“Why Can't We Stop Eating?”: an Investigation of the Emotional and Cognitive Processing of and Reactivity Toward Cues in F

“WHY CAN’T WE STOP EATING?”: AN INVESTIGATION OF THE EMOTIONAL AND

COGNITIVE PROCESSING OF AND REACTIVITY TOWARD CUES IN FOOD

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Abstract

by Jiawei Liu, Ph.D. Washington State University July 2018

Chair: Rachel L. Bailey

The purpose of this dissertation is to examine the relationship between the presence of different types of food cues (i.e., social cues, use cues) in food advertisements and subsequent motivational, affective and psychophysiological responding, memory, and behavioral intentions.

In general, this dissertation has three goals. The first is to examine how different types of food cues affect automatic cognitive resource allocation toward encoding and storing food ad information. The second goal is to examine if the food ads containing multiple types of cues elicit stronger, additive, affective and cognitive responding compared to other types of food advertisements. The third goal is to investigate how the presence of different types of food cues in food advertising encourage buying tendencies toward the food products. Results indicated that the addition of use and group cues to food ads elicit stronger appetitive motivational activation that yield greater cognitive efforts, arousal, positive emotional feelings, and approach responses, including purchase intentions. Cognitive processing results indicated that the addition of use cues

vi has more impact on resources required of a message while social cues have more impact on resources allocated. Implications and future research are discussed.

vii TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... iii

ABSTRACT ...... vi

LIST OF TABLES ...... x

LIST OF FIGURES ...... xi

CHAPTER

CHAPTER ONE: INTRODUCTION ...... 1

Overview ...... 1

Contribution to theory...... 4

Contribution for practice ...... 7

CHAPTER TWO: REVIEW OF LITERATURE ...... 10

Obesity and The Power of Food Marketing ...... 10

Cue Reactivity in Substance Addiction Research ...... 14

Cue Reactivity in Food Research ...... 18

Assessing Dynamic Affective and Cognitive Processing ...... 26

CHAPTER THREE: METHODS ...... 41

Experiment 1 ...... 41

Experiment 2 ...... 52

CHAPTER FOUR: RESULTS ...... 59

Emotion ...... 59

Resource Allocation (STRTs), Encoding, and Storage Performance...... 78

Heart Rate ...... 94

viii Later Behavioral Intentions ...... 99

CHAPTER FIVE: DISCUSSION ...... 105

Goal 1: Cognitive Processing of Use and Social Cues ...... 105

Goal 2: Examining Addictive Appetitive Responses of Use and Social Cues ...... 107

Goal 3: Later Behavioral Intentions ...... 108

Limitations of This Dissertation ...... 109

Theoretical Implications ...... 110

Practical Implications ...... 114

REFERENCES ...... 116

APPENDIX

Sample Coding Sheet ...... 136

Encoding and Storage Performance ...... 137

ix LIST OF TABLES

Page

Table 3.1: Summary Statistics of Selected Food Ads ...... 42

Table 3.2: Estimates for Multilevel Models of Self-reported Arousal ...... 43

Table 3.3: Brief Introduction for the Formula ...... 50

Table 4.1: Estimates for the Three-level Multilevel Models of Skin Conductivity ...... 61

Table 4.2: Estimates for the Three-level Multilevel Models of OO Activation...... 66

Table 4.3: Estimates for the Three-level Multilevel Models of CS Activation ...... 70

Table 4.4: Estimates for the Two-level Multilevel Models of Self-reported Positive Emotion ... 74

Table 4.5: Estimates for the Two-level Multilevel Models of Self-reported Negative Emotion .. 76

Table 4.6: Estimates for the Two-level Multilevel Models of Self-reported Arousal Emotion .... 78

Table 4.7: Estimates for the Three-level Multilevel Models of STRTs ...... 80

Table 4.8: Estimates for Multilevel Logistic Models of Recognition Accuracy ...... 84

Table 4.9: Estimates for Multilevel Logistic Models of Encoding Performance ...... 85

Table 4.10: Estimates for Multilevel Logistic Models of Storage Performance ...... 92

Table 4.11: Estimates for Three-level Multilevel Models of Heart Rate...... 98

Table 4.12: Estimates for Multilevel Models of Favorable Attitude toward the Product ...... 100

Table 4.13: Estimates for Multilevel Models of Social Support ...... 101

Table 4.14: Estimates for Multilevel Models of Purchase Intention ...... 102

Table 4.15: Estimates for Multilevel Models of Behavioral Control ...... 103

x LIST OF FIGURES

Page

Figure 3.1: Social Cue Exemplars ...... 45

Figure 4.1: Skin conductivity as a function of social cue (individual vs. group cues) and time .. 63

Figure 4.2: Skin conductivity as a function of social cue (individual vs. group cues) and use cue

(absence vs. presence) and time ...... 64

Figure 