cues (Figure 7). According to

Mclure et al. (2004) suggest that the activation seen in the DLPFC, hippocampus and midbrain provides evidence that Coke possesses a greater wealth of cultural meaning than that associated with Pepsi.

As seen in the above study, functional magnetic resonance imaging (fMRI) is a useful means of measuring the significant levels of activation in the brain. As an area of the brain becomes more active, it requires more oxygen. It is these changes in oxygen levels that fMRI aims to measure. However, fMRI is not the only method utilised by researchers to understand how the brain reacts to stimuli. Another tool used to investigate brain activity within a consumer setting is the less prominent magnetoencephalography (MEG). In contrast to fMRI, MEG measures brain activity by recording the magnetic fields produced by the naturally occurring electrical currents in the brain. In a study conducted by Junghofer, Kissler, Schupp, Putsche,

Elling and Dobel (2010), investigated which brain regions were responsible for the early processing (>120ms) of man-made stimuli. During the study, two separate measures of consumer behaviour were collected. Self-report data was collected from participants via a survey in which they expressed activities related to their consumer behaviour toward specific brands of shoes or motorcycles. Moreover, a brain-based measure was also collected in which participants were exposed to images of different brands of motorcycles and shoes. The most interesting finding presented by Junghofer et al., was the discrepancy between the self-report data and the data collected from the brain responses. Explicitly, self-report data showed a clear difference in consumer behaviour and brand expertise between each gender, however this was less evident from the results of the brain measures. Figure 8 shows that although activity in the occipito- temporal regions differed between males and females, many participants showed rather similar activation to both shoes and cars.

Figure 7. Significant activations between Coke delivered following an image of a Coke can and Coke delivered following the presentation of a light cue. Significant activations were found bilaterally in the hippocampus, the left parahippocampal cortex, midbrain and dorsolateral prefrontal cortex. These findings were exclusively found with Coke (McClure et al., 2004) (with permission).

Figure 8. Image taken from Junghöfer et al. (2010) (with permission) indicating the difference in activation of the occipito-temproal cortex (except for the primary visual areas) in males and females when viewing motorcycles and shoes.

The study conducted by Junhöfer et al. (2010) present findings similar to those expressed in a number of pieces of research. It is repeatedly reported that a discrepancy exists between subjective and objective measures of consumer behaviour. From a marketing perspective, these findings illustrate the continuing problems that arise when consumers are asked questions in relation to their willingness to buy, rather than obtaining a response via subconscious processes.

In sum, the study conducted by McClure et al. (2004) presented findings that explain the success that Coca Cola has had over its rival, Pepsi. However, the only conclusions we can draw from this study is that there are strong neurocorrelates related to Coke, but not to Pepsi. Although we know that Coke has a greater wealth of knowledge associated with it in comparison to Pepsi, there is little we can do with these findings in terms of marketing. More specifically, we are unable to generalise the findings to that of other products, we are unable to draw conclusions as to why Coke has developed a greater amount of cultural meaning and Pepsi has not, and most importantly, we cannot use these findings to improve Pepsi as a product to help it better compete with Coke.

In a similar manner, the study conducted by Junhöfer et al. (2010) has self10no immediate translational value, however, there is the possibility that these findings can help companies to better understand trends in consumer behaviour. Furthermore, these trends can then be used to assist in the development in activities related to their products. Again, the only conclusion that we are able to draw from this study is that a product liked by consumers may initiate activation in the occipito-temporal cortex. However, although this study may be seen as useless for companies that have already released their products onto the market, companies that are looking to release their product and wish to investigate how well it will compete with existing products may find this study more relevant.

The inability to conduct studies that are translational is a major issue that is repeated time and time again throughout the consumer neuroscience literature. However, consumer neuroscience is still in its infancy and hopefully, as the technology and methods are better understood, it becomes possible to generalise the findings of such studies to the field of marketing.

4.2. Package design and labelling

It should come as no shock that a more appealing product is capable of initiating a much more positive emotion. Previous studies have shown that individuals have been seen to express heightened levels of emotion toward attractive product packaging in comparison to unattractive product packaging (see Honea & Horsky, 2011). Have you ever wondered why when you buy something as expensive as a piece of jewellery, the packaging is usually made to look rather plain. Some products that are assumed to be of high value, highly experiential and have a positive influence on sensory systems, have been known to be presented in rather plain boxes as this neutralises the expectations of the individual, thus results in intensified subsequent emotions (Honea & Horsky, 2011). In addition to modifying the emotional responses of consumers, product aesthetics are able to alter the expectations of consumers. In many cases, the effect that product aesthetics has on consumer behaviour can be seen without the use of any neuroimaging techniques. Simply, the modification of product packaging can be used to mislead consumers into believing that products are larger or hold more than they actually do. There are many reasons that companies modify the packaging of their products, however the focus of this section is not to report how product packaging is used to mislead consumers (European Parliament,

2012), but rather identify the areas involved in processing packaging using neuroimaging techniques.

So what happens at a non-conscious level that affects which products we find appealing and which of those we do not? When shopping, it is usually the case that the decisions we make are made non-consciously and in a matter of seconds (Milosavljevic, Koch & Rangel, 2011), so it is imperative that the product being marketed stands out from its competitors. So how do companies decide what their new product packaging should look like and whether or not the public will find them enticing? Well the answer lies with neuroscience. A new branch of neuroscience termed “neuroaesthetics” has been used to address the questions surrounding the way the brain is activated in the presence of product packaging. Given that much of consumer behaviour is driven by processes at a non-conscious level (Chartrand, 2005), it would be inappropriate to simply ask for a verbal response as to which product or packaging they would be more likely to purchase. Moreover, previous research has shown us time and time again that there are discrepancies between self-report measures (subjective measures) and neuroimaging measures (objective measures; Walla, Brenner & Koller, 2011).

Several studies conducted by Reimann, Zaichkowsky, Neuhaus, Bender & Weber (2010) investigated the effect that good aesthetic properties had on brain activity. Interestingly, the first two of their studies revealed that participants chose products with aesthetic properties more often than products with standard packaging, even when a well-known brand was used (Figure 9). It was also reported that participants took longer to make the choice that resulted in the product with the aesthetic packaging being chosen.

Figure 9. Left: Significant levels of activation in the vmPFC regarding brand and type of packaging (standardised vs. not standardised). Right: Percentage of activation change in the vmPFC (Reimann et al. 2010) (with permission).

Figure 10. Significantly larger levels of activation in the vmPFC (A), striatum, particularly nucleus accumbens (B), cingulate cortex (C), primary visual cortices (D), and precuneus (E) during aesthetic product presentations (Reimann et al. 2010) (with permission).

To assess which regions of the brain were responsible for the increase in affective processes, Reimann et al. (2010) conducted an fMRI study and found that participants engage specific brain areas when assessing aesthetic package design. More specifically, significant increases in activation were seen in the ventromedial prefrontal cortex (vmPFC), the striatum

(especially in the right nucleus accumbens) and also in the cingulate cortex (Figure 10). In addition, the heightened levels of activation in the vmPFC due to aesthetic packaging were witnessed for both well-known and unfamiliar brand names.

Each of the abovementioned regions of the brain plays an important role in the processing of aesthetic features of products. The literature repeatedly shows that the vmPFC becomes activated when an individual is rewarded (McClure et al. 2004; Plassmann et al., 2012). In this case, the reward was considered to be when participants saw a product that possessed aesthetic properties. Similarly, the striatum (in this case the right nucleus accumbens) also plays a role in the processing of aesthetic properties. However, in contrast, the striatum becomes activated when participants anticipate a reward. According to Reimann et al. (2010) these regions of the brain work in sync at the point when the consumer views a product with aesthetics.

In another study that focused on the way that products are labeled rather than the way that the products are packaged, it was reported that in obese individuals, several regions of the brain are more highly activated when an item of food is perceived to be of a higher calorie content

(Ng, Stice, Yokum&Bohon, 2011). During this study, an identical milkshake was delivered to obese and lean participants (based on their body mass index), however one was labeled as low fat and the other as regular. Obese participants were seen to show higher levels of activation in the somatosensory, gustatory, and reward evaluation regions when presented with a regular milkshake versus an identical milkshake labeled ‘low-fat’.

Figure 11. Difference in activation between lean and obese women. Activation of the caudate was due to anticipated intake of high-fat versus water. Activation of the frontal operculum was due to anticipated intake of high fat versus low fat milkshakes. Activation of the Rolandic operculum was due to the receipt of high fat versus low fat milkshake (Ng et al. (2011)) (with permission).

With regard to the obese individuals in their study, Ng et al. (2011) found significantly higher activation of the Rolandic operculum (gustatory cortex) and caudate. These areas were reported as becoming activated when participants anticipated the intake of food. In addition, obese women were also seen to have a more active posterior cingulate gyrus and hippocampus, parrahippocampalgyrus and vmPFC. Ng et al. stated that these areas may have been responsible for the encoding of the reward value. Figure 11 shows the difference in the activation of the caudate, operculum and Rolandic operculum between lean and obese women.

According to Ng et al. (2011), the findings from their study offer an explanation as to why obese people remain obese even when they focus on eating low-fat foods. When an individual eats a food that is high in calories, the reward experience during consumption increases the expectation of reward, thus eating continues and may result in overeating. In contrast, when eating low calorie foods, people may overeat in order to compensate for the relative reduction of pleasantness and reward.

This study provides an excellent example of the translational value of consumer neuroscience. Although it does not allow businesses to increase the monetary value of its products, it identifies why consumers behave differently depending on the labeling of the product. There is no doubt that the findings presented by Ng et al. (2011) may be beneficial to not only the health industry, but the way that supermarkets interact with consumers. Many supermarkets promote products that are ‘low fat’ with the expectation that consumers will be buy these products and consume less calories, however it may be having the opposite effects.

Moreover, the study conducted by Ng et al. (2011) shows that obese people show more activation in several brain regions when they are not only expecting to receive food, but when eating something that is labeled as low fat rather than regular. Although the findings of Reimann et al. cannot be generalized to specific marketing contexts at present, the ability to generalize these findings to specific products and consumer scenarios will become better understood as this field continues to grow.

4.3. Final statement

It is undeniable that consumer neuroscience is of benefit to both the research and marketing world. However, it is possible that the technology reported within this chapter is not well enough understood to be able to generalise the findings to the field of marketing and have it result in benefits to a company. The studies presented above show a correlation between neuroimaging and buyer behaviour, however the ability to generalise these findings to specific consumer contexts is difficult. From the studies provided throughout this summary, it can be speculated that the technology being used in consumer neuroscience studies may be far ahead of our comprehension. However there are more basic forms of neurophysiological technologies available which appear to be much more promising.

In addition to the use of fMRI or MEG within a consumer neuroscience setting, another recent development within this field is the use of startle reflex modulation (Walla, Brenner &

Koller, 2011). This method involves the presentation of several stimuli, some of which are associated with a loud startle probe designed to initiate a startle response in the participant. In humans, it is found that for pleasant or positive stimuli, the startle response is reduced in comparison to that witnessed when unpleasant or negative stimuli are presented. The startle reflex has been used within a marketing context (Walla et al., 2011; Grahl et al., 2012) and provides a direct measure of emotion that can be directly linked to a participant’s like or dislike.

In Walla et al.’s study (2011), it acknowledges that a discrepancy exists between participants’ stated preference for a brand and what their startle reflex shows. In addition, it shows a significant difference in eye blink response between liked and disliked brands (Figure 12).

Figure 12. Mean eye blink response to liked versus disliked brands. (with permission)

It is plausible that the findings presented by Walla et al. (2011) indicate that physiological techniques may be more beneficial in the earlier stages of product development.

Through the use of startle reflex modulation; it may be possible to determine whether or not consumers are likely to react positively to a product before it reaches markets. This process may also allow businesses to determine how to make their products more appealing before they are marketed. It is clear that the potential of neuroscience to benefit the marketing world is present, however it may be a few more years away.

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Paper One

Established liked versus disliked brands: brain

activity, implicit associations and explicit

responses

Reference for publication:

Bosshard, S. S., Bourke, J. D., Kunaharan, S., Koller, M., & Walla, P. (2016). Established liked

versus disliked brands: brain activity, implicit associations and explicit responses. Cogent

Psychology, 3: 1176691. doi: 10.1080/23311908.2016.1176691

Co-author statements

By signing below I confirm that Shannon Bosshard contributed the majority of written content in the paper/publication entitled: Bosshard, S. S., Bourke, J. D., Kunaharan, S., Koller, M., &

Walla, P. (2016). Established liked versus disliked brands: brain activity, implicit associations and explicit responses. Cogent Psychology, 3: 1176691. doi: 10.1080/23311908.2016.1176691

07/06/2016

Jesse Bourke Date

06/06/2016

Sajeev Kunaharan Date

21/07/2016

Monika Koller Date

22/07/2016

Peter Walla Date

Faculty Assistant Dean Research Training Date

Abstract

Consumers' attitudes towards established brands were tested using implicit and explicit measures. In particular, late positive potential (LPP) effects were assessed as an implicit physiological measure of motivational significance. The implicit Association Test (IAT) was used as an implicit behavioural measure of valence-related aspects (affective content) of brand attitude. We constructed individualised stimulus lists of liked and disliked brand types from participant’s subjective pre-assessment. Participants then re-rated these visually presented brands whilst brain potential changes were recorded via electroencephalography (EEG). First, self- report measures during the test confirmed pre-assessed attitudes underlining consistent explicit rating performance. Second, liked brands elicited significantly more positive going waveforms

(LPPs) than disliked brands over right parietal cortical areas starting at about 800 ms post stimulus onset (reaching statistical significance at around 1000 ms) and lasting until the end of the recording epoch (2000 ms). In accordance to the literature this finding is interpreted as reflecting positive affect-related motivational aspects of liked brands. Finally, the IAT revealed that both liked and disliked brands indeed are associated with affect-related valence. The higher levels of motivation associated with liked brands is interpreted as potentially reflecting increased purchasing intention, but this is of course only speculation at this stage.

Keywords: attitudes; brands; EEG; neuromarketing; Information Technology;

NeuroIS

1. Introduction

1.1. Background

Every day we are presented with stimuli that require evaluating. Until recent years, the majority of attitude research was conducted within traditional social psychological studies.

However, as competition between businesses grew, and the need for product differentiation became a necessity, emphasis was placed on investigating attitudes within consumer contexts.

When making consumer based decisions, our attitudes towards a brand play a major contributing role regarding whether we make a purchase or not. As a result, attitudes have recently received a large amount of interest within the field of consumer neuroscience. This field has progressively integrated novel methods of assessing this phenomenon in both basic and applied contexts

(Morin, 2011).

Whether a company is trying to introduce a new brand or promote an existing brand, they are faced with the question of how to assess consumers’ attitudes, especially as a consequence of utilising marketing strategies to modify attitudes. Current marketing literature refers to brand attachment when attempting to identify a consumers attitude towards a brand. Brand attachment refers to the strength of the bond between the consumer and the specific brand/product (Park et al., 2010). The strength of this bond is said to act as a good indicator of the brand’s profitability and the customer’s perceived value of the brand (Thomson et al., 2005).

It is crucial to use a multidimensional approach and use as many measures as possible to quantify the various aspects of brand attitude as brands themselves are considered to be multidimensional concepts (Aaker, 1997). This approach will complete traditional approaches that rely on surveys and other methodologies that require explicit responses only. Explicit attitudes are deliberate and contemplative evaluations formulated through reasoning (Gawronski

& Bodenhausen, 2006). The act of reasoning does have the potential to result in a form of cognitive pollution whereby the evaluation of a stimulus results in the response becoming polluted by cognitive processes (Walla et al., 2011; Walla & Panksepp, 2013). The lack of acknowledgement of implicit factors consistently produced discrepant findings (for review see

De Houwer et al., 2001). Various recent cases demonstrate discrepancies between explicit and implicit measures (Grahl et al., 2012; Geiser and Walla, 2011; Walla et al., 2013) and as a result, there has been a recent turn towards implicit measures of attitudes, which are able to provide an insight into raw affective processing while also providing researchers and practitioners with a more complete picture related to brand attitude. For instance, Walla et al (2010) showed that virtually walking through urban environments can result in different effects depending on explicit or implicit measures; Dunning et al. (2010) found a non-linear relationship between the intensity of angry faces and non-conscious measures. Similarly, Grahl et al. (2012) reported that even specific bottle shapes can elicit a non-conscious change whilst explicit ratings remain constant. In case implicit and explicit measures match up, the complete picture represents strong assurance, and if they don't match up, there is reason to believe that this discrepancy reflects differences between conscious and non-conscious processing. Those differences could be useful to help shaping products and/or marketing strategies.

More recent research has suggested that attitudes are, in many ways, driven by nonconscious processes thus, measures sensitive to these aspects of attitudes are required. In contrast to explicit attitudes, implicit attitudes are evaluative associations automatically activated in the presence of a relevant stimulus, regardless of conscious intentionality for evaluation

(Cunningham et al., 2005). This means that both positive and negative evaluations can occur without conscious awareness (Devine, 1989). This automatic nature of implicit evaluations reinforces their conceptualisation as non-conscious processes (Dijksterhuis, 2004). Furthermore, implicit attitudes are shown to be considerably robust (Petty et al., 2006) and better predictors of spontaneous behaviour. 1.2. Implicit Measurements

Of the behavioural (non-physiological) implicit measures, the Implicit Association Test (IAT; see Greenwald et al., 1998) is arguably the most popular and effective responselatency-based implicit measure. The IAT has been used primarily as a tool within social psychology to determine implicit attitudes and stereotypes of social constructs including race

(ecomorphological group) and gender (Banaji & Greenwald, 1995; Banaji & Hardin, 1996;

Greenwald & Banaji, 1995; Greenwald, et al., 2002; Greenwald & Farnham, 2000; Dovidio et al., 2002; Fazio et al., 1995; Greenwald et al., 1998). In recent times however, the use of the IAT has extended into fields including marketing research (Brunel et al., 2004; Maison et al., 2001).

Nevertheless, it has to be mentioned that the IAT has been met with a number of criticisms regarding legitimacy as a reliable and valid index of implicit attitudes (De Houwer, 2006; De

Houwer et al., 2007; Fiedler et al., 2006; Hofmann et al., 2005). According to Rothermund and

Wentura (2004), rather than the IAT measuring implicit associations, it may instead provide an indication of salient asymmetries. Similarly, Mitchell (2004) found that when completing the

IAT, participants sort the stimuli into two categories; one that is accepted and another that is rejected. From these findings, it is possible that the IAT does not measure attitudinal aspects of a stimulus, but instead reflects the means by which participants have sorted the stimuli.

Electroencephalography (EEG) has been demonstrated as a useful physiological technique for obtaining implicit information through a number of approaches. For example, non- conscious verbal memory traces have been shown (e.g. Rugg et al., 1998). Although a limited number of papers have investigated attitudes using EEG, even fewer of these papers are related to consumer neuroscience (for review see: Wang & Michael, 2008). However, of the few papers that are seen to investigate attitudes using EEG within consumer contexts, it has been proposed that EEG can differentiate between brand related stimuli containing either a positive or negative valence. Handy et al. (2008) found that when participants rated unfamiliar logos as positive, these stimuli elicited more activity than those that were rated as negative across frontal and parietal regions as late as 600ms.

Further evidence of EEG as suitable means in determining differences between positive and negative stimuli within marketing contexts was put forth by Vecchiato et al. (2010). Rather than investigating brain activity related to positive and negative logos, Vecchiato et al. investigated brain activity in relation to TV commercials. Their research revealed that TV commercials that were rated as pleasant resulted in higher levels of activity than those rated as unpleasant (Vecchiato et al., 2010). Again, it was reported that frontal and parietal areas were largely involved in the processing of the commercials.

Although the literature is scarce, it is clear that EEG reveals some insight into an individual’s attitudes and motivation. Through the analysis of asymmetrical activity across the prefrontal cortex, Davidson et al. (1979) suggested that high activity across the left frontal hemisphere is associated with positive emotions and high activity across the right frontal hemisphere is associated with more negative emotions. Since this report, motivational components have also been identified with relative higher left and right activity being associated with approach and avoidance systems respectively (Harmon-Jones, 2004). The asymmetry model has recently proved informative in numerous consumer contexts (e.g. Brown et al., 2012; Ohme et al., 2009, 2010; Ravaja et al., 2013; Solnais et al., 2013).

From these findings, it can be inferred that through the use of EEG, we may be able to identify a link between brain activity and consumer brand attitude. Of most interest for the present study, the most empirically valid EEG approach as an index of motivation and affect has been a distinct event-related potential (ERP) component, the Late Positive Potential (LPP; see

Moran et al., 2013). It has not only been implemented in an expansive volume of research, but also recently received psychometric endorsement (see Moran et al., 2013). According to the literature, stimuli that are emotionally arousing produce an enhanced LPP compared to neutral stimuli (Cacioppo et al., 1993, 1994; Cuthbert et al., 2000) and those with higher motivational significance produce larger LPPs (Lang et al.,1997). An overall greater LPP sensitivity has been found in the right hemisphere during evaluative tasks (Crites & Cacioppo, 1996).

1.3. The Present Study

The rationale for the present study was to use the IAT to prove that explicitly rated brands that are liked are indeed associated with positive affect and disliked brands with negative affect. In addition, via EEG recordings we aimed at testing whether or not liked and disliked brands are further associated with different motivational aspects. The present study also extends upon the study by Walla et al. (2011) in that it adds further implicit measures to measure brand attitude. They too investigated brand attitude, but focussed on startle reflex modulation, heart rate and skin conductance. No studies addressing the sensitivity of ERPrelated attitudes measures were expressed in this paper, and to our knowledge remain absent in the current existing literature. Furthermore, in contrast to much of the existing literature, the current study focuses on individual’s perceptions of highly familiar brands. We used an online survey to produce individual lists of liked and disliked brands and then invited eligible participants to record brain potentials and take IAT measures. We first hypothesised that self-reported measures during physiological recording would strongly reflect explicit pre-assessment ratings. Following the existing literature we expected the LPP component to vary as a function of brand attitude allowing us to make inferences about affect-based motivational aspects. Finally, we expected

IAT data to also support differences between liked and disliked brands and thus prove to be reliably reflective of brand attitude. 2. Methods

2.1. Participants

Initial recruitment for the study involved 27 participants, three of whom were excluded following pre-assessment of brand attitudes. The mean age of the remaining 24 participants (12 females) was 23.58 (SD = 2.39). All participants were tertiary education students recruited by word of mouth. They volunteered and gave their written informed consent. Participants were right handed, had normal or corrected to normal vision, were free of central nervous system affecting medications and had no history of neuropathology. They were also asked to not drink any alcohol or coffee and to not smoke for at least 24 hours before the experiment. Participants were financially reimbursed for their time and travel. The study was approved by the Newcastle

University Ethics Committee.

2.2. Stimuli

The initial stimulus list for pre-assessment comprised 300 subjectively chosen common brands names, familiar to people from Australia (See Appendix A for list of presented brand names). Using an online survey, participants provided a subjective rating of like or dislike for each brand name on a 21-point analogue-type scale, ranging from -10 (Strong Dislike) to +10

(Strong Like). Upon initiation of the experiment, we created individualised stimulus lists using the subjective ratings obtained from the online survey. Each stimulus list comprised 200 brand names: including the participant’s 30 most liked brand names, 30 most disliked brand names, 60 neutral brand names, and 80 non-target (filler; also neutral brands but were not conditioned in subsequent sessions) brand names. This accumulated 120 target brand names across three types; positive, negative and neutral. Brand names were presented in capital white letters, Tahoma font and on a black background (no logos were presented). In the frame of this paper only measures related to liked and disliked brands are further analysed. 2.3. Individual pre-assessment of brand attitudes

Participants subjectively rated 300 brand names using an online survey (via www.limesurvey.com), prior to entering the lab. We required participants to read each brand name and indicate their attitude towards it using a mouse/trackpad on the provided slider.

Participants were explicitly instructed to not adjust the slider if they were unfamiliar with a particular brand. Rating a brand as neutral required the participant to manually click “0”. This phase of the experiment occurred at a time of the participant’s choosing, with choice of computer also left to their discretion. The survey took on average 15-20 minutes to complete. Participants who demonstrated adequate familiarity and attitude scope were eligible for the experimental phase of the study. That is, participants who were either unfamiliar the majority of the brands, or did not have a large spread of attitudes (ranging from strongly liked to strongly disliked) were excluded from the experiment. This came as a result of not being able to construct a stimulus list with discernable positive and negative target items. Three participants were unable to further participate due to such inadequate brand pre-assessment.

2.3.2. Lab experiment

Following completion of pre-assessment, we invited eligible participants individually into the lab. Participants were encouraged to attend the lab for their first within three days of having completed the online survey. During their visit, we collected all explicit and implicit measures of attitudes towards brand names. Explicit measurement involved subjective self-report, whilst implicit measures were collected using electroencephalography (EEG) and the IAT. Upon entering the lab, participants were seated comfortably in front of a 32 inch LED television

(screen resolution of 1024x768 pixels). We connected participants to a BioSemiActiveTwo EEG system (BioSemi, Amsterdam, The Netherlands) and measured potential changes using 64 cranial electrodes, as well as eight external reference electrodes placed lateralocularly, supraocularly, infraocularly and on the mastoids. We used the computer program Presentation (NeuroBehavioral Systems, Albany, United

States) to visually present the appropriate instructions and individualised stimulus lists. The presentation of stimuli in addition to neurophysiological signal recording was conducted from a separate room. We commenced testing with the participant by themselves in a dimly lit room to ensure adequate focus on the stimuli. A white fixation-cross appeared on a black background for

500 ms, followed by a brand name for 5s. Participants provided a self-reported rating of 1

(Strong Dislike) to 9 (Strong Like) for the brand using a standard keyboard, whilst it was on screen. Brain potential changes and self-report were collected for the 120 target brands. To reduce fatigue effects participants were provided a break halfway through this stage. Overall, it took approximately 30 minutes to complete. Participants were then asked to complete 5 rounds of the IAT (see Figure 1 for modified IAT).

Figure 1: Modified version of the original IAT (adapted from Greenwald et al.,

1998). Filled black circles on the left of the stimulus indicate left button presses and

vice versa. Task 3 = congruent, Task 5 = Incongruent condition.

2.4. Data Recording and Processing

2.4.1. Self Report and Implicit Association Test (IAT)

For self-report data, mean ratings of liked and disliked brands were compared using paired-sampled t-tests. These analyses were completed at both the pre and post assessment phases. As for the IAT, we used a modified version of the original test (Greenwald et al., 1998), which consisted of 5 separate discrimination tasks each with 30 visual presentations to be classified as either a target or non-target stimulus. Although the structure and administration of the IAT remained identical to the original IAT, rather than using stimuli that fall under the guise of social psychology (eg. Faces of different races; Greenwald et al., 1998), we instead used brand names. In task 1 (initial target concept) study participants were asked to discriminate between visual stimuli either related to their individually rated most liked brand (target brand) or related to their individually rated most disliked brand (non-target brand). Study participants were required to press the ‘A’ key for target brand and the ‘L’ key for non-target brand. In task 2

(associated attribute) participants were visually presented with valenced words and asked to press the ‘A’ key for pleasant words (eg. beautiful, healthy, happy, perfect) and the ‘L’ key for unpleasant words (eg. frighten, angry, sad, worthless). In task 3 (initial combined task) tasks 1 and 2 were combined. Study participants were asked to press the ‘A’ key in case of target brand or pleasant words and the ‘L’ key when presented with a negative word or a non-target brand.

Task 4 (reversed target concept) was similar to task 1, however participants were asked to press the ‘A’ key for non-target brands and the ‘L’ key for target brands. Finally, task 5 (reversed combined task) was a combination of task 2 and task 4. Participants were required to press the

‘A’ key in case of non-target brands and pleasant words and the ‘L’ key when presented with a negative word or a non-target brand. In accordance with existing literature (De Houwer et al.,

1998), a comparative analysis was made between reaction times of participants during task 3 and task 5. For a pictorial explanation of how the IAT was implemented, see Figure 1.

2.4.2. Event related potentials

We recorded EEG at a rate of 2048 samples/second using a 64-channel

BioSemiActiveTwo system and ActiView software (BioSemi, Amsterdam, The Netherlands).

Data sets were processed individually using EEG-Display (version 6.3.13; Fulham, Newcastle,

Australia). During processing we reduced the sampling rate to 256 samples/s and applied a band pass filter of 0.1Hz to 30Hz. Blink artefacts were corrected by referencing to the supraocular external electrode (excluding two sets referenced to Fpz due to unclean external signals). In order to eliminate noise generated by eye movements, we conducted horizontal, vertical and radial eye movement corrections (see. Croft & Barry, 1999). The data was coded to brand type

(i.e., liked, disliked). We established epochs from -100 ms prior to stimulus onset (a baseline), to

2000 ms following stimulus onset. The resultant epochs were baseline corrected and an average was generated across single trials for each condition. The individual data sets were then re- referenced to a mastoid electrode. Grand averaged ERPs were generated to display brain activity differences. Grand averaged ERPs were then analysed in 200 ms (between 200ms and 1800ms) blocks using ttests to compare mean activity during these periods (200 ms-400 ms, 400 ms-600 ms, 600 ms-800 ms etc.)

3. Results

3.1. Self-report at pre-testing

To analyse the self-report data, the responses towards participants most liked and most disliked brands were collated. We then conducted a paired samples t-test on these two conditions and found that on average, the mean of self-reported liked brands (the top 30 most liked) was

9.44 (SD = 2.49) and the mean of disliked brands (30 least liked brands) was -4.56 (SD = 5.41; see figure 2). As expected, this effect was seen to be highly significant (t = 25.765, df = 118, p <

0.001, two tailed; ԁ = 3.54).

Figure 2: Mean (30 most liked and 30 most disliked) self reported brand name rating

during the online survey. Ratings are based on a scale from -10 (maximum disliked)

to +10 (maximum liked) (error bars included).

3.2. Self-report during the lab experiment

In order to assess self-report responses towards liked and disliked brands during the lab experiment, we collated all responses towards participants most liked and most disliked brands.

We then conducted a paired samples t-test to assess the sensitivity of self-report to pre-assessed explicit brand attitudes. Consistent with predictions, self-report measures differed significantly according to brand type also during physiological recording (t = 21.721, df = 118, p < 0.001, two tailed; ԁ = 3.03). As expected, liked brands (M = 7.39, SD = .98) were rated significantly higher than disliked brands (M = 3.39, SD = 2.03; see Figure 3).

Figure 3: Mean self reported brand name rating during the physiological recording

test session. Again, 30 most liked and 30 most disliked brand names. Ratings are

based on a scale from 1 (maximum disliked) to 9 (maximum liked) (error bars

included).

3.1.3. Event related potentials

We produced averaged ERP figures to broadly assess effects of brand type over the entire epoch of interest. Visual inspection of overlaid ERPs revealed strongest LPP differences between liked and disliked brands at frontal site AF7 and parietal sites P7 and P8 (see figure 4). We then conducted paired t tests on all electrode sites to compare brand effects.

Figure 4: Grand averaged ERPs related to disliked and liked brands. At P8 liked

brands elicited a more positive going potential compared to disliked brands.

Unexpectedly, we saw no significant effect across left frontal electrode site AF7 for the entire duration of the epoch, however we did see a pattern emerging which saw greatest significance at about 1400 ms (t = -1.773; df = 23; p = .089; two tailed; ԁ = .51). In contrast, parietal site P8 saw liked brands evoke more positive activity throughout majority of the ERP.

This effect was seen to begin at around 1000 ms (t = -1.578; df = 23; p = .019; two tailed; ԁ =

0.59) and remain until 1800 ms, reaching greatest significance at around 1400 ms (t = 3.110; df =

23; p = .005; two tailed; ԁ = 0.66). Analysis on left parietal site P7 revealed no significant brand effect with greatest significance achieved at around 1200 ms (t = -1.421; df = 23; p = .169; two tailed; ԁ = 0.26). Figures 4 and 5a shows the dominant LPP effect over the right parietal area in relation to liked brands.

Figure 5: Topographical maps demonstrating a most pronounced LPP over the right

parietal cortical area in response to liked brands.

3.1.4. Implicit Association Test During analysis of the IAT responses, we compiled all participants‟ responses and found the mean reaction time for each phase. We then removed all responses that were provided either too quickly or too slowly. All responses that fell three standard deviations (calculated in milliseconds) from the overall mean reaction time of each phase were removed. We also removed all incorrect responses. We then analysed the data regarding participants most liked brands (see Figure 6). We conducted a paired t-test and consistent with predictions found that there was a significant difference in reaction time between the congruent condition (M = 607.47 ms, SD = 117.95) and the incongruent condition (M = 677.70 ms, SD = 186.96) (t = -6.457; df =

344; p < 0.001; two tailed; ԁ = 0.46). We then proceeded to conduct an analysis of participants‟ responses towards disliked brands (see Figure 6). We again, as expected, found a significant difference between the congruent condition (M = 630.42 ms, SD = 164.56) and incongruent condition (M = 693.06 ms, SD = 194.03); (t = -4.505; df = 309; p < 0.001; two tailed; ԁ = 0.35).

Figure 6: IAT findings demonstrate that our participants had automatic positive

associations with prior rated liked brands and negative associations with prior rated

disliked brands. The implicit nature of the IAT might be useful in the future to test evaluative conditioning effects without requiring explicit responses (error bars

included).

4. Discussion

The findings of our study are two-fold. First, we provide evidence that like and dislike as in brand attitude are indeed associated with deep positive and negative affect and second, we demonstrate that liked brands are implicitly associated with increased motivational aspects compared to disliked brands. Although purely speculative at this stage it might be reasonable to believe that this is reflective of increased purchasing intentions related to liked brands.

4.1. Self-report and IAT

Congruent with our predictions, self-reported measures during the lab experiment strongly reflected those obtained during pre-assessment even though the contexts in which both sets of data were collected varied considerably. This indicates the consistent nature of explicitly rated brand like and dislike in the frame of our study. Prior to entering the lab, participants were required to rate brand names using a 21-point scale and were not under any time constraints, while participants were only allowed a few seconds to respond using a ninepoint scale during neurophysiological recording. Cunningham and Zelazo (2007) state that explicit attitudes are ultimately influenced by two competing motivational drives, to reduce error and reduce cognitive demand. As individuals are allowed to take more time to make decisions, their accuracy is said to increase, however the cognitive load also increases. In contrast, when under time constraints, participants are able to reduce cognitive load, however the chance of errors increase respectively.

With regards to the current study, the pre-assessment phase saw participants take more time to respond, thus their responses were thought to have been more accurate and, in turn, require an increased cognitive load. In contrast, during the physiological recording phase, where participants only had a limited time to respond, the cognitive load was less, but room for error increased. Our results may indicate a trade-off between these two motivations and this may have contributed to the congruent ratings. Such considerations are important when comparing explicit attitudes obtained over different contexts (Stafleu et al., 1994). However, most importantly we could confirm that explicit rating performance revealed same results when compared across two different measurement times.

In principle, the IAT has been developed as a measure of a person's automatic and thus rather implicit association between valence-related information and stored mental representations of any content or concept (Greenwald et al., 1998). In our study the IAT was used to test whether or not implicit associations between positive valence and liked brands and negative valence and disliked brands exist. The results strongly support this hypothesis. Given that like and dislike in our study is reflective of brand attitude, the current research provides further support that the IAT is a suitable means of distinguishing between positive and negative attitudes on a rather non- conscious level, which is consistent with previous research (e.g. Brunel et al., 2004). The results show that reaction time is significantly reduced when participants responded to a liked brand in the presence of a pleasant word and also when a disliked brand is presented with an unpleasant word (congruent condition). In contrast, the results also show that there is a significant increase in participant’s reaction time when responding to liked brands in the presence of negative words and also for negative brands in the presence of positive words (incongruent condition) indicating a lack of association between those two informations. However, it should be noted that our data does not support (or refute) the assumption that the IAT directly measures implicit facets of attitudes, even though we strongly believe that this is the case.

As previously mentioned, the IAT has been met with criticisms regarding its ability to measure implicit attitudes (see De Houwer, 2006) and, although it may be useful as an implicit measure within consumer research, it should be used cautiously. According to Boysen et al.,

(2006) people may be able to influence their responses on the IAT and, as a result, alter the outcome of this supposed automatic, implicit task. Therefore, the authors of the current paper suggest that the IAT be used in conjunction with other implicit measures. Further research is needed to define the value of the IAT. 4.2. Event related potentials

Within social psychological studies, negative and positive stimuli are considered to be more inherently affective (i.e., out-group prejudices etc.) and are often evolutionary based mechanisms (i.e., detecting threats; Brewer, 1999) that are both associated with increased motivational levels. In our study, we found evidence that liked brands elicit significantly higher levels of motivation compared to disliked brands, which is interesting. Brand name attitudes are entirely learned and highly semantic (Stuart et al., 2001). This is supported by findings that brand attitudes can be derived and shaped without the individual actually having any direct experience with the brand (Ahluwalia et al., 2000; Sweldens, Van Osselaer, & Janiszewski, 2010). This might be a reason for the discrepancy in level of motivation.

Although the lateralised dominance of an enlarged LPP for liked brands to the right hemisphere is in contrast to numerous studies on social attitudes which suggest that the left hemisphere displays a greater LPP for positive attitudes, other research has demonstrated that the right hemisphere is generally more sensitive to LPP effects (Cacioppo et al., 1996). There is considerable consensus that this right hemisphere bias in evaluative processing, is modulated by the level of motivational significance of the stimulus (Cacioppo et al., 1996; Cacioppo et al.,

1994; Cunningham et al., 2005; Cuthbert et al., 2000; Gable & Harmon-Jones, 2013). This understanding of the LPP is very much in line with our own view and we interpret our findings to infer that liked brands, although generating more activity, may not have been perceived to be significantly more implicitly positive than disliked brands, but were more motivationally arousing. More research into these findings is necessary before clearer conclusions can be drawn.

The considerably late onset of the LPP in our findings further support the suggestion that perhaps, brand attitudes are highly iterated and reprocessed constructs. A number of studies have shown significant motivational discrepancies using the LPP as early as 300ms to 400ms

(Olofsson et al., 2008; Pastor et al., 2008). The LPP onset of roughly 1000ms in our study infers that considerably more processing occurred before the stimuli were distinguished as either liked or disliked (see Falkenstein et al., 1994). This late onset could also be a reflection of the use of well-known brands rather than those, which are fictitious (as seen in Handy, 2010).

Finally, it has to be mentioned that our data regarding frontal sites, although only a trend and not significant, supports existing literature (Davidson et al., 1979; Harmon-Jones, 2004) that liked or positive stimuli evoke greater potentials than disliked or negative stimuli across the left prefrontal cortex. From this finding, we can infer that like other affective stimuli, brands that are liked or more motivationally arousing result in higher potentials across the left prefrontal cortex more so than do disliked or aversive brands; and that this higher level of activity may give an indication of a participant’s purchase intention. Although this is only speculation at this stage, it helps forming new hypotheses for future studies with a strong applied aspect.

Although the LPP has been explored in consumer contexts, to our knowledge previous studies have used only novel stimuli (Handy et al., 2010). Our study increased external validity by assessing brand attitudes previously formed in everyday life. The pre-assessment phase further increased the utility of this approach by ensuring strength of subjective participant attitudes. We acknowledge that experimental control is important and more easily obtained using unfamiliar stimuli. However, attitude formation and change does not occur in a vacuum and translatability of research is of particular importance in consumer neuroscience. We therefore recommend further use of established brand stimuli such as those used in the present study. To further expand on the use of existing brands, we also suggest assessment of stimuli such as familiar brand logos and products. These have shown to strongly activate neural systems of familiarity in functional magnetic resonance imaging paradigms (Schaefer et al., 2006; Tusche et al., 2010) and may demonstrate effects unique from brand names. Moreover we emphasise the requirement of ensuring appropriate procedures during pre-assessment, such as controlling for factors that influence evaluative error and cognitive demand. The IAT is a cognitive index of implicit attitudes further higher-order than ERP, to the point of being susceptible to cognitive bias (De Houwer, 2006). Given its popularity for attitude assessment (De Houwer, 2006; Gattol et al., 2011; Hofmann et al., 2005), it may prove useful to consolidate this traditional response-latency measure with such contemporary ERP techniques for a broader scope of attitudes.

4.3. Conclusions

In the present study, self-report, ERP measures and the IAT were demonstrated to be sensitive to pre-assessed brand attitudes. The effects observed using ERP specifically affirms higher-order motivational processes as potentially underlying contributors to our explicit results.

A larger LPP effect over the right parietal cortex for liked brands inferred greater motivational significance for liked compared to disliked brands. The IAT results prove that brand attitude is indeed associated with deep affective content. In summary, even though both liked and disliked brands are associated with affective content, liked brands elicited significantly higher levels of motivation levels, which might be reflective of increased purchasing intentions related to liked brands.

Further research expounding the different mechanisms involved in evaluative processes should likewise prove beneficial for understanding attitudes generally and in applied contexts.

Broadly, the implications of our own, and prospective related research may also provide clinical insight into severe consumer behaviours such as gambling and substance abuse and dependence

(Foxall, 2008). In conclusion, the present study serves as an important persuasion that ‘like’ it or not, brain and behaviour sciences have progressed to a point where sole use of self-report measures requires an attitude change in itself. It is our responsibility as researchers to ensure this progression is acknowledged and utilised so that future evaluative research optimally measures up.

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Paper Two

Can we change your opinion towards your most

favourite and most hated brands? Your brain

says yes even if your mouth says no.

Reference for publication:

Bosshard, S. S., Kunaharan, S., Koller, M., & Walla, P. (2016). Can we change your opinion

towards your most favourite and most hated brands? Your brain says yes even if your

mouth says no. Manuscript submitted for publication

Co-author statements

By signing below I confirm that Shannon Bosshard contributed the majority of written content in the paper/publication entitled: Bosshard, S. S., Kunaharan, S., Koller, M., & Walla, P. (2016).

Can we change your opinion towards your most favourite and most hated brands? Your brain says yes even if your mouth says no. Manuscript submitted for publication.

06/06/2016

Sajeev Kunaharan Date

21/07/2016

Monika Koller Date

22/07/2016

Peter Walla Date

Faculty Assistant Dean Research Training Date

Abstract

In the present study, using both implicit and explicit measures, we addressed the issue of whether strongly developed relationships towards brands could be modified through the use of evaluative conditioning. Using an online survey, individual participant brand lists were created and formed the basis of this experiment. Participants were then exposed to conditioning during three subsequent visits to the lab. Throughout the experiment, a combination of explicit and implicit measures was used to assess changes in attitude. Specifically, participants were asked to rate the brand names on a Likert-type scale. Simultaneously, changes in brain electric activity in response to the brands were recorded via electroencephalography (EEG). Upon completion of this task, participants underwent two Implicit Association Tests (IAT; one for liked brands and one for disliked brands). There were two main findings of this study. Firstly, no significant changes in attitude were observed via the use of self-report and the IAT. Secondly, conditioning effects revealed by ERPs were only seen for disliked brands and were greatest after only a single exposure. From these findings, we can infer that rather implicit cortical brain activity is sensitive to evaluative conditioning effects that do not turn into conscious experiences.

1. Introduction

For the majority of existing businesses, progressive growth is stimulated by the successful marketing of products and/or services. Consumers are driven to purchase brands or products they like, and avoid those they don’t. As a result, marketers are constantly looking to modify consumer’s attitudes towards their brands or products. Currently, the most utilised method of changing consumer attitudes is evaluative conditioning. This process involves the repeated pairing of a conditioned stimulus (CS; e.g. a brand name) with an unconditioned stimulus (US; e.g. affective sound or picture). Eventually, the repeated pairings result in a transfer of affect from the US to the CS. Although research suggests that evaluative conditioning can lead to an increase in brand awareness (Du Plessis, 1994; Hollis, 1995), sales (Haines,

Chandran & Parkhe, 1989; Haley and Baldinger, 1991) and brand positivity (Smith, Feinberg &

Burns, 1998), there is debate as to whether it can reliably change well established attitudes towards familiar brands (Smith et al., 1998; Gresham & Shimp, 1985).

Marketing literature constantly stresses the importance of brand identity on the consumer’s perception and decision making process (Dotson et al., 2012). Extant research shows that consumers use brand names as an indicator of performance and quality (Poulsen et al.,

1996). In addition, such literature posits that brand perceptions can even influence sensory processing (e.g. taste; Fornerino and d’Hautville, 2010; Rossi, Borges & Bakpayev, 2015).

Findings such as these stress the importance of branding. In a market where competitors can easily copy product characteristics, it is essential that a strong brand identity and personality are formed in order to build brand equity (van Rekom, Jacobs, & Verlegh, 2006). Understanding this importance, many companies have implemented conditioning paradigms in attempts to increase positive brand perceptions. The most common forms of conditioning involve businesses using heuristic stimuli such as music, celebrities, happy and fun scenes, and bright colours in order to persuade potential consumers that the product being offered is beneficial. Of these mediums, music is the most commonly assessed (Garlin & Owen, 2006) atmospheric variable when businesses try to connect with consumers’ emotions (Morrison & Beverland, 2003). According to Petty et al. (2005), when a consumer is not initially motivated to purchase a product, they will engage in the processing of heuristic cues. Research has shown that these types of stimuli can successfully be used to condition attitudes (e.g., Blair & Shimp, 1992; Gorn, 1982; Stuart,

Shimp, & Engle, 1987; Pleyers, Corneille, Luminet, & Yzerbyt, 2007; Walther & Grigoriadis,

2004). Although these effects are widespread when the stimuli are fictitious, the use of familiar stimuli has resulted in very different findings.

According to Stammerjohan et al. (2005), highly familiar brands are characterized by well-established, relatively stable attitudes that are somewhat resistant to the effects of advertising. Marketing related research into the effects of conditioning on brands has repeatedly suggested that neutral or unfamiliar brands are susceptible to such paradigms; however changing attitudes towards mature brands is often unsuccessful (Shimp et al., 1991; Cacioppo et al., 1992;

Kellaris & Cox, 1989). In cases where conditioning of mature brands is unsuccessful, the findings are explained by suggesting that people are more influenced by previous opinions than by new information (Weilbacher, 2003). As a result, their views are resistant to change. Latent inhibition (LI) is the process whereby people’s attitudes towards a stimulus are resistant to change as a result of previous exposure to that stimulus. Null findings as a result of latent inhibition are common within marketing literature (Gorn, 1982; Gresham and Shimp, 1985;

Stuart, Shimp & Engle, 1987). Brand name attitudes are entirely learned, highly semantic (Stuart,

Shimp, & Engle, 2001) and can be derived and shaped without the individual actually having any direct experience with the brand (Ahluwalia, Burnkrant, & Unnava, 2000; Sweldens, Van

Osselaer, & Janiszewski, 2010). This is in stark contrast to the literature presented previously that suggests that well-known brands are resistant to the effects of conditioning. If it were truly the case that attitude changes towards well-known brands was not possible, then this literature casts doubt on the entire industry of marketing. The main idea of the current paper is to suggest that the prominent and repeatedly reported null effects towards well-established brands occur as a result using tools that lack the necessary sensitivity to detect those changes in attitude. As mentioned previously, understanding consumer attitudes until recently has solely relied on self- report measures, which remains the preferred option of many marketing firms.

At this point, it is necessary that a distinction be made between explicit and implicit attitudes. Explicit attitudes are said to be contemplative and formulated through reasoning

(Gawronski & Bodenhausen, 2006). They are usually measured in terms of fully conscious responses such as ticking boxes, pressing buttons, or giving verbal responses. In contrast, implicit attitudes are associations that are automatically activated in the presence of relevant stimuli without any conscious awareness of evaluation (Cunningham, Raye, & Johnson, 2004).

These attitude aspects require more sophisticated methodological approaches (see later). The distinction between these two types of attitudes arose after constant reports suggested that people either cannot, or do not want to fully explain their preferences (Babiloni, 2012; Greenwald &

Banaji, 1995).

Attitude research that is seen to compare implicit and explicit attitudes constantly reveal discrepant findings (Gibson, 2008; Grahl et al., 2012; Geiser and Walla, 2011; Walla et al.,

2013). For instance, Walla et al. (2010) found that when eating ice cream, chocolate or yoghurt, although participants had no stated preference for a particular food item, implicit responses showed that ice cream was preferred. Similar findings were presented by Grahl et al. (2012), who reported that specific bottle shapes can elicit a non-conscious change whilst explicit ratings remain constant. More specifically, although both males and females stated that one particular bottle (out of three) was the least attractive, physiological responses (startle reflex) suggested that this bottle turned to elicit negative affect in only male brains.

Neuroscience is able to offer an alternative explanation to latent inhibition regarding the discrepant findings between these two attitudes. According to Walla, Brenner and Koller (2011) contemplation and reasoning inevitably give rise to cognitive pollution. In more simplistic terms, the more conscious processing that takes place during the evaluation of a stimulus, the less reliable/more polluted the response becomes, with respect to raw affective processing (see also

Walla & Panksepp, 2013). Given these discrepancies, it is proposed that cognitive pollution is an alternative explanation to the null findings where latent inhibition has regularly been presented in psychological studies.

Given all these findings, it is imperative to consider alternative means of assessing brand attitude that complete traditional approaches. Of the available measures, the Implicit

Associations Test is one of the most commonly cited measures of implicit attitude (IAT; see

Greenwald, McGhee, & Schwartz, 1998). Although its use is widespread within psychological studies, it is less frequently utilised to assess consumer attitudes. The IAT assumes that there is an associated network whereby concepts of memories, attitudes and valence are all intrinsically linked (Anderson & Bower, 1973; Collins & Loftus, 1975). The application of the IAT involves participants engaging in response latency tasks. During these tasks the relative strength of association between memory, attitude and valence of a stimulus is assessed. In more simplistic terms, it is inferred that the faster a participant is able to respond to a stimulus (or stimulus pair), the stronger the implicit association. For example Olson and Fazio (2001) used the IAT to assess the effects of conditioning. During their task, participants were presented with Pokemon characters intermixed with positive, negative and neutral pictures and words. The results revealed that participants responded to the stimuli faster during the compatible conditions than the incompatible conditions (for methods, see Olson and Fazio). Similar findings have been presented in several other social phenomena including prejudice (e.g., McConnell & Liebold,

2001), self-esteem (e.g., Greenwald & Farnham, 2000), and social identity (e.g., Greenwald,

Banaji, Rudman, Farnham, & Nosek, 2002; for reviews, see Fazio & Olson, 2003; Greenwald &

Nosek, 2001).

Although it appears to be the case that the IAT is a useful means of determining implicit attitudes within various contexts, it has not been without controversy. It has been proposed that the effects of the IAT can be affected by cognitive processes (Blair, 2002). Mitchell, Nosek and

Banaji (2003) revealed that participants not presented with valenced words, responded more favourably towards black individuals when compared to white individuals. However, when the race of the individuals was revealed to participants, a trend towards favouring white people emerged. In addition to these findings, Dasgupta and Greenwald (2001) found reduced race IAT effects when participants were shown favoured black people and disliked white people. These results cast doubt on the IATs ability to exclusively measure non-conscious affective components related to attitudes.

In contrast to the IAT, electroencephalography (EEG) is an emerging technique used to assess implicit attitude. Since this technology was applied to attitudinal research, there has been a general consensus that it is capable of distinguishing between positive (approach) and negative

(avoidance) affect (Davidson et al., 1979; see also Bosshard et al., 2016). The asymmetry model proposed by Davidson et al. suggests that greater relative left frontal EEG activity is associated with the processing of positive/approach affects, whereas greater relative right frontal EEG activity with the processing of negative/avoidance affects. Many studies have supported the idea of an asymmetry in emotional processing (Kayser, Tenke, Nordby, Hammerborg, Hugdahl &

Erdmann, 1997; Wheeler, Davidson & Tomarken, 1993; Gotlib, Ranganath, & Rosenfeld, 1998;

Wiedemann et al., 1999; Coan & Allen, 2003; Harmon-Jones, Sigelman, Bohlig, & Harmon-

Jones, 2003), however; within marketing research the use of EEG is rather limited.

Although it may be the case that the application of EEG to the domain of marketing may be scarce, the findings that have been presented are promising. In a study that assessed participant’s future purchase behaviour, Ravaja, Somervuori and Salminen (2013) reported that relatively greater left frontal activity was associated with an increased likelihood for participants to purchase goods. In addition, Ravaja et al. revealed that when participants were offered a product at a price that was below normal, this also resulted in heightened left frontal activation, indicating motivation to purchase the item. Moreover, their research also revealed that participants produced greater left frontal activity in the presence of well-known brands compared to private labelled brands (those that are of a lower price and considered to be of lower quality;

Hankuk & Aggarwal, 2003). Again, this finding was said to contribute to an increased likelihood to purchase the item. Finally, Ravaja et al. showed that perceived quality was positively associated with relatively greater left frontal activation. In terms of right frontal activity, excessive pricing is known to result in the activation of the right insula, an area linked with negative emotions including pain, disgust and sadness (Davidson, 2004). In addition, a reduction in activity across right prefrontal sites has been shown to predict higher monetary risk taking

(Gianotti et al., 2009) as well as a decrease in a participant’s likelihood to punish during the ultimatum game (Knoch, Gianotti, Baumgartner, & Fehr, 2010). In sum, it is clear that the literature within marketing contexts supports the notion that left frontal activation is indicative of a motivational approach state.

Although the asymmetry model posited by Davidson et al. is mentioned within numerous consumer neuroscience studies, it is not the only means by which EEG can be used to attain information relating to implicit brand attitude. Of interest to the present study is one of the most empirically valid EEG approach as an index of motivation and affect, the Late Positive Potential

(LPP; see Moran et al., 2013). The LPP is a late, positive event related potential (ERP) component that has its onset at around 300ms following stimulus onset and can exceed 5000ms.

The LPP has been extensively used within the literature and as a result, has received psychometric endorsement which revealed that the LPP demonstrated good to excellent reliability as a measure of emotion/affective processing (Moran et al.). Stimuli that are seen to be either more emotionally affective or more motivationally significant are said to evoke the largest

LPPs (Moran et al.). Specifically, a number of reports have shown that LPP effects across parietal sites are greater for both pleasant and unpleasant stimuli than those that are neutral

(Moran et al., 2013; Cacioppo et al., 1996; Cacioppo, Crites, Berntson & Coles, 1993). In addition, regardless of valence, LPP effects are reported to be greater in the right hemisphere than the left (Crites & Cacioppo, 1996).

Current study

The rationale for the present study is to assess well-established liked, disliked, and neutral brand attitudes using a multidimensional approach. In doing so, the sensitivity of implicit and explicit measures to detect changes in attitudes will be compared.

Initially, we used an online survey to produce individual lists of liked and disliked brands and then invited eligible participants back to record brain potentials and take IAT measures. We first hypothesised that self-reported measures during the first session of physiological recording would strongly reflect explicit pre-assessment ratings. With regards to conditioning, based on existing latent inhibition (LI) research, we hypothesise that self report data will show no effect.

Following the existing literature, we expected the LPP effects to vary as a function of brand attitude allowing us to make inferences about affect-based motivational aspects. In accordance with existing perceptions regarding lateralisation and affective-motivation, we predict that at baseline liked brands would generate greater activity over left frontal electrode sites, whereas disliked brands would elicit greater activity over right frontal areas. Secondly, we hypothesise that as a result of conditioning, activity for both liked and disliked brands will change sequentially across both hemispheres. We also hypothesise that as the number of conditioning trials increases, EEG will be sensitive in detecting these changes via the use of the LPP. Finally, we expected IAT data to support differences between disliked brands conditioned in a ‘liked’ direction from liked brands conditioned in a ‘disliked’ direction thus prove to be reliably reflective of brand attitude.

2. Methods

2.1 Participants

Initial recruitment for the study involved 22 participants, two of whom were excluded following pre-assessment of brand attitudes. The mean age of the remaining 20 participants (10 females) was 22.81 (SD = 2.37). All participants were tertiary education students recruited by word of mouth. All participants volunteered and gave written informed consent. Participants were right handed, had normal or corrected to normal vision, were free of central nervous system affecting medications or substances (including alcohol, caffeine and nicotine), and had no history of neuropathology. Participants were financially reimbursed for their time and travel. The study was approved by the Newcastle University Ethics Committee.

2.2 Stimuli

The initial stimulus list for pre-assessment comprised of 300 subjectively chosen common brands names, familiar to people from Australia (see Appendix A for a list of presented brands). Using an online survey, participants provided a subjective rating of like or dislike for each brand name on a 21-point analogue-type scale, ranging from -10 (Strong Dislike) to 10

(Strong Like). In addition, brands that had received a rating of zero by participants formed the list of neutral brands. Of the neutral brands, 80 were used as filler brands. Filler brands were also presented during each recording session however, they had not been associated with any valenced sounds.

2.2.1 Conditioning stimuli

In order to condition the target brand names, auditory stimuli from the International

Affective Digitized Sound system (IADS; Bradley & Lang, 1999) were used. The IADS consists of 111 sounds of an affective nature and was designed specifically to provide a better control of emotional stimuli relating to sounds. All 111 sounds in this database have been pre-evaluated regarding emotional valence (and arousal), thus allowing the sounds to be matched in terms of their affect (positive or negative). The 30 most unpleasant sounds and the 30 most pleasant sounds were selected and paired randomly with each of the brand names (evaluative conditioning).

2.3 Individual pre-assessment of brand attitudes.

Participants subjectively rated 300 brand names using an online survey (via www.limesurvey.com), prior to entering the lab. We required participants to read each brand name and indicate their attitude towards it using a mouse/track pad on the provided slider.

Participants were explicitly instructed not to adjust the slider if they were unfamiliar with a particular brand. Rating a brand as neutral required the participant to manually click “0”. This phase of the experiment occurred at a time of the participant’s choosing, with choice of computer also left to their discretion. The survey took on average 15-20 minutes to complete. Participants who demonstrated adequate familiarity and attitude scope were eligible for the experimental phase of the study. That is, participants who had insufficient brand name attitudes to construct a stimulus list with discernable positive and negative target items were excluded from the experiment. Two participants were unable to further participate due to such inadequate brand pre-assessment.

2.4 Lab Experiment

Following completion of pre-assessment, we invited eligible participants individually into the lab. During this first session, we collected baseline measurements of explicit and implicit attitudes towards brand names (see Bosshard et al., 2016). Explicit measurement involved subjective self-report, whilst objective measures were collected using electroencephalography

(EEG) and the IAT (see below). Upon entering the lab, participants were seated comfortably in front of a 32” LED television (resolution of 1024x768 pixels). We connected participants to a

BioSemiActiveTwo EEG system (BioSemi, Amsterdam, The Netherlands) and measured potential changes using 64 cranial electrodes as well as eight external reference electrodes placed lateralocularly, supraocularly, infraocularly and on the mastoids.

We used the computer program Presentation (NeuroBehavioral Systems, Albany, United

States) to visually present the appropriate instructions and individualised stimulus lists. The presentation of stimuli in addition to all psychophysiological signal recording was conducted from a separate room. Participants were given a brief overview of the study during set up of the equipment. We commenced testing with the participant by themselves in a dimly lit room to ensure adequate focus on the stimuli. A white fixation-cross appeared on a black background for

500ms, followed by a brand name for 5s. Participants provided a self-reported rating between 1

(Strong Dislike) and 9 (Strong Like) for each brand using a standard keyboard, whilst it was on screen. Compared to the initial online rating performance a different rating style was chosen for the actual experiment in order to avoid similarity effects. Brain potential changes and self-report were collected for the 60 target brands. To reduce fatigue effects participants were provided a break halfway through this stage. Overall, it took approximately 30 minutes to complete.

Participants were then asked to complete 5 rounds of the IAT (see Figure 1 for modified IAT).

Figure 1: Modified version of the original IAT (adapted from Greenwald et al.,

1998). Filled black circles on the left of the stimulus indicate left button presses and

vice versa. Task 3 = congruent, Task 5 = Incongruent condition. Following the completion of the IAT, participants were exposed to one, five and ten rounds of conditioning (total of 16 conditioning rounds; across three separate conditioning trials). One round of conditioning lasted approximately six minutes. Participants were allowed to take breaks as required. The duration between lab visits was standardised as best as possible and participants were required to attend subsequent sessions between 2 and five days from his/her last.

2.5 Data Recording and Processing

2.5.1 Explicit data

Mean self-reported ratings were compared by using paired-sampled t-tests. Within participant’s individualised brand lists, participant’s 30 most liked brands were conditioned negatively and their 30 most disliked brands were conditioned positively.

2.5.2 Implicit Association Test (IAT)

We used a modified version of the original IAT (Greenwald et al., 1998), which consisted of 5 separate discrimination tasks each with 30 visual presentations to be classified as either a target or non-target stimulus. Of the 60 brands rated previously by participants as liked or disliked, the 30 most liked were conditioned negatively and the 30 most disliked were conditioned positively. These brands became the target brands. In task 1 (initial target concept) study participants were asked to discriminate between visual stimuli either related to their individually rated most liked (or disliked) brand (target brand) or related to an individually rated neutral brand (non-target brand). Study participants were required to press the “A” key for target brand and the “L” key for non-target brand. In task 2 (associated attribute) participants were visually presented with valenced words and asked to press the “A” key for pleasant words (eg. beautiful, healthy, happy, perfect) and the “L” key for unpleasant words (eg. frighten, angry, sad, worthless). In task 3 (initial combined task) tasks 1 and 2 were combined. Study participants were asked to press the “A” key in case of target brand and pleasant words and the “L” key when presented with a negative word or a non-target brand. Task 4 (reversed target concept) was similar to task 1, however participants were asked to press the “A” key for non-target brands and the “L” key for target brands. Finally, task 5 (reversed combined task) was a combination of task

2 and task 4. Participants were required to press the “A” key in case of non-target brands and pleasant words and the “L” key when presented with a negative word or a non-target brand. A comparative analysis was made between reaction times of participants during task 3 and task 5.

During each of the blocks, stimuli were presented for 300ms; however, participants were given

1500ms to respond during each trial. Between each stimulus, a fixation cross was presented for

300ms and between the fixation cross and the following stimulus, was another 700ms gap. For a pictorial explanation of how the IAT was implemented (see Bosshard et al., 2016).

2.5.3 Event related potentials

We recorded EEG at a rate of 2048 samples/second using a 64-channel

BioSemiActiveTwo system and ActiView software (BioSemi, Amsterdam, The Netherlands).

Data sets were processed individually using EEG-Display (version 6.3.13; Fulham, Newcastle,

Australia). During processing we reduced the sampling rate to 256 samples/second and applied a band pass filter of 0.03Hz to 30Hz. Blink artefacts were corrected by referencing to the supraocular external electrode (excluding two sets referenced to Fpz due to unclean external signals). The data was coded to brand type (i.e., liked, disliked and filler). We established epochs from -100ms prior to stimulus onset (a baseline), to 1400ms following stimulus onset. The resultant epochs were baseline corrected and an average was generated across single trials for each condition. The individual data sets were then re-referenced to a common average. We produced averaged ERP figures to broadly assess effects of brand type over the entire epoch of interest. In order to eliminate noise generated by eye movements, we conducted horizontal, vertical and radial eye movement corrections (see. Croft & Barry, 1999). For following statistical analysis epochs were divided into 200ms blocks and the mean amplitudes were calculated by means of Analysis of Variance (ANOVA) with follow-up comparisons between conditions using t-tests during each time frame. Given that numerous, recent papers are seen to focus on frontal and parietal sites when investigating attitudes and behaviour (Brown, Randolph & Burkhalter, 2012; Davidson, 1992; Gable & Harmon-Jones,

2013; Peterson, Shackman & Harmon – Jones, 2007), we too chose to focus on similar sites.

Specifically, frontal electrodes sites AF3 and AF4 were chosen in addition to parietal sites P5 and P6. Following conditioning, each ERP was compared to that which was recorded the session prior.

3. Results

3.1 Self Report

We conducted a 2 (Brand type: liked, disliked) x 4 (Conditioning rounds: zero, one, five, ten) repeated measures ANOVA to determine whether there were any effects on brand rating as a result of conditioning. Results revealed that there was no significant brand type by session interaction F(3,291) = .299, p = .826).

Figure 2. Self report responses towards liked and disliked brands throughout all four sessions.

While liked brands were consistently rated as more positive compared to disliked brands across all sessions (* denotes p< .001) these ratings did not change between sessions.

3.2 IAT

In order to assess the responses provided during the IAT task, all incorrect responses as well as any responses falling outside of three standard deviations from the mean were removed.

T-tests were used to compare the congruent and incongruent stimuli during each session for both liked and disliked brands. The data showed that the reaction time during the congruent phase within both the liked and disliked condition was significantly reduced when compared to the incongruent phase.

Figure 3. IAT results (in milliseconds) reflecting responses towards liked brands in the congruent and incongruent phases. Whilst significant differences occurred between the congruent and incongruent phases, reaction times were not seen to vary across sessions. * denotes p< 001.

Figure 4. IAT results (in milliseconds) reflecting responses towards disliked brands in the congruent and incongruent phases. Whilst significant differences occurred between the congruent and incongruent phases, reaction times were not seen to vary across sessions. * denotes p< 001.

Further analysis of IAT responses revealed that there were significant differences in the overall reaction times between sessions. Reaction times recorded during the congruent phases were seen to decrease between session one and session two for both the liked (t = 8.307; df =

224; p< .001; two tailed) and disliked conditions (t = 12.777; df = 224; p < .001; two tailed).

Similarly, for the incongruent condition, reaction times were also seen to be significantly reduced for both the liked (t = 10.667; df = 224; p< .001; two tailed) and disliked conditions (t =

15.957; df = 224; p< .001; two tailed). In contrast, remaining sessions were seen to elicit a significant increase in reaction times, p < .001.

3.3 Event related potentials

3.3.1 Baseline

At baseline, left frontal site AF3 revealed that liked brands elicited significantly more negative activity than disliked brands for the entire duration of the epoch. This effect reached greatest significant at around 1300ms (t = -2.495; df = 19; p = .022; two tailed). No significant differences were witnessed across right frontal areas however, right parietal site P6 revealed that liked brands elicited a larger LPP than disliked brands between 1000ms and 1400ms. This effect saw its greatest effect at around 1400ms (t = 2.495; df = 19; p = .022; two tailed). For a more comprehensive analysis of the parietal baseline results, see Bosshard et al. (2016).

3.4 Conditioning

3.4.1 Liked Brands

Firstly, 2 (hemisphere: right, left) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVAs were conducted during each 200ms window beginning at 400ms. Paired comparisons using a Bonferroni correction were then conducted at frontal and parietal sites. At frontal and parietal sites, the ANOVA revealed no conditioning effects for liked brands; however t-tests revealed that conditioning effects were most prominent after two rounds of exposure (see

Figure 5).

Electrode Site; Session (M; SD) T-test

Time Window

AF3; 400-600ms Baseline (M = -1.57; SD = 3.08) ; Six conditioning rounds (M = -3.13; t = 2.227; df = 19; p = .039;

SD = 2.49) two tailed

AF3; 400-600ms Baseline (M = -1.57; SD = 3.08) ; Sixteen conditioning rounds (M = - t = 2.265; df = 19; p = .035;

3.12; SD = 2.10) two tailed

AF3; 600-800ms Baseline (M = -1.43; SD = 3.61) ; Six conditioning rounds (M = -3.71; t = 3.872; df = 19; p = .001;

SD = 2.83) two tailed

AF3; 600-800ms Baseline (M = -1.43; SD = 3.61) ; Sixteen conditioning rounds (M = - t = 2.999; df = 19; p = .007;

3.69; SD = 2.60) two tailed

AF4; 400-600ms Baseline (M = -1.18; SD = 3.53) ; Six conditioning rounds (M = -2.90; t = 2.407; df = 19; p = .026;

SD = 2.71) two tailed

AF4; 600-800ms Baseline (M = -1.15; SD = 3.68) ; Six conditioning rounds (M = -3.21; t = 2.337; df = 19; p = .031;

SD = 3.87) two tailed

Figure 5. Data obtained via t-tests. Baseline activity at frontal sites AF3 and AF4 was compared to that obtained after one, six, and sixteen rounds of conditioning.

In addition, a 2 (hemisphere: right, left) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVA revealed a brief lateralisation effect across frontal hemispheres. This effect arose between 1000ms (F(1,19) = 4.498, p = .047) and 1400ms (F(1,19) = 5.25, p = .034).

Moreover, paired comparisons revealed that during these windows, significantly greater activity was elicited across front left hemisphere electrode site AF3 (1000ms: M = -3.02; M = -1.81; p =

.047; 1200ms: M = -2.30; M = -1.74; p = .034).

Figure 6. ERPs generated during each conditioning round for liked brands across frontal electrode sites AF3 and AF4. Gradual changes between each session are evident across both electrode sites as brands were conditioned more negatively.

Similarly, across parietal sites t-tests revealed that conditioning effects were present, and again largest after a second round of exposure (see Figure 6).

Electrode Site; Time Session (M; SD) T-test

Window

P6; 400-600ms Baseline (M = 2.46; SD = 3.69) ; one t = -2.135; df = 19; p = .046; two tailed

conditioning round (M = 3.68; SD = 2.65)

P6; 400-600ms Baseline (M = 2.46; SD = 3.69) ; six t = -2.490; df = 19; p = .022; two tailed

conditioning rounds (M = 4.01; SD = 2.91) Electrode Site; Time Session (M; SD) T-test

Window

P6; 400-600ms Baseline (M = 2.46; SD = 3.69) ; sixteen t = -2.450; df = 19; p = .024; two tailed

conditioning rounds (M = 3.98; SD = 2.70)

P6; 600-800ms Baseline (M = 2.08; SD = 3.78) ; one t = -2.757; df = 19; p = .013; two tailed

conditioning round (M = 3.678; SD = 2.31)

P6; 600-800ms Baseline (M = 2.08; SD = 3.78) ; six t = -2.455; df = 19; p = .024; two tailed

conditioning rounds (M = 3.93; SD = 3.24)

Figure 7. Data obtained via t-tests. Baseline activity at parietal sites P5 and P6 was compared to that obtained after one, six, and sixteen rounds of conditioning.

Figure 8. ERPs generated during each conditioning round for liked brands across parietal electrode sites P5 and P6. A distinct increase in activity is evident between baseline recording and subsequent sessions at parietal site P6 only.

3.4.2 Disliked Brands

Firstly, 2 (hemisphere: right, left) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVAs were conducted during each 200ms window beginning at 400ms. Paired comparisons using a Bonferoni correction were then conducted at frontal and parietal sites. At frontal sites, after one round of conditioning activity was seen to increase significantly for approximately 400ms beginning at 400ms (F(3,57) = 5.298, p = .003) and remained until 800ms

(F(3,57) = 8.582, p < .001). Further t-tests revealed prolonged increases in activity between baseline and subsequent conditioning rounds (see table below; Figure 8).

Electrode Site; Time Session (Mean; Standard T-test

Window Deviation)

AF3; 400-600ms Baseline (M = -1.04; SD = 2.51); one t = 4.136; df = 19; p = .001; two tailed

conditioning round (M = -2.85; SD = 2.11)

AF3; 400-600ms Baseline (M = -1.04; SD = 2.51); six t = 4.446; df = 19; p < .001; two tailed

conditioning rounds (M = -3.13; SD = 2.15)

AF3; 400-600ms Baseline (M = -1.04; SD = 2.51); sixteen t = 2.624; df = 19; p = .017; two tailed

conditioning rounds (M = -3.00; SD = 2.85)

AF3; 600-800ms Baseline (M = -.79; SD = 2.83); one t = 5.204; df = 19; p < .001; two tailed

conditioning round (M = -3.31; SD = 2.42)

AF3; 600-800ms Baseline (M = -.79; SD = 2.83); six t = 4.610; df = 19; p < .001; two tailed

conditioning rounds (M = -3.21; SD = 2.75)

AF3; 600-800ms Baseline (M = -.79; SD = 2.83); sixteen t = 3.393; df = 19; p = .003; two tailed

conditioning rounds (M = -3.32; SD = 3.08)

AF3; 800-1000ms Baseline (M = -1.33; SD = 3.59); one t = 4.399; df = 19; p < .001; two tailed

conditioning round (M = -3.80; SD = 3.52)

AF3; 800-1000ms Baseline (M = -1.33; SD = 3.59); six t = 2.354; df = 19; p = .029; two tailed

conditioning rounds (M = -3.80; SD = 3.52) Electrode Site; Time Session (M; SD) T-test

Window

AF3; 800-1000ms Baseline (M = -1.33; SD = 3.59); sixteen t = 3.089; df = 19; p = .006; two tailed

conditioning rounds (M = -3.80; SD = 3.52)

AF3; 1000-1200ms Baseline (M = -80; SD = 3.77); one t = 3.210; df = 19; p = .005; two tailed

conditioning round (M = -3.46; SD = 4.17)

AF3; 1000-1200ms Baseline (M = -80; SD = 3.77); sixteen t = 3.142; df = 19; p = .005; two tailed

conditioning rounds (M = -3.13; SD = 3.75)

AF4; 400-600ms Baseline (M = -1.30; SD = 2.65); one t = 2.581; df = 19; p = .018; two tailed

conditioning round (M = -2.82; SD = 3.04)

AF4; 400-600ms Baseline (M = -1.30; SD = 2.65); six t = 4.274; df = 19; p < .001; two tailed

conditioning rounds (M = -3.18; SD = 2.67)

AF4; 400-600ms Baseline (M = -1.30; SD = 2.65); sixteen t = 3.267; df = 19; p = .004; two tailed

conditioning rounds (M = -3.68; SD = 2.87)

AF4; 600-800ms Baseline (M = -.79; SD = 2.83); one t = 5.204; df = 19; p < .001; two tailed

conditioning round (M = -3.31; SD = 2.42)

AF4; 600-800ms Baseline (M = -.79; SD = 2.83); six t = 4.610; df = 19; p < .001; two tailed

conditioning rounds (M = -3.2 1; SD = 2.75)

AF4; 600-800ms Baseline (M = -.79; SD = 2.83); sixteen t = 3.393; df = 19; p = .003; two tailed

conditioning rounds (M = -3.32; SD = 3.08)

AF4; 800-1000ms Baseline (M = -1.33; SD = 3.59); one t = 4.399; df = 19; p < .001; two tailed

conditioning round (M = -3.80; SD = 3.52)

AF4; 800-1000ms Baseline (M = -1.33; SD = 3.59); six t = 2.354; df = 19; p = .029; two tailed

conditioning rounds (M = -2.92; SD = 3.44)

AF4; 800-1000ms Baseline (M = -1.33; SD = 3.59); sixteen t = 3.089; df = 19; p = .006; two tailed

conditioning rounds (M = -3.54; SD = 3.59)

AF4; 1000-1200ms Baseline (M = -.80; SD = 3.77); one t = 3.210; df = 19; p = .005; two tailed

conditioning round (M = -3.46; SD = 4.17)

AF4; 1000-1200ms Baseline (M = -1.33; SD = 3.59); sixteen t = 3.142; df = 19; p = .005; two tailed

conditioning rounds (M = -3.13; SD = 3.75)

Figure 9. Data obtained via t-tests. Baseline activity at frontal sites AF3 and AF4 was compared to that obtained after one, six, and sixteen rounds of conditioning.

Figure 10. ERPs generated during each conditioning round for disliked brands across frontal electrode sites AF3 and AF4. Gradual changes between each session are evident across both frontal electrode sites as brands were conditioned more positively.

With regard to parietal sites, significant conditioning effects were again witnessed however, only across right hemisphere electrode site, P6. Electrode site P6 consistently revealed increases in activity after only one round of conditioning.

Electrode Site; Time Session (M; SD) T-test

Window

P6; 400-600ms Baseline (M = 2.14; SD = 3.68); one t = -2.135; df = 19; p = .001; two tailed

conditioning round (M = 3.88; SD = 2.34)

P6; 400-600ms Baseline (M = 2.14; SD = 3.68); six t = -2.490; df = 19; p = .002; two tailed

conditioning rounds (M = 3.88; SD = 2.34)

P6; 400-600ms Baseline (M = 2.14; SD = 3.68); sixteen t = -2.450; df = 19; p = .048; two tailed

conditioning rounds (M = 3.58; SD = 2.94)

P6; 600-800ms Baseline (M = 1.33; SD = 3.79); one t = -2.670; df = 19; p = .015; two tailed

conditioning round (M = 3.90; SD = 4.54)

P6; 600-800ms Baseline (M = 1.33; SD = 3.79); six t = -3.148; df = 19; p = .005; two tailed

conditioning rounds (M = 3.33; SD = 2.48)

P6; 800-1000ms Baseline (M = 1.84; SD = 4.04 one t = -2.345; df = 19; p = .030; two tailed

conditioning round (M = 4.34; SD = 4.15)

P6; 1000-1200ms Baseline (M = 1.67; SD = 3.97); one t = -2.763; df = 19; p = .012; two tailed

conditioning round (M = 4.38; SD = 4.24)

P6; 1200-1400ms Baseline (M = 1.96; SD = 3.81); one t = -3.175; df = 19; p = .005; two tailed

conditioning round (M = 4.61; SD = 4.02)

Figure 11. Data obtained via t-tests. Baseline activity at parietal sites P5 and P6 was compared to that obtained after one, six, and sixteen rounds of conditioning.

Figure 12. ERPs generated during each conditioning round for disliked brands across parietal electrode sites P5 and P6. Gradual changes between each session are evident across right parietal site P6 as brands were conditioned more positively.

3.4.3 Frontal Asymmetry Effects

A 2 (hemisphere: right, left) x 2 (brandtype: liked, disliked) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVA was conducted to investigate condition effects.

Results revealed a consistently significant hemisphere x brandtype interaction which saw liked brands elicit greater activation across left hemisphere electrode site AF3, whilst disliked brands elicited greater activation across right hemisphere electrode site AF4 (F(1,19) = 4.518, p = .047).

This effect was evident from 600ms and was evident for the remainder of the epoch.

3.4.4 Filler Brands

In a similar manner as the liked and disliked brands, 2 (hemisphere: right, left) x 4

(conditioning rounds: zero, one, five, ten) repeated measures ANOVAs were conducted for filler brands during each 200ms window, beginning at 400ms. Paired comparisons using a Bonferroni correction were then conducted at frontal (AF3, AF4) and parietal sites (P5, P6). No conditioning or asymmetry effects were evident across frontal sites. In addition, no conditioning effects were witnessed across parietal sites.

Figure 13. ERPs generated for all filler brands during each condition across frontal electrode sites AF3 and AF4. No changes between sessions were evident.

Figure 14. ERPs generated for all filler brands during each condition across parietal electrode sites P5 and P6. No changes between sessions were evident.

4. Discussion

We used a conditioning paradigm with the aim to modify participant’s attitudes towards their most liked and disliked brand names (established brand attitudes). Using a combination of traditional self-report measures as well as more innovative non-conscious measures, we found differences in their ability to detect conditioning effects. It was revealed that all measures were able to distinguish between liked and disliked brands, but that only EEG was consistently sensitive to conditioning effects and thus able to detect changes in attitude toward well established brands as a result of evaluative conditioning. To strengthen the finding of EEG data to be sensitive to evaluative conditioning we also report the finding that the filler brands used in the present study, which were also repeatedly presented, but without any conditioning between the sessions, did not show any of the effects that we found for the target brands. This is additional empirical evidence strongly supporting that the EEG-related evaluative conditioning effects are solid and robust.

4.1 Self Report and IAT

Analysis of self-report data (explicit rating performance) as well as IAT data revealed no evaluative conditioning effects. It is widely accepted that self-report measures provide only a limited insight into consumer attitudes (Ohme, 2008; Ohme, Reykowska, Wiener &

Choromanska, 2010; Percy, Hansen & Randrup, 2004). Of studies seen to investigate the effects of conditioning on attitudes towards well-known brands, many report null findings (Field

&Davey, 1999; Rozin, Wrzensiewski, & Byrnes, 1998; Kellaris & Cox, 1989). Our data mimic these findings and thus represent strong and robust support with respect to existing literature.

However, in combination with our data on implicit processes it is suggested once again, that traditional measures should not be used independently, but in conjunction with those that measure implicit aspects of attitudes. A number of authors have raised awareness of the need to assess automatic or non-conscious forms of attitudes (Gibson, 2008; Bargh, 2002; Chartrand,

2005; Dijksterhuis et al., 2004). In two experiments conducted by Gibson it was stated that failure to include measures of implicit attitude would have seriously underestimated the effects of evaluative conditioning. Specifically, it was reported that neglecting implicit measures would have led to the conclusion that mature brands are unaffected by evaluative conditioning. As already mentioned in the introduction section, it is suggested that the null findings pertaining to well established brands may arise as a result of cognitive pollution, an issue first raised by Walla and colleagues (Walla, Brenner & Koller, 2011). As participants were exposed to the conditioning paradigm, it may have been the case that this procedure exacerbated cognitive pollution, thus maintaining a lack of findings demonstrating evaluative conditioning effects. Although IAT and self-report measures appear to be capable of distinguishing between liked and disliked brand names at baseline and after subsequent conditioning, our results indicate that they are incapable of detecting changes in attitudes. In fact, our results revealed that reaction times increased as participants were exposed to further conditioning. In a way, these findings support the idea that cognitive pollution may have increased as a consequence of the conditioning paradigm. Although it was unexpected, these findings seem to reiterate the findings of existing literature. In several studies it has been suggested that the IAT may not exclusively measure implicit attitudes, but instead be influenced by cognition. In recent literature it has been reported that mere supposition has resulted in IAT effects. De Houwer (2006) and Gregg, Seibt and Banaji (2006) reported that participants presented with two fictitious groups and instructed to imagine that one group was positive (good, peaceful, etc.) and the second was negative (bad, violent etc.) resulted in participants responding more quickly to the compatible condition than the incompatible condition. In addition to these findings, both studies went further and reported that even after participants were exposed to conditioning trials, the effects of the IAT were not enhanced. As a result of such findings, it has been proposed that the IAT may not exclusively measure implicit attitudes (Blair, 2002; Mitchell, Nosek & Banaji, 2003) or instead, that it may reflect the ease of which participants are able to associate two categories rather than reflect an individual’s attitudes toward a category of stimuli (Olson & Fazio, 2001).

4.2 Event Related Potentials

Of all the measures utilised within the current paper, ERPs arguably provide the greatest insight into the processing of brands. To begin, our findings show that liked brands elicit increased activity across frontal left hemisphere electrode sites. Our findings are in line with current literature (Fox, 1991; Davidson, 1993; Davidson & Rickman, 1999) and suggest that the processing of pleasant and approach related stimuli evoke larger potentials across left frontal electrode sites, whilst unpleasant and avoidant stimuli result in an increase in right anterior activity. Our results support a fast growing body of literature that suggests that frontal asymmetry can provide an accurate indication of behaviour, and more specifically consumer behaviour. As mentioned previously Ravaja (2013) found that reduction in the price of certain products elicited relatively greater left frontal activation. In addition, the occurrence of greater left frontal activation was associated with an increased likelihood for participants to purchase goods. Although not specifically published with the intentions to increase understanding of consumer behaviour, the asymmetry model has also been utilised to assess individual’s preference of music and taste, both of which are readily assessed within consumer contexts.

Schmidt and Trainor (2001) recently presented findings that suggested that asymmetrical frontal

EEG activity distinguished valence of the musical excerpts which saw joyful and happy music elicit relative greater left anterior activity and fearful and sad music elicit relatively greater right anterior activity. In addition, Fox and Davidson (1986) reported that asymmetrical frontal brain activity discriminated sweet and sour tastes. Specifically, it was reported that greater relative left frontal EEG activity to the presentation of sweet solutions and greater relative right frontal EEG activity to the presentation of sour solutions. Given these behavioural examples, it should come as no surprise that our data relating to consumer behaviour is in line with the abovementioned literature.

In addition to asymmetry effects, the results relating to the LPP provide further insight into the implicit processing and perceptions of brands. According to extant research, LPP effects are more evident across parietal regions (Pastor et al., 2008). Moreover, emotionally laden stimuli are reported to elicit larger LPP effects across right parietal sites (Cacioppo et al., 1996).

In line with previous findings, LPP effects were witnessed for both liked and disliked brands at both right and left parietal sites with right hemisphere electrode sites eliciting the largest effects.

Interestingly however, liked brands were seen to elicit the largest LPP effects. This is in contrast to the majority of the literature that suggests LPP effects should be enhanced for both pleasant and unpleasant stimuli (Moran et al., 2013; Cacioppo et al., 1996; Cacioppo, Crites, Berntson &

Coles, 1993). It is possibly the case that this finding is due to the fact that disliked brands were processed in a similar manner to the filler brands (i.e., neutral brands that had not been utilised within the conditioning paradigm) and as a result were not considered, at an implicit level, to be as motivationally significant as liked brands. Generally, larger LPP effects are evoked by evaluatively inconsistent stimuli (e.g., an unpleasant stimulus presented within a block of pleasant stimuli) than by evaluatively consistent stimuli (e.g., a pleasant stimulus presented within a block of pleasant stimuli; Crites & Cacioppo, 1996; Cacioppo, Crites, Berntson &

Coles, 1993). Within the current study, participants were exposed to a single block containing all liked, disliked, and filler brands. As a result, the findings of a larger LPP for liked brands would suggest that they were processed as evaluatively inconsistent, thus providing evidence to suggest that disliked and filler brands were processed more similarly than liked and disliked brands even though they were matched for valence.

In addition to the LPP effects published within this paper, were conditioning effects.

Unfortunately, the majority of conditioning literature is yet to embrace implicit measures, let alone neurophysiological measures which further adds to the scarcity of comparable research papers. Of the research available that utilises implicit measures, numerous fail to report changes in brand attitude. For instance, Shimp et al. (1991), using the IAT, found that attitudes for novel stimuli could be changed through the use of a conditioning paradigm however, changing attitudes for mature brands (i.e., Coke and Pepsi) was unsuccessful. In addition, Gibson (2008) suggested that participants that had strong attitudes towards Coke and Pepsi did not show changes in attitude toward either of the two brands after having been exposed to conditioning.

These pieces of literature have added to the already general assumption that mature brand attitudes cannot be changed via the use of conditioning. In contrast to these studies, the present research highlights the need for more sensitive measures of implicit attitude. Across frontal sites, the current research shows that after six rounds of conditioning, liked brands were seen to elicit significantly more activity than when measured at baseline. At parietal sites, after one exposure of the brand/sound pairing, conditioning effects were evident. Although further conditioning maintained these effects, the largest increase in activity was seen after only a single exposure.

All conditioning effects for liked brands were evident between 400ms and 800ms.

In contrast, for disliked brands, conditioning effects were far more pronounced and longer lasting. Across frontal sites, conditioning effects were seen after only a single exposure.

In addition, at right and left anterior electrode sites, one round of conditioning was seen to elicit the largest increases in activation. Effects of conditioning disliked brands were seen to arise at approximately 400ms and remain throughout the entire 1400ms epoch. Furthermore, at parietal site P6, similar conditioning effects were recorded. After only one round of conditioning, the largest increases in activity were recorded. This being said, after six and sixteen exposures to the brand/sound pairing, significant conditioning effects were still evident.

Probably the most convincing piece of evidence to reinforce that the effects of conditioning witnessed within this paper were actually a result of conditioning, come from the filler brands. Although no significant changes in activity arose at either anterior electrode site, visual inspection would somewhat promote the notion that electrode site AF4 saw an effect. We understand this to be a result of "repetition effects" since no conditioning was undertaken, however the brands were repeatedly shown. It is possible that this repetition effect was also seen throughout the data for liked and disliked brands, thus enhancing the conditioning effects that were recorded. However, in contrast, at parietal site P6, no such gradual effects were seen for filler brands. With this in mind, it is assumed that the conditioning effects recorded at P6 for disliked brands are true conditioning effects.

4.3 Conclusions and Implications

In the present study, self-report, IAT and ERP measures were used to investigate participant’s attitudes towards their most liked and most disliked brands. From the present study, four main conclusions can be drawn. Firstly, the findings suggest that self-report and IAT measures lack the sensitivity required in order to determine whether evaluative conditioning is of any use. Instead, the most implicit of all the measures (ERPs) seemed to be the most sensitive.

Secondly, in line with existing literature, left frontal electrode sites elicited greatest activity when processing liked brands. Thirdly, liked brands evoked greatest LPP effects across right parietal site. Finally, conditioning effects revealed by ERPs were only seen for disliked brands and were greatest after only a single exposure.

The current research adds to the vast amount of research that promotes the inclusion of implicit measures to assess brand attitude. Had implicit measures not been included in the present study, the effects of conditioning would have been completely absent. Through the inclusion of such measures, we have established that traditional approaches lack the required sensitivity to competently and accurately assess consumer attitudes.

In an applied sense, our results reveal exciting findings that suggest advertisers should not run the same ad for too long. The ERPs presented within the current paper indicated that although a significant degree of evaluative conditioning occurred after all three CS:US exposures, conditioning effects were most prominent after only a single pairing. This finding supports those presented by several authors, (Stuart, Shimp & Engle, 1987; Domjan & Burkhard,

1985; Smith, Feinberg & Burns, 1998), who report that a single conditioning trial will elicit a large effect, whereas subsequent conditioning trials will elicit smaller effects until a maximum is reached. Given that the majority of the abovementioned authors relied on fictitious brands, more research in this area needs to be considered before any more robust conclusions can be drawn.

Not only does extant literature suggest that conditioning effects diminish, but it has also been reported that over exposure to a single ad can in fact result in negative conditioning

(Greyser, 1973). Furthermore, Sweldens, van Osselaer and Janiszewski (2010) report that different conditioning procedures might encourage different learning processes. With this in mind, businesses may gain benefit from utilising different mediums when creating ad campaigns. Although this paper only focused on sounds as the conditioning medium, visual stimuli (Pham,

Geuens & Pelsmacker, 2013; Brown, Homer & Inman, 1998), olfactory stimuli (Todrank,

Byrnes, Wrzesniewski & Rozin, 1995), and taste (Wadhera & Capaldi-Phillips, 2014) have all been used to condition positive affect. In principle, these current results emphasise the need of marketers to move away from traditional self-report measures and begin to address the issue explicit types of measures inherently possess.

In addition to the abovementioned findings, our results promote the idea that liked brands may be more resistant to the effects of evaluative conditioning. Although not directly linked to our study, the use of shock advertising may provide some support for this theory. Unlike typical advertising techniques, shock advertising aims at deliberately startling and offending its audience

(Gustafson &Yssel, 1994; Venkat & Abi-Hanna, 1995). Although one would assume such advertising techniques would deter consumers from engaging with products, research has suggested that shock advertising can have positive effects on attention, memory, and behaviour

(Dahl, Frankenberger & Manchanda, 2003). Given our ERP findings that suggest the robust nature of well-established liked brands, it is suggested that businesses possessing such brands may be able to employ more controversial forms of marketing techniques without risking damage to their brands.

In sum, further research within this area should focus on existing brands that are well- known to participants. This method will allow for the most comprehensive understanding of how brand attitudes are formed and modified. It may also be beneficial to note that businesses should not rely on a single ad campaign for extended periods, but instead change campaign and consider focusing on different mediums by which to present their brands.

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Paper Three

Can we condition well-established, familiar,

neutral brands? A new, implicit approach to

understanding neutral brand attitudes.

Reference for publication:

Bosshard, S. S., Kunaharan, S., Koller, M., & Walla, P. (2016). Can we condition familiar,

neutral brands. Can we condition well-established, familiar, neutral brands? A new,

implicit approach to understanding neutral brand attitudes. Manuscript submitted for

publication

Co-author statements

By signing below I confirm that Shannon Bosshard contributed the majority of written content in the paper/publication entitled: Bosshard, S. S., Kunaharan, S., Koller, M., & Walla, P. (2016).

Can we condition well-established, familiar, neutral brands? A new, implicit approach to understanding neutral brand attitudes. Manuscript submitted for publication.

06/06/2016

Sajeev Kunaharan Date

21/07/2016

Monika Koller Date

22/07/2016

Peter Walla Date

Faculty Assistant Dean Research Training Date

Abstract

In the present study, we assessed whether attitudes towards familiar neutral brands could be modified within a conditioning paradigm. Attitudes were assessed using both implicit and explicit measures. Initial brand preferences on an individual basis were determined through the use of an online survey and formed the basis of this experiment. Each participant was then repeatedly exposed to conditioning during three subsequent sessions within the laboratory.

Throughout the experiment, participants were asked to rate the brand names on a Likert type scale. Simultaneously, changes in brain electric activity in response to the brands were recorded via electroencephalography (EEG). Upon completion of this task, participants underwent two

Implicit Association Tests (IAT; one for neutral brands that were conditioned negatively and another for neutral brands that were conditioned positively). There were several findings that came as a result of this study. Firstly, no significant changes in attitude were observed via the use of self-report or the IAT. Moreover, our results suggest that ERPs were the most sensitive to changes in brand attitude. Specifically, no hemispheric dominance was recorded across frontal sites. This lack of a hemispheric dominance for either neutral condition reinforces the idea that brands viewed by participants were indeed neutral. In addition, and most interesting were the

ERP findings that showed after only a single round of conditioning, changes in attitude at implicit levels was evident. From these findings, we can speculate that the process of assessing brand attitudes would be more accurate if ERPs were utilised. Our findings also suggest that changing an individual’s brand perception may be more likely to occur when exposure to a single brand/CS pairing are limited, and instead, a variety of brand/CS pairings are utilised.

1. Introduction

Attitudes allow us to distinguish between stimuli that we find appealing and those we would rather avoid. According to Gawronski and Galen (2007) attitudes play a significant role in determining what we pay attention to and how we interpret stimuli. In today’s advertising market where competition is fierce, brand differentiation is imperative. As marketing agencies aim to modify peoples’ perceptions of their products, only a small selection of brands actually go on to become well-known, well-liked, and regularly purchased. In contrast to liked and disliked brands that possess strong affect, neutral brands are those that are equivalently familiar; however they are not associated with either positive or negative feelings, and are reported to elicit lower levels of arousal (Aron, Aron & Norman, 2001).

Although advertising seems relatively straightforward, the underlying principles that result in changes in brand attitude are far less understood. Advertising works on the assumption that repeated pairings of a brand and a positive stimulus, be it a celebrity endorser, a pleasant image, or a pleasant sound will eventually result in a transfer of positivity to the brand. The process of repeatedly pairing a neutral stimulus (in this case, a brand; unconditioned stimulus;

US) with an emotionally charged stimulus (be it pleasant or unpleasant; otherwise referred to as a conditioned stimulus; CS) is referred to within the field of psychology as conditioning.

Although businesses spend billions of dollars annually on advertising (“Advertisers will spend nearly $600 Billion worldwide in 2015”, 2014), the majority of psychology research suggests that well-established brand attitudes are often resistant to advertising (Stammerjohan et al.,

2005). Moreover, it has been suggested that changing attitudes towards well-known brands is extremely difficult (Shimp et al., 1991; Cacioppo et al., 1992; Kellaris & Cox, 1989).

The inability to modify people’s attitudes towards brands within laboratory settings is generally attributed to people being more influenced by previous opinions than by new information (Weilbacher, 2003). Within psychological literature, many of the null findings pertaining to the evaluative conditioning of familiar stimuli (neutral or not) are put down to the effects of latent inhibition (LI). In more simplistic terms, latent inhibition occurs when previous exposure to a stimulus (a brand in this case) results in an inability to learn new things about the same stimulus. These findings have been reported repeatedly within the past few decades (Allen

& Madden, 1985; Gresham & Shimp, 1985; Kleine, Macklin & Bruvold, 1986; Macklin, 1985).

This being said, in contrast, outside of the marketing domain, numerous papers have shown that the changing of attitudes towards familiar neutral stimuli is possible. In such cases, attitudes towards neutral olfactory stimuli (van den Bosch et al., 2015), pictorial stimuli (Levey & Martin,

1975), faces (Glaser & Walther, 2012), familiar names (Staats & Staats, 1958), and words (Staats et al., 1959) have been successfully conditioned in both a negative and positive manner. Given these discrepant findings, it is quite possible that well established, neutral brands can be conditioned within a laboratory setting.

Although previous literature suggests that LI is responsible for the inability to change an individual’s attitude towards a well-established stimulus, it is more likely the case that these null findings arise as a result of attitudes being measured inadequately. This inadequacy in measuring attitudes arises as a result of people either not being able to, or not wanting to fully explain their preferences (Babiloni, 2012; Greenwald & Banaji, 1995). According to Fazio (2001), attitudes are comprised of an automatic component. For instance, Fazio presented participants with an attitude object (a stimulus evaluated by participants as either positive or negative) followed by an adjective that was also negative or positive. It was reported that when participants were presented with an attitude object and an adjective that were similar in valence, responses were faster. Since these findings, the notion that there is a non-conscious or automatic component to attitudes has been repeatedly supported within recent literature (Cunningham, Johnson, Gatenby, et al., 2003; Cunningham, Johnson, Raye, et al., in press; Greenwald & Banaji, 1995;

Cunningham, Raye & Johnson, 2004). All in all, there is overwhelming support within the literature that attitudes are multidimensional and as a result, their assessment requires a multidimensional approach (Aaker, 1997). As findings such as these become more prominent, the need to find an alternative, more accurate measure of attitude is imperative. At this point, it is important that a distinction be made between explicit attitudes that are assessed via traditional self-report measures, and implicit attitudes that are assessed using tools seen to focus on non- conscious processes related to attitudes.

Explicit attitudes and are said to be contemplative and formulated through reasoning

(Gawronski & Bodenhausen, 2006). This type of attitude is assessed using traditional, self- report measures (e.g. surveys & focus groups). As consumers undergo the process of reasoning at a conscious level, higher order structures are called upon and this inevitably produces a negative effect referred to as cognitive pollution (Walla, Brenner & Koller, 2007). Cognitive pollution clouds the judgement of the consumer and when questioned about their attitude towards a product or brand, the consumer, although able to provide a response, has provided a contaminated insight of their attitude.

In contrast, implicit attitudes are associations that are automatically activated in the presence of relevant stimuli without any conscious awareness of evaluation (Cunningham, Raye,

& Johnson, 2004). Implicit attitudes have repeatedly been shown to contradict explicitly stated responses. That is, negative associations can be activated even if the individual subjectively perceives their outlook towards it to be positive, and vice versa (Devine, 1989). Furthermore, implicit attitudes are shown to be considerably robust (Petty, Tormala, Briñol, & Jarvis, 2006) and better predictors of spontaneous behaviour (Gawronski & Bodenhausen, 2012). As a result, the authors of the current paper propose that gauging a thorough understanding of consumer’s attitudes can only come from simultaneously using a combination of implicit and explicit attitudes.

The inclusion of both implicit and explicit measures within attitude research is imperative. Of the few studies that are seen to investigate attitudes using a multidimensional approach, many report discrepancies in the sensitivity of implicit and explicit tools used (Gibson, 2008; Grahl et al., 2012; Geiser and Walla, 2011; Walla et al., 2013). For instance, Walla et al.

(2010) found that when eating ice cream, chocolate or yoghurt, although participants had no stated preference for a particular food item, implicit responses showed that ice cream was preferred. Furthermore, these findings are similar when attitudes towards conditioned stimuli are assessed. Gibson (2008) presented participants who were familiar with Coke or Pepsi, images of

Coke and Pepsi paired with either positive or negative words. After several trials, attitudes towards Coke and Pepsi were recorded using explicit as well as implicit measures and the results suggested that although explicit attitudes were unaffected by conditioning, implicit attitudes were seen to change in the direction of which they were conditioned. Moreover, Gibson has gone so far as saying that say that without the inclusion of implicit measures, the effects of evaluative conditioning would have been seriously underestimated.

Of the implicit tools used to investigate attitudes, the Implicit Association Test (IAT; see

Greenwald, McGhee, & Schwartz, 1998) is the most commonly cited. The IAT was initially utilised within psychological research to measure non-conscious attitudes in relation to social prejudices including racism and stereotypes (Greenwald, McGhee & Schwartz, 1998; Banaji &

Greenwald, 1995; Banaji & Hardin, 1996; Greenwald & Banaji, 1995). The IAT is arguably the most popular and effective response-latency-based implicit measure, even within consumer contexts. It has however been met with a number of criticisms regarding legitimacy as a reliable and valid index of implicit attitudes (De Houwer, 2006; De Houwer, Beckers, & Moors, 2007;

Fiedler, Messner, & Bluemke, 2006; Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005).

Many of these criticisms arose after suggestions the IAT assessed salient asymmetries rather than implicit associations between two stimuli (Rothermund & Wentura, 2004). In other words, it has been proposed that the IAT can be affected by cognitive processes (Blair, 2002). For example,

De Houwer (2005) presented English speaking participants with two groups of Dutch words

(pleasant and unpleasant words). Participants were then informed that at a later stage in the experiment one group of words would be paired with pleasant images whilst the other words would be paired with unpleasant images. Prior to their actual pairing and presentation, participants were required to undergo an IAT. The results of the study revealed that simply alerting participants to the upcoming pairings during the later phases of the experiment was enough to elicit significant differences in responses during the IAT. As a result of such findings, it is imperative that alternative implicit measures be sourced and utilised within attitude research.

An emerging alternative to the IAT is electroencephalography (EEG). This measure of non-conscious processing has been shown as a useful technique for obtaining implicit information. Very few papers have been seen to utlise EEG within attitude research and even fewer have been within consumer contexts (for review see: Wang & Michael, 2008). Of the papers that utilise EEG as a measure of attitude, results suggest that it is capable of differentiating between positive (approach) and negative (avoidance) affect (Davidson et al.,

1979). Davidson et al. proposed an asymmetry model which has numerously found greater relative left frontal EEG activity to be associated with the processing of positive affects, whereas greater relative right frontal EEG activity with the processing of negative effects. Moreover,

Harmon-Jones (2004) reported that in addition to the asymmetry model providing insight into the emotive components of a stimulus, it may also provide insight into the motivational components of a stimulus. Harmon-Jones suggested that relative increases in left and right activity are associated with approach and avoidance systems respectively. The asymmetry model has been informative within a number of domains including that of consumer behaviour. Ohme et al.

(2010) reported whilst viewing television commercials, participants displayed increases in approach related behaviour (seen as increases in left frontal activity) during which product- benefit, product, and brand scenes were presented. In addition, Handy et al. (2008) found that when participants rated logos as positive, these stimuli elicited more activity as late as 600ms across left frontal sites and parietal sites than those that were rated more negatively. Most recently, Ravaja et al. (2013) provided support for the notion that greater left frontal activation may be indicative of future purchase behaviour. More specifically, Ravaja et al. reported that when brand and price are altered, greater left frontal activity indicating greater intent to purchase.

Whilst the asymmetry model has been cited repeatedly, there are other means by which

EEG can be used to assess attitude. The late positive potential (LPP) is one of the most empirically valid EEG approach as an index of motivation and affect and has been extensively used within the literature to assess (Lang et al. 1997; Crites & Cacioppo, 2996). As a result, the

LPP has received psychometric endorsement which revealed that it had good to excellent reliability as a measure of emotion/affective processing (see Moran et al., 2013). Generally, it is reported that stimuli that are seen to be either more emotionally affective or more motivationally significant are said to evoke the largest LPPs (Moran et al., 2013). These pieces of literature also suggest that greater LPP activity is usually generated over right hemisphere electrode sites during evaluative tasks (Crites & Cacioppo, 1996).

Current study

The purpose of the current study was to firstly, measure attitudes towards well- established, neutral brands using both implicit and explicit measures. Secondly, we wanted to investigate whether implicit and explicit measures of attitudes were sensitive to the effects of conditioning on neutral stimuli. The present study extends on the study by Walla et al. (2011) in that it utilises EEG and IAT as implicit measures of affect. Walla et al. investigated brand attitudes, but focused on startle reflex, heart rate and skin conductance. This study, to our knowledge, is one of only a few that not only assessed well-established, neutral brands, but it also gauged changes in attitudes using both implicit and explicit attitudes via a conditioning paradigm.

We used an online survey to produce individual lists of neutral brands and then invited eligible participants back to record brain potentials and take IAT measures. We first hypothesised that self-reported measures during the first session of physiological recording would strongly reflect explicit pre-assessment ratings. In accordance with much of the existing

LI literature, it was hypothesised that explicit brand attitudes would not change as a result of conditioning. Furthermore, we expected the LPP effects to vary as a function of brand attitude allowing us to make inferences about affect-based motivational aspects. In accordance with affective-motivational conceptualisations, we predicted that neutral brands conditioned to become more positive would produce greater LPP effects over frontal areas than neutral brands conditioned in a negative direction. Thirdly, we expected IAT data to support differences between neutral brands conditioned in a ‘liked’ direction from those conditioned in a ‘disliked’ direction thus prove to be reliably reflective of brand attitude.

2. Methods

2.1 Participants

Initial recruitment for the study involved 22 participants, two of whom were excluded following pre-assessment of brand attitudes. The mean age of the remaining 20 participants (10 females) was 22.81 (SD = 2.37). All participants were tertiary education students recruited by word of mouth. All participants volunteered and gave written informed consent. Participants were right handed, had normal or corrected to normal vision, were free of central nervous system affecting medications or substances (including alcohol, caffeine and nicotine), and had no history of neuropathology. Participants were financially reimbursed for their time and travel. The study was approved by the Newcastle University Ethics Committee.

2.2 Stimuli

The initial stimulus list for pre-assessment comprised 300 subjectively chosen common brands names, familiar to people from Australia (see Appendix A for a list of presented brands). Using an online survey, participants provided a subjective rating of like or dislike for each brand name on a 21-point analogue-type scale, ranging from -10 (Strong Dislike) to 10 (Strong Like).

Upon initiation of the experiment, we created individualised stimulus lists using the subjective ratings obtained from the online survey. Each stimulus list comprised 200 brand names, including the participant’s 30 most liked brand names, 30 most disliked brand names, 60 neutral brand names, and 80 non-target (filler; brands that had been rated as neutral, had been presented during each session, but had not been paired with valenced sounds within a conditioning paradigm) brand names. This accumulated 120 target brand names across three types; positive, negative and neutral. Brand names were presented in capital white letters, Tahoma font and on a black background (no logos were presented). In the frame of this paper only measures relating to the neutral condition were further analysed.

2.2.1 Conditioning stimuli

In order to condition the target brand names, auditory stimuli from the International

Affective Digitized Sound system (IADS; Bradley & Lang, 1999) were used. The IADS consists of 111 sounds of an affective nature and was designed specifically to provide a better control of emotional stimuli relating to sounds. All 111 sounds in this database have been pre-evaluated regarding emotional valence (and arousal), thus allowing the sounds to be matched in terms of their affect (positive or negative). The 30 most unpleasant sounds and the 30 most pleasant sounds were selected and paired randomly with each of the brand names (evaluative conditioning).

2.3 Procedure

2.3.1 Individual pre-assessment of brand attitudes. Participants subjectively rated 300 brand names using an online survey (via www.limesurvey.com), prior to entering the lab. We required participants to read each brand name and indicate their attitude towards it using a mouse/trackpad on the provided slider.

Participants were explicitly instructed to not adjust the slider if they were unfamiliar with a particular brand. Rating a brand as neutral required the participant to manually click “0”. This phase of the experiment occurred at a time of the participant’s choosing, with choice of computer also left to their discretion. The survey took on average 15-20 minutes to complete. Participants who demonstrated adequate familiarity and attitude scope were eligible for the experimental phase of the study. That is, participants who had insufficient brand name attitudes to construct a stimulus list with discernable positive, negative, and neutral target items were excluded from the experiment. Two participants were unable to further participate due to such inadequate brand pre-assessment.

2.3.2 Lab Experiment

Following completion of pre-assessment, we invited eligible participants individually into the lab. During this first session, we collected baseline measurements of explicit and implicit attitudes towards brand names. Explicit measurement involved subjective self-report, whilst objective measures were collected using electroencephalography (EEG) and the IAT. Upon entering the lab, participants were seated comfortably in front of a 32” LED television

(resolution of 1024x768 pixels). We connected participants to a BioSemiActiveTwo EEG system

(BioSemi, Amsterdam, The Netherlands) and measured potential changes using 64 cranial electrodes as well as eight external reference electrodes placed lateralocularly, supraocularly, infraocularly and on the mastoids.

We used the computer program Presentation (NeuroBehavioral Systems, Albany, United

States) to visually present the appropriate instructions and individualised stimulus lists. The presentation of stimuli in addition to all psychophysiological signal recording was conducted from a separate room. Participants were given a brief overview of the study during set up of the equipment. We commenced testing with the participant by themselves in a dimly lit room to ensure adequate focus on the stimuli. A white fixation-cross appeared on a black background for

500ms, followed by a brand name for 5s. Participants provided a self-reported rating between 1

(Strong Dislike) and 9 (Strong Like) for the brand using a standard keyboard, whilst it was on screen. Brain potential changes and self-report were collected for the 120 target brands. To reduce fatigue effects participants were provided a break halfway through this stage. Overall, it took approximately 30 minutes to complete. Participants were then asked to complete 5 rounds of the IAT (see Figure 1 for modified IAT).

Following the completion of the IAT, participants were exposed to one, five and ten

(during sessions 1, 2 and 3 respectively) rounds of conditioning (total of 16 rounds). One round of conditioning lasted approximately six minutes. Participants were allowed to take breaks as required. The duration between lab visits was standardised as best as possible and participants were required to attend subsequent sessions between 2 and five days from his/her last.

2.4 Data Recording and Processing

2.4.1 Explicit data

Mean self-reported ratings were compared using paired-sampled t-tests. Within participant’s individualised brand lists, of the 60 neutral brands, 30 were conditioned positively and 30 were conditioned negatively. Self-report ratings for condition A and B that had been collected prior to conditioning were combined and used as a baseline measure (referred to as

‘Combined Session 1’).

2.4.2 Implicit Association Test (IAT) We used a modified version of the original IAT (Greenwald et al., 1998), which consisted of 5 separate discrimination tasks each with 30 visual presentations to be classified as either a target or non-target stimulus. Of the 60 brands rated previously by participants as neutral, 30 were conditioned negatively (Neutral Condition A) and 30 were conditioned positively (Neutral

Condition B). These brands became the target brands. For the first session, responses toward the neutral brands were combined to form a single condition prior to any conditioning (referred to as

‘Combined Session 1’). In task 1 (initial target concept) study participants were asked to discriminate between visual stimuli either related to their individually rated most liked brand

(target brand) or related to their individually rated most disliked brand (non-target brand). Study participants were required to press the “A” key for target brand and the “L” key for non-target brand. In task 2 (associated attribute) participants were visually presented with valenced words and asked to press the “A” key for pleasant words (eg. beautiful, healthy, happy, perfect) and the

“L” key for unpleasant words (eg. frighten, angry, sad, worthless). In task 3 (initial combined task) tasks 1 and 2 were combined. Study participants were asked to press the “A” key in case of target brand and pleasant words and the “L” key when presented with a negative word or a non- target brand. Task 4 (reversed target concept) was similar to task 1, however participants were asked to press the “A” key for non-target brands and the “L” key for target brands. Finally, task 5

(reversed combined task) was a combination of task 2 and task 4. Participants were required to press the “A” key in case of non-target brands and pleasant words and the “L” key when presented with a negative word or a non-target brand. A comparative analysis was made between reaction times of participants during task 3 and task 5. For a pictorial explanation of how the IAT was implemented, see Figure 1.

Figure 1. Modified version of the original IAT. Filled black cirles on the left of the stimulus indicate left button presses and visa versa. Task 3 = congruent, Task 5 = Incongruent condition.

2.4.3 Event related potentials

We recorded EEG at a rate of 2048 samples/second using a 64-channel

BioSemiActiveTwo system and ActiView software (BioSemi, Amsterdam, The Netherlands).

Data sets were processed individually using EEG-Display (version 6.3.13; Fulham, Newcastle,

Australia). During processing we reduced the sampling rate to 256 samples/second and applied a band pass filter of 0.1Hz to 30Hz. Blink artefacts were corrected by referencing to the supraocular external electrode (excluding two sets referenced to Fpz due to unclean external signals). In order to eliminate noise generated by eye movements, we conducted horizontal, vertical and radial eye movement corrections (see. Croft & Barry, 1999). The data was coded to brand type (i.e., liked, disliked). We established epochs from -100ms prior to stimulus onset (a baseline), to 1400ms following stimulus onset. The resultant epochs were baseline corrected and an average was generated across single trials for each condition. The individual data sets were then re-referenced to a mastoid reference electrode. Grand averaged ERPs were generated to display brain activity differences. The first EEG recording was averaged across conditions (A and B), and this was later used as a baseline measure to determine subsequent conditioning effects. Electrode sites were then selected based on visual inspection. Epochs were divided into

200ms blocks and the mean amplitudes were compared using t-tests during each time frame. This method was repeated for filler brands to ensure the effects seen were a direct result of conditioning.

3. Results

3.1 Self Report

We conducted a one-way ANOVA to compare the responses across all four sessions for both neutral conditions. An average across both conditions for session 1 was taken as no conditioning had been undertaken. No significant differences in explicit rating were seen for condition A (F(3,388) = .983, p = .401), or condition B (F(3,388) = .278, p = .841) as a result of conditioning.

10 9 8 7 6 5 4 3 2 1 0 A B A B A B Combined Session 2 Session 3 Session 4 Session 1

Figure 2. Mean self report ratings of both neutral conditions (A and B) across all four sessions.

3. 2 IAT

During analysis of the IAT responses, we began by removing all responses that fell three standard deviations from the overall mean of each phase. We also removed all incorrect responses and then analysed the data pertaining to participants’ most liked brands. A 4

(conditioning rounds: zero, one, five, ten) x 2 (phase: congruent, incongruent) repeated measures ANOVA was conducted for both neutral conditions. Results revealed that throughout all four sessions, the congruent conditions resulted in significantly faster reaction times than the incongruent condition for both neutral condition A (F(1,230) = 584.333, p < .001) and B

(F(1,230) = 265.731, p < .001). As a result of conditioning, significant changes in reaction time were witnessed for both neutral condition A (F(3,690) = 9.840, p < .001) and B (F(3,690) =

7.191, p < .001).

Pairwise comparisons revealed a significant reduction in reaction time after only one round of conditioning for condition A (p < .001; M = 6440.58, M = 6109.81). For condition B, although a reduction in reaction time was evident between session 1 and session 2, this was not significant (p= .069). Furthermore, for both condition A and B, subsequent conditioning rounds saw an inverse effect whereby reaction times were seen to significantly increase rather than decrease.

Figure 3. IAT results (in milliseconds) reflecting responses towards brands that were conditioned negatively (neutral condition A) in the congruent and non-congruent conditions. * denotes a p value less than .001. ** denotes p value less than .01

Figure 4. IAT results (in milliseconds) reflecting responses towards brands that were conditioned positively (neutral condition B) in the congruent and non-congruent conditions. * denotes a p value less than .001

3.3 Event Related Potentials

3.3.1 Condition A

For both frontal and parietal electrode pairs, a 2 (hemisphere; right, left) x 4 (conditioning round; one, two, three, four) repeated measures ANOVA was conducted for each 200ms time period beginning at 400ms. Paired comparisons using a Bonferroni correction were then conducted at frontal and parietal sites.

Results revealed that significant conditioning effects were evident throughout the entire epoch. These effects began at approximately 400ms (F(3,57) = 3.958, p = .012) and remained until approximately 1200ms (F(3,57) = 5.017, p = .004). Paired t-tests were then conducted to further investigate conditioning effects.

Electrode Site; Time Session (Mean; Standard T-test

Window Deviation)

AF3; 400-600ms Baseline (M = -1.44; SD = 2.81); one t = 2.527; df = 19; p = .021; two tailed

conditioning round (M = -2.86; SD = 3.21)

AF3; 400-600ms Baseline (M = -1.44; SD = 2.81); six t = 2.138; df = 19; p = .046; two tailed

conditioning rounds (M = -2.68; SD = 2.81)

AF3; 400-600ms Baseline (M = -1.44; SD = 2.81); sixteen t = 2.543; df = 19; p = .020; two tailed

conditioning rounds (M = -4.02; SD = 3.77)

AF3; 600-800ms Baseline (M = -1.23; SD = 3.03); one t = 2.182; df = 19; p = .042; two tailed

conditioning round (M = -3.35; SD = 3.62)

AF3; 600-800ms Baseline (M = -1.23; SD = 3.03); sixteen t = 2.767; df = 19; p = .012; two tailed

conditioning rounds (M = -4.86; SD = 5.47)

AF3; 800-1000ms Baseline (M = -1.46; SD = 3.84); one t = 2.813; df = 19; p = .011; two tailed

conditioning round (M = -4.74; SD = 4.67)

AF3; 1000-1200ms Baseline (M = -1.31; SD = 4.12); sixteen t = 2.187; df = 19; p = .041; two tailed

conditioning rounds (M = -3.87; SD = 4.24)

AF4; 400-600ms Baseline (M = -1.57; SD = 2.82); one t = 2.607; df = 19; p = .017; two tailed

conditioning round (M = -3.16; SD = 2.58)

AF4; 400-600ms Baseline (M = -1.57; SD = 2.82); six t = 2.719; df = 19; p =.014; two tailed

conditioning rounds (M = -2.74; SD = 3.12)

AF4; 600-800ms Baseline (M = -1.56; SD = 3.04); one t = 2.594; df = 19; p = .018; two tailed

conditioning round (M = -3.21; SD = 2.57)

AF4; 600-800ms Baseline (M = -1.56; SD = 3.04); sixteen t = 2.337; df = 19; p = .031; two tailed

conditioning rounds (M = -4.01; SD = 4.45)

AF4; 800-1000ms Baseline (M = -1.56; SD = 3.41); one t = 2.276; df = 19; p = .035; two tailed

conditioning round (M = -3.18; SD = 2.85)

AF4; 800-1000ms Baseline (M = -1.56; SD = 3.41); sixteen t = 2.163; df = 19; p = .044; two tailed

conditioning rounds (M = -3.81; SD = 3.95)

Figure 5. Data obtained via t-tests. Baseline activity at frontal sites AF3 and AF4 was compared to that obtained after one, six, and sixteen rounds of conditioning.

Figure 6. ERP curves generated at frontal electrode sites AF3 and AF4 at baseline and again after one, six and sixteen conditioning rounds for neutral brands that were conditioned positively.

Gradual changes can be seen as a result of subsequent conditioning.

In addition, at parietal sites, a 2 (hemisphere; right, left) x 4 (conditioning round; one, two, three, four) repeated measures ANOVA revealed no conditioning effects. Furthermore, paired t-tests also revealed no significant conditioning effects.

3.2.3 Condition B

Again, a 2 (hemisphere; right, left) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVA, using a Bonferroni correction, was conducted for frontal and parietal electrode pairs.

Significant conditioning effects were witnessed throughout the entire epoch beginning at

400ms (F(3,57) = 6.071, p = .001). These effects were seen to persist between until around 800ms (F(3,57) = 6.753, p = .001). Paired t-tests were conducted to further investigate conditioning effects.

Figure 7. ERP curves generated at frontal electrode sites AF3 and AF4 at baseline and again after one, six and sixteen conditioning rounds for neutral brands that were conditioned positively.

Gradual changes can be seen as a result of subsequent conditioning.

Electrode Site; Time Session (Mean; Standard T-test

Window Deviation)

AF3; 400-600ms Baseline (M = -1.44; SD = 2.81); one t = 2.577; df = 19; p = .018; two tailed

conditioning round (M = -2.85; SD = 1.67)

AF3; 400-600ms Baseline (M = -1.44; SD = 2.81); six t = 4.082; df = 19; p = .001; two tailed

conditioning rounds (M = -3.56; SD = 2.34)

AF3; 400-600ms Baseline (M = -1.44; SD = 2.81); sixteen t = 3.107; df = 19; p = .006; two tailed

conditioning rounds (M = -3.48; SD = 1.69)

AF3; 600-800ms Baseline (M = -1.23; SD = 3.03); one t = 2.767; df = 19; p = .012; two tailed

conditioning round (M = -2.95; SD = 2.34)

AF3; 600-800ms Baseline (M = -1.23; SD = 3.03); six t = 3.831; df = 19; p = .001; two tailed

conditioning rounds (M = -3.89; SD = 2.82) Electrode Site; Time Session (M; SD) T-test

Window

AF3; 600-800ms Baseline (M = -1.23; SD = 3.03); sixteen t = 3.758; df = 19; p = .001; two tailed

conditioning rounds (M = -3.94; SD = 2.15)

AF3; 800-1000ms Baseline (M = -1.46; SD = 3.84); one t = 2.445; df = 19; p = .024; two tailed

conditioning round (M = -3.12; SD = 2.59)

AF3; 800-1000ms Baseline (M = -1.46; SD = 3.84); six t = 2.950; df = 19; p = .008; two tailed

conditioning rounds (M = -3.59; SD = 3.04)

AF3; 800-1000ms Baseline (M = -1.46; SD = 3.84); sixteen t = 2.393; df = 19; p = .027; two tailed

conditioning rounds (M = -3.67; SD = 2.58)

AF4; 400-600ms Baseline (M = -1.57; SD = 2.82); six t = 2.280; df = 19; p = .034; two tailed

conditioning rounds (M = -2.77; SD = 2.37)

AF4; 400-600ms Baseline (M = -1.57; SD = 2.82); sixteen t = 2.607; df = 19; p = .017; two tailed

conditioning rounds (M = -3.23; SD = 2.16)

AF4; 600-800ms Baseline (M = -1.57; SD = 2.82); six t = 2.570; df = 19; p = .019; two tailed

conditioning rounds (M = -3.04; SD = 2.43)

AF4; 600-800ms Baseline (M = -1.57; SD = 2.82); sixteen t = 2.642; df = 19; p = .016; two tailed

conditioning rounds (M = -3.74; SD = 2.98)

Figure 8. Data obtained via t-tests. Baseline activity at frontal sites AF3 and AF4 was compared to that obtained after one, six, and sixteen rounds of conditioning.

In addition, at parietal sites, a 2 (hemisphere; right, left) x 4 (conditioning round; one, two, three, four) repeated measures ANOVA revealed no conditioning effects. Furthermore, paired t-tests also revealed no significant conditioning effects.

3.4.3 Asymmetry Effects

To check for asymmetry effects across frontal areas, a 2 (hemisphere: right, left) x 2

(brandtype: unpleasant, pleasant) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVA was conducted. The data revealed no significant brandtype x hemisphere interaction effects throughout the entire epoch. An identical ANOVA was conducted for parietal sites P5 and P6 and again, no interaction effects were present throughout the epoch.

3.4.4 Lateralisation Effects

To investigate potential lateralisation effects at frontal and parietal regions, a 2

(hemisphere: right, left) x 2 (brandtype: unpleasant, pleasant) x 4 (conditioning rounds: zero, one, five, ten) repeated measures ANOVA was conducted. Results revealed that across frontal regions, no hemisphere x session effect was evident. That is to say that neither hemisphere elicited greater activation during the presentation of the neutral brands during any session.

Similarly, no hemisphere x session effect was evident across parietal regions.

3.2.4 Filler Brands

In a similar manner as the liked and disliked brands, 2 (hemisphere: right, left) x 4

(conditioning rounds: zero, one, five, ten) repeated measures ANOVAs were conducted during each 200ms window beginning at 400ms. Paired comparisons using a Bonferroni correction were then conducted at frontal (AF3, AF4) and parietal sites (P5, P6). No conditioning or lateralisation effects were evident across frontal sites. As for parietal sites, again, right hemisphere dominance was evident. Activity across right hemisphere electrode site P6 was consistently greater than at

P5.

Figure 9. ERPs generated for all filler brands during each condition across frontal electrode sites

AF3 and AF4. No significant changes between sessions were evident.

Figure 10. ERPs generated for all filler brands during each condition across frontal electrode sites P5 and P6. No significant changes between sessions were evident. 4. Discussion

Using a conditioning paradigm, we were able to condition participant’s attitudes towards well-established, neutral brand names. Using a combination of traditional/explicit and innovative/implicit measures, we were able to assess the sensitivity of each measure in detecting changes in attitudes as a result of conditioning. At baseline, it was revealed that all measures were capable of identifying whether the brands shown were indeed neutral. However, after subsequent conditioning, only EEG appeared to be sensitive to changes in attitude. To strengthen the finding of EEG data to be sensitive to evaluative conditioning, we also report the finding that the filler brands used in the present study, which were also repeatedly presented, but without any conditioning between the sessions, did not show any of the effects that we found for the target brands.

4.1 Self Report and IAT

Although our pre assessment phase suggests that participants were able to differentiate between liked, disliked and neutral brands (see Bosshard et al., 2016), we found no differences in explicit ratings overs subsequent sessions following conditioning. This finding, although unexpected, is in line with the majority of conditioning literature that cite the effects of latent inhibition when trying to modify attitudes towards well established stimuli (Gorn, 1982;

Gresham and Shimp, 1985; Stuart, Shimp & Engle, 1987). For example, within consumer contexts, Kellaris and Cox (1989) presented participants with slides of a yellow pen whilst pleasant or unpleasant music was simultaneously played (similar in setting to viewing an advertisement). Participants were then allowed to select either a yellow or white coloured pen as a reward for having participated in the experiment. According to Kellaris and Cox, the valence of the music had no significant effect on the choice of coloured pen. As a result of such findings, a number of authors have raised awareness of the need to assess automatic or non-conscious forms of attitudes (Gibson, 2008; Bargh, 2002; Chartrand, 2005; Dijksterhuis et al., 2004). The contradictory findings between explicit and implicit attitudes presented in the current paper may support the theory of cognitive pollution posited by Walla et al. (2011).

The results regarding the IAT suggest that it may be sensitive to changes in attitudes; however these findings should be interpreted with caution. The results within the current paper suggest that after only a single round of conditioning, reaction times were seen to decrease. This effect was seen for both of the neutral conditions. However, this being said, subsequent conditioning saw increases in reaction time. As a result, we interpret our findings to suggest that the IAT may in fact not exclusively measure implicit attitudes, but instead the means by which participants grouped the stimuli. This interpretation is in line with that presented by several authors (De Houwer (2006); Gregg, Seibt & Banaji, 2006). In such cases, it was reported that participant’s reaction times were affected by mere supposition. Participants that had been presented two fictitious groups were instructed to imagine that one of the groups was positive

(good, peaceful, etc.) and the second was negative (bad, violent etc.). Results indicated that participants responded more quickly to the compatible condition than the incompatible condition in both scenarios.

4.2 Event Related Potentials

Of all the measures utilised within the current paper, EEG seemed to be the most sensitive to the effects of conditioning. These results reiterate the need for marketers to become less reliant on self-report measures. Within consumer contexts, motivation, be it approach or avoidance, plays an important role in an individual’s intentions to purchase or engage with a brand (MacInnis, Moorman & Jaworski, 1991; Youn, Wells and Zhao, 2001; Percy, Hanson &

Randrup, 2004).

According to the asymmetry model introduced earlier (Davidson et al., 1979), greater relative left frontal EEG activity is associated with the processing of positive/approach affects. Within consumer contexts, it has been shown that greater left frontal activity was associated with an increased likelihood for participants to purchase goods (Ravaja, 2013). Ravaja also indicated that when participants were offered a product at a price that was lower than expected, this also resulted in heightened left frontal activation, indicating motivation to purchase the item. In contrast, excessive pricing has been shown to result in greater right frontal activation (Davidson,

2004). The lack of such findings within the present study supports the notion that the brands viewed by participants were in fact neutral. Unexpectedly, even after conditioning, neutral brands did not generate greater left hemisphere activity after being positively conditioned, nor did negatively conditioned brands elicit greater right frontal activation. This finding is possibly due to the fact that brands are not as inherently affective as those used in previous attitude research. Typically, attitude research is seen to focus on associations that are innate and stronger

(eg. out-group prejudices; Brewer, 1999). In contrast, as mentioned previously, brand attitudes are thought be, for the most part, learned and highly semantic (Stuart, Shimp, & Engle, 2001).

Although no asymmetry effects were evident across frontal sites, conditioning was seen to elicit changes in implicit attitudes. Specifically, conditioning with unpleasant stimuli yielded gradual changes over all sessions whereas, conditioning in a pleasant direction elicited strong effects after only a single conditioning round, which were then seen to plateau throughout subsequent sessions. Although not statistically significant, visual inspection of the ERPs indicated that conditioning using unpleasant stimuli elicited greater effects than conditioning using pleasant stimuli. The ERPs findings presented within the current paper emphasise the importance of successful marketing strategies for neutral brands. Within psychology and marketing literature, it has been proposed that a negativity bias exist, whereby negative stimuli generally acquire more attention than those that are positive (Nguyen & Claus, 2013; Baumeister et al., 2001, Rozin & Royzman, 2001, Skowronski & Carlston, 1989). The results presented in the current paper reiterate those presented in extant literature and suggest that negative stimuli appear to be more motivationally significant and as a result, more potent forms of stimuli (Ito & Larsen, 1998; Shook, Fazio, & Vasey, 2007; Winchester & Winchester, 2009). Within an applied setting, there is an overwhelming amount of evidence that links negativity bias with a decrease in consumer sentiment. Furthermore, this reduction in sentiment has a negative effect on consumption while increases have no effect (Nguyen & Claus). All in all, it is plausible positive associations within consumer contexts are simply of less importance to us and thus, more difficult to establish (van den Bosch et al., 2015).

Moreover, for negatively conditioned stimuli, conditioning effects were seen to greatest after only one exposure. This finding is in line with a substantial volume of literature that suggest that one round of conditioning will typically elicit the largest change in attitude, whereas subsequent conditioning trials will evoke only smaller changes until a maximum is reached

(Stuart, Shimp & Engle, 1987; Domjan & Burkhard, 1985; Smith, Feinberg & Burns, 1998). This being said, more research is required before conclusions can be drawn given that the majority of existing conditioning literature either presents null findings or uses fictitious brand stimuli that are incomparable to the well-established stimuli utilised within the current research.

In addition to the conditioning effects at frontal sites, parietal sites were seen to elicit LPP effects. Existing literature suggests that LPP effects are most pronounced in the posterior scalp locations, maximally at parietal regions (Cacioppo et al. 1994; Pastor et al., 2008; Schupp et al.

2006; Zilber, Goldstein, and Mikulincer 2007). More specifically, the LPP has been reported to become enlarged in response to the processing of emotion laden stimuli (Heller, 1993), during the processing of stimuli that are evaluatively inconsistent (e.g., an unpleasant stimuli presented within a block of pleasant stimuli; Crites & Cacioppo, 1996; Cacioppo, Crites, Berntson &

Coles, 1993), or when viewing stimuli that are motivationally significant (Bradley et al. 2003;

Lang and Bradley 2010). Overall, there is a general consensus that there is right hemisphere dominance with respect to LPP effects (Cacioppo et al., 1996; Heller). Our findings substantiate existing literature, in that we too found right hemisphere dominance during the presentation of neutral brands with greater activity being recorded at right parietal site P6 than at left parietal site P5. This finding promotes the notion that the brands viewed by participants were either emotionally or motivationally significant, even though they were neutral. Previous research has also suggested that the LPP may provide useful in determining the valence of brand attitude

(Bosshard et al., 2016). Bosshard et al. revealed that liked brands elicited greater activity across right parietal sites than did brands that were reported by participants as being disliked. In the present research, no such differentiation between the neutral conditions was evident, once again providing evidence to support that idea the brands viewed by participants were neutral.

4.3 Conclusions

In the present study, self-report, ERP measures and the IAT were demonstrated to be sensitive to brand attitudes. However, only ERPs seemed to be sensitive to the effects of positive and negative conditioning on brands. These findings affirm the fact that brands and more specifically, brand attitudes are highly iterated and reprocessed constructs that are generally not well understood. The lack of LPPs and front hemisphere dominance revealed that the brands viewed by participants were indeed neutral. In terms of conditioning, neutral brands that were paired with negative sounds revealed larger effects than neutral brands paired with positive stimuli.

It is essential that several recommendations for future research be made. It is imperative that a distinction be made between neutral brands that participants have had no contact with and those that are well-known yet remain neutral. Having made this distinction clear, the findings presented in future papers will be useable within applied settings. Authors within this field must acknowledge that implicit attitudes exist and as a result, the current overreliance on fictitious brand stimuli is unnecessary. On that note, it is essential that future research utilises a combination of explicit and implicit measures of attitude. It is essential that authors realise that the traditional approach to consumer research is inadequate. A final recommendation is aimed at marketers. Whether marketing a brand, product, or an individual (eg. political campaign) it is important to have an understanding of the implicit mind of the consumer. Although at a conscious level attitudes may remain the same, negative events can have dire consequences on the implicit attitudes of an individual. When building strong positive relationships, it appears to be the case that a great deal of work can come unstuck when an individual is simply exposed to just a single negative association.

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General Discussion & Conclusion

The research presented within the current thesis imply that much like our other decisions, those that occur within consumer contexts may too be driven by non-conscious process that occur outside of our awareness. Additionally, the current research makes several advancements within the domain of consumer neuroscience and psychology, in that the findings are translational, the stimuli used were not fictitious, and it may also be possible that the data shows that contingency awareness may not be required for evaluative conditioning effects to occur.

More importantly this thesis, in its entirety, posits that not only is it possible that the effects of conditioning occur at differing levels of information processing, but also that novel approaches to the understanding of consumer behaviour may hold the key to potentially predicting future purchase intentions. It was hypothesised that at baseline, all measures, both implicit and explicit, would be sensitive to valence. In doing so, each measure was hypothesised to be capable of distinguishing between liked, disliked, and neutral brand attitudes. In addition, based on existing conditioning literature, it was hypothesised that subsequent conditioning would reveal no changes in explicit responses. Furthermore, we hypothesised that the IAT would differentiate between liked brand attitudes conditioned negatively, disliked brand attitudes conditioned positively, and neutral brand attitudes conditioned both positively and negatively. We also hypothesised that EEG would be most sensitive to attitude changes as a result of conditioning.

Specifically, it was hypothesised that liked brands would elicit greatest activity over left anterior brain regions whereas disliked brands would elicit greatest activity over right anterior regions.

Finally, it was hypothesised that LPP effects would vary as a function of brand attitude allowing us to make inferences about affect-based motivational aspects. Additionally, it was hypothesised that LPP activity for liked and disliked brands would change sequentially as a result of conditioning. 1.1 Explicit responses

In line with predictions, at baseline, self-reported ratings towards brand names strongly reflected positive and negative brand affect. This finding is of no surprise. Another finding that came as no surprise was that explicit ratings did not change as a result of conditioning.

Psychological literature suggests that attitudes towards well-established stimuli (including that of brand names) are relatively stable and unaffected by conditioning paradigms (Stammerjohan et al., 2005). Although the inability to express one’s attitudes is commonly reported throughout consumer behaviour research, very few explanations are put forth to suggest why this occurs. As mentioned previously, latent inhibition may provide some insight, however drawing such a conclusion may be far from what actually occurs. What must be noted here is that the LI explanation is primarily put forth when direct comparisons between explicit and implicit measures are not made. An alternative explanation, according to Solms and Panksepp (2012), suggests that the brain knows more than it is willing to admit at a conscious level. The inability of the brain to ‘admit’ what it knows, according to Walla et al., (2011) and Walla & Panksepp

(2013) may occur as a result of cognitive pollution. Using this explanation, it is implied that the process of thinking about or evaluating a stimulus results in the utilisation of higher order cognitive processes which inevitably pollutes an individual’s responses. As a result of such pollution, responses that arise do not accurately depict the true evaluations of the responder.

Although such an explanation requires further research, when viewed concurrently, the data obtained within the current study via traditional measures and those obtained via neurophysiological measures seem to support this notion.

1.2 Implicit Association Test

With regard to the IAT, the results are as expected, and demonstrate that this measure may not reliably indicate an individual’s attitude. Even though, at baseline, our findings seem to imply that the IAT is an effective measure of attitude, after conditioning the measure appears to lack the sensitivity required to determine any changes in attitude. The findings presented in our study support those reported by Gibson (2008), who found that, at baseline, the IAT seemed to provide an accurate insight into consumer preferences however, after conditioning, the IAT did not detect any changes in attitudes. During the abovementioned study, Gibson required participants to evaluate the drinks Coke and Pepsi during a pretest and again after having undergone conditioning. Gibson reported that when the brands were well known and evaluated as liked or disliked, the IAT did not effectively detect changes in attitude.

However, before ruling out the use of the IAT completely, its use as a measure of neutral brand attitudes is far more contentious. Although the findings of the present research suggest that the IAT lacks the sensitivity to detect changes in attitudes towards neutral brands, discrepant findings have been reported. For instance, Gibson reported that implicit neutral attitudes can be changed. In addition, Olson and Fazio (2001) reported that implicit attitudes can be changed provided explicit attitudes are also change. With respect to Gibson’s findings, it is impossible to rule out demand effects. Gibson states that the participants were able to identify the purpose of the study at greater than chance levels. With respect to the findings presented by Olson and

Fazio, the fact that implicit attitudes could only be changed when explicit attitudes were changed may provide additional support that demand artefacts influenced the results. In the present study, although not explicitly recorded, we assume that participants were unaware of the purpose of the study. In line with the findings presented by Olsen and Fazio, the lack of changes to both the explicit ratings and the IAT may implicate the involvement of conscious processes within the

IAT. It is exactly for this reason that we believe our data reiterates the importance of utilising the

IAT with caution. Moreover, although the findings of the present study in which no changes to explicit ratings were reported, contradict those presented by Olson and Fazio, it is plausible that this may have been due to the nature of the stimuli used. In contrast to the present study which saw the use of well-established attitudes, Olson and Fazio were seen to condition fictitious stimuli. It is plausible that possessing no attitude towards a stimulus is more likely to allow for the formation of an attitude than forming a positive or negative attitude towards a well-known, yet still neutral brand. The use of well-established and fictitious stimuli is a different, yet equally concerning topic and will be discussed in further detail below.

1.3 Electroencephalography

In contrast to both the traditional, explicit measures and the contentiously implicit IAT utilised within the present study, electroencephalography provides far more promising results. At baseline, we provide evidence that liked and disliked brands are implicitly associated with deep positive and negative affect respectively. These findings were supported by both the asymmetry effects over frontal sites, in addition to the LPP effects witnessed across parietal sites.

In line with previous literature presented by Davidson et al. (1979), the asymmetry model asserts the general assumption that approach motivations are linked to pleasant affect across the left anterior brain regions whereas, withdrawal motivations are linked with unpleasant affect across the right anterior brain regions. Our findings promote the notion that liked brands elicit greater activation across anterior left brain regions whereas disliked brands elicit greater activation across right anterior brain regions. With such findings comes strong support for the notion that brands and brand attitudes are respectively linked with not only implicit affect, but also strong motivational components. What makes this finding ever so clearer is that the neutral brands within the present study were seen to elicit no such frontal asymmetries. Together, these findings may provide further evidence that the use of the asymmetry model within consumer contexts is warranted. More specifically, it is plausible that the use of the asymmetry model may provide insight when attempting to determine future purchase behaviour. Although little research is available, according to Ravaja, Somervuori, and Salminen (2013), the approach related motivation derived as a result of left anterior activity has the potential to be a good indicator of future purchase intentions. Ravaja et al., reported that increased left frontal activation was associated with a higher perceived need for a product, during the presentation of favoured brands, and prior to engaging in a purchase decision. Although the current study did not employ a task within which participants were required to make a purchase, based on these findings, it is highly probably that the brands that elicited greater left frontal activation may have been those that participants were most likely to purchase.

In addition to the possible motivational components detected via the use of frontal asymmetry, the LPPs witnessed across parietal sites may provide further support that the brands presented within the current research are of motivational significance. At baseline, and after conditioning, LPPs were seen for both liked and disliked brands. Moreover, these effects were seen to be enhanced across right parietal electrode sites. These findings are in line with the majority of the literature that suggests LPP effects should be enhanced for both pleasant and unpleasant stimuli (Moran et al., 2013; Cacioppo et al., 1996; Cacioppo, Crites, Berntson &

Coles, 1993). What is more interesting is the possibility that our data promotes the use of the

LPP as a measure of affect. In contrast to previous literature, the liked brands utilised within the present study were seen to elicit larger LPP effects. Although this is possible, it more likely the case that liked brands were processed in a similar manner to disliked and neutral brands. In the present study, participants were exposed to one large block of brand names, which included all liked, disliked, and neutral brands. It is commonly asserted that, larger LPP effects are evoked by evaluatively inconsistent stimuli (e.g. an unpleasant stimulus presented within a block of pleasant stimuli) than by evaluatively consistent stimuli (e.g. a pleasant stimulus presented within a block of pleasant stimuli; Crites & Cacioppo, 1996; Cacioppo, Crites, Berntson & Coles, 1993). Given that liked brands elicited a larger LPP, it may be the case that they were evaluatively inconsistent, and thus processed in a different manner to neutral and disliked brand names. The idea that disliked and neutral brands are processed similarly is not too farfetched. The majority of psychological literature is seen to utilise innate, negative stimuli toward which a strong aversion or a strong motivation to avoid is held. In contrast, it is highly unlikely that such avoidance arises towards brands. For instance, although an individual may be motivated to avoid people of a different race (Brewer, 1999), foods that have an unpleasant taste (Nikitin et al., 2016), or stimuli that have an unpleasant smell (Stancak et al., 2015), it is highly unlikely that the same aversion would be maintained when viewing a brand that is disliked. It is obvious that in these cases, the previously mentioned stimuli are avoided as a result of an innate aversion, whereas the aversion of a brand is more to do with simply not liking the brand, thus the consumer avoids making a purchase.

The final interesting piece of evidence derived from the current study with regards to

EEG is its sensitivity to detect conditioning effects. No papers to our knowledge have assessed the use of EEG as a means by which to determine conditioning effects. For this reason, before any assumptions are made, it must be stated that more research is needed. This being said, the data recorded throughout the present research promotes the use of EEG as an appropriate tool to measure the effects of evaluative conditioning towards liked and disliked brands. As expected, sequential changes in frontal and parietal activity were recorded as a result of evaluative conditioning of liked and disliked brands. Specifically, EEG presented findings which promoted the notion that despite repeated exposure, a single round of conditioning elicited the greatest effects on implicit attitudes. Moreover, the use of EEG also presented findings to suggest that liked brands are more resistant to the effects of conditioning.

Similarly, conditioning of neutral brands was seen to elicit similar effects. For instance, after only a single round of conditioning, neutral brands paired with unpleasant stimuli yielded gradual changes over all sessions. In contrast, conditioning in a pleasant direction elicited strong effects after a single conditioning round, and then rather stabilised effects throughout subsequent sessions. Again, these findings may suggest that neutral brands are more easily conditioned in a negative manner than a positive manner. Within the context of this research study, EEG seemed to allow for the most implicit insight into brand attitudes. Such findings are exciting and will be discussed in further detail within the implications section. Although some may approach our findings with caution, the inclusion of filler brands further support the notion that EEG is sensitive to conditioning effects. Filler brands were those that had been repeatedly presented throughout each phase of the experiment but were not conditioned. As expected, these brands revealed no significant conditioning effects. Instead, visual inspection revealed minor changes in activity, which may possibly reflect repetition effects.

2. Implications

The findings of the current thesis, first and foremost, support the use of a multidimensional approach to understanding consumer attitudes. Traditionally, survey type approaches to understanding consumer behaviour have resulted in numerous findings, many of which support the notion that well-established brand attitudes are resistant to change. The recent inclusion of implicit measures of attitudes within the literature has led to a general consensus that brain activity may hold the key to understanding consumer behaviour. It is possible that the application of implicit measures will lead to a clearer and more accurate understanding of consumer behaviour. The first evidence for this is the lack of discrepant findings when implicit measures are utilised. Even the findings presented within the current thesis support those which have been previously reported. With this in mind, is imperative that future research within both a laboratory setting and within a marketing setting continue with this approach.

Furthermore, it has been proposed above that the lack of acknowledgement of implicit processes within marketing has resulted in confusion as to whether brand attitudes can be changed and by what means is it possible to change brand attitudes, if at all. Tentative support for the ability to modify consumer attitudes is presented within the included papers. Supporting the notion that attitudes can be modified, at least at a non-conscious level, are the findings presented via the use of EEG. Such findings suggest that after only a single round of conditioning, implicit attitudes are changed. These changes are evident for liked, disliked, and neutral brands. If it is indeed the case that a single exposure to a stimulus, be it positive or negative, is enough to elicit changes in implicit attitudes whilst remaining conditioning elicits very few changes, it is possible that these findings suggest that a single advertisement should not be too heavily relied upon. According to the literature, it is possibly the case that over exposure to a single advertisement may result in a negative perception of the product or brand. To resolve this issue, it is suggested that businesses employ a variety of stimuli (potentially of differing mediums) with which they pair their brand.

The most exciting aspect of this research is the possibility that implicit measures allow us to predict future purchase behaviour. Although much of the previous research focused on identifying a ‘buy button´ within the minds of consumers, which could be ‘pressed’ by neurophysiological measures in order to facilitate a purchase, the current research presents a much more plausible approach to being able to predict purchase behaviour. In doing so, the current papers may also remove much of the stigma associated with what is more commonly known as ‘neuromarketing’ and promote the use of neurophysiological measures to understand consumer behaviour as opposed to implementing such an approach to force consumers to buy things that they do not want. Although predicting purchase behaviour fell outside of the scope of the present thesis, existing literature (Ravaja et. al, 2013) suggests that implicit measures, in particular EEG, may hold the key to predicting purchase behaviour.

Although there is an obvious need to implement implicit measures within consumer contexts, prior to this, there is a dire need for marketing studies to differentiate between neutral stimuli with which participants have no attitude towards, and those with which participants are familiar however, possess no explicit attitude towards. The current thesis promotes the idea that there is indeed a difference between possessing a neutral attitude towards a stimulus, and possessing no attitude towards a stimulus. Although concerning, the lack of papers seen to make this distinction is somewhat justified. As a result of the discrepant findings, paired with constant reports that well-established stimuli could not be changed, researchers began to utilise fictitious stimuli. This approach was also witnessed within consumer research. As a result of using fictitious stimuli, the findings presented within existing literature may not realistically depict attitude changes that occur within genuine marketing contexts. The results presented within much of the existing literature, although not directly suggesting so, imply that advertising a well- established brand with the intention of changing the attitude of the consumer is a waste of time and money. In contrast, the present research may suggest the exact opposite. Instead, the present findings promote the use of advertising campaigns as a successful approach to changing the attitudes of consumers in order to gain a larger consumer base.

On a final note, although, the above studies elude that changes in implicit brand attitudes brought about by repeated pairings with sounds is an appropriate medium by which attitudes can be modified, future research should assess the appropriateness of other mediums. The ineffectiveness of sounds to continue to elicit strong changes in attitudes after a single round of conditioning within our studies invites the question of whether sounds are the best means by which to elicit changes in attitudes. It is possible that other mediums may be more inherently affective. If this were indeed the case, such mediums may elicit larger changes in attitudes.

3. Final comments

The current thesis presents a new insight into understanding consumer behaviour. Not only has the isolated use of traditional methods of assessing consumer behaviour been further criticised, but the use of electroencephalography as an alternative measure has been proposed.

The findings of the present study allow for an exciting new approach to understanding consumer behaviour. In addition, the use of evaluative conditioning as a means to successfully mould consumer attitudes is supported. In sum, the above studies present further support for the idea that conditioning effects do not occur at all levels of consciousness. As a result, these findings reiterate the potential for evaluative conditioning effects to occur without contingency awareness. With these findings comes a new understanding of processing stimuli within evaluative contexts, one where cognitive pollution, and not latent inhibition, is the antagonist which prevents the consumer from stating their attitudes. We suggest that future research continue to focus on changing attitudes towards well-established brands, with the end goal to accurately anticipate future purchase behaviour without the consumer explicitly stating their preference.

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PlosOne, 6(11): e26782. doi:10.1371/journal.pone.0026782.

Appendix A: Initial Brands Stimulus List

1. Logitec 52. Nestle 103. Qantas 2. Kirks 53. Kleenex 104. Jet Star 3. Coca-Cola 54. Vans 105. Officeworks 4. Microsoft 55. Pizza Hut 106. Toshiba 5. Powerade 56. Motorola 107. Coopers 6. Lipton 57. Kodak 108. Durex 7. Apple 58. Adidas 109. Tooheys 8. Samsung 59. Rolex 110. Corona 9. Connor 60. Audi 111. Victoria Bitter 10. IBM 61. Hyundai 112. James Squire 11. GE 62. Panasonic 113. Jim Beam 12. Intel 63. Kraft 114. Bundaberg 13. Nokia 64. Porsche 115. Whiskas 14. Toyota 65. Tiffany & Co. 116. Pedigree 15. Disney 66. Duracell 117. Oak 16. McDonald's 67. Moet & Chandon 118. Mount Franklin 17. Mercedes-Benz 68. Johnson & Johnson 119. Peter Alexander 18. Marlboro 69. Shell 120. Dolce & Gabana 19. Lacoste 70. Nissan 121. Roxy 20. American Express 71. Starbucks 122. Western Digital 21. BMW 72. Lexus 123. Suzuki 22. Gillette 73. Smirnoff 124. Subaru 23. Louis Vuitton 74. LG 125. Holden 24. Honda 75. Prada 126. Mazda 25. Pepsi 76. Armani 127. Mitsubishi 26. Nescafe 77. Nivea 128. Cadbury 27. Hungry Jacks 78. Levis 129. Allen’s 28. Dell 79. Vegemite 130. Starburst 29. Sony 80. Fosters 131. Lego 30. Budweiser 81. Aspro 132. Dolmio 31. Oracle 82. Johnnie Walker 133. Guiness 32. Ford 83. Speedos 134. Pandora 33. Nike 84. Heineken 135. Marc Jacobs 34. Canon 85. Westpac 136. Victor & Rolf 35. Kellogg's 86. Ansell 137. Lorna Jane 36. Ikea 87. Billabong 138. Dior 37. Siemens 88. Bluescope 139. Maybelline 38. Harley-Davidson 89. Optus 140. Winfield 39. Gucci 90. Arnotts 141. Longbeach 40. Dunlop 91. Bakers Delight 142. Gatorade 41. Philips 92. Boost Juice 143. Wilson 42. Nintendo 93. Coles 144. Fender 43. L’Oreal 94. Woolworths 145. Gibson 44. Heinz 95. Angus & Robertson 146. Lindt 45. McCain’s 96. Eagle Boys 147. Lynx 46. Volkswagen 97. Dominoes 148. Tefal 47. Colgate 98. Telstra 149. Bridgestone 48. Wrigley's 99. Dick Smith 150. Supre 49. KFC 100. David Jones 151. Casio 50. Chanel 101. Hamilton 152. Calvin Klein 51. Avon 102. Energy Australia 153. Dove 154. Ripcurl 211. Mossimo 268. Impulse 155. Havaianas 212. Rusty 269. OMO 156. Frontline 213. Volcom 270. Sunbeam 157. Friskies 214. Akubra 271. Red Rock 158. Ferrari 215. R. M. Williams 272. Breville 159. MyDog 216. Ajax 273. Delonghi 160. Vodaphone 217. Uncle Toby’s 274. Ibanez 161. Dr Lewins 218. Glade 275. Steeden 162. Ambi Pur 219. Walkers 276. Palmolive 163. Schick 220. Husqvarna 277. New Balance 164. Subway 221. Birds Eye 278. Asus 165. Fisher Price 222. Praise 279. Kambrook 166. Schweppes 223. Flora 280. Nikon 167. Fujitsu 224. Philadelphia 281. Reebok 168. Bonds 225. Jalna 282. Sharp 169. HTC 226. Colby 283. Virgin 170. Garnier 227. Coon 284. Hummer 171. TRESemme 228. Baileys 285. Lamborghini 172. GHD 229. Victoria’s Secret 286. Wrangler 173. Sea Folly 230. Oral B 287. Ego 174. Tiger Lilly 231. Bostik 288. Acer 175. Paul Frank 232. John Deere 289. Allianz 176. Olay 233. Sega 290. Baxter 177. Piping Hot 234. Zippo 291. Chum 178. Ray-Ban 235. Tic Tac 292. Clearasil 179. Remington 236. Mobil 293. Proactiv 180. Whitmans 237. Swisse 294. Compaq 181. Dulux 238. Campbell’s 295. DNKY 182. Homebrand 239. Mars 296. Dettol 183. Black & Gold 240. Yamaha 297. Fuji Film 184. No Frills 241. Sandisk 298. Goodyear 185. Trills 242. Betty Crocker 299. Dyson 186. Dilmah 243. Maggi 300. Nexcare 187. Everlast 244. Sara Lee 188. Neutrogena 245. Viva 189. Vera Wang 246. Reece 190. Tony & Guy 247. Nesquik 191. Cotenelle 248. Mortein 192. Revlon 249. Old el Paso 193. Hewlett Packard 250. Pascall 194. Oakley 251. Crayola 195. Daihatsu 252. Radox 196. Slazenger 253. Trident 197. Tapout 254. Evian 198. Banana Boat 255. Twinings 199. Fisher Paykel 256. Primo 200. Converse 257. Rivers 201. Hasbro 258. Libra 202. Smiths 259. Saxbys 203. Panadol 260. Steggles 204. Neurofen 261. Toblerone 205. Eveready 262. Meadowlea 206. Esprit 263. Helgas 207. Energizer 264. Wonder White 208. Guess 265. White Wings 209. Globe 266. Four Seasons 210. Lonsdale 267. Sunsilk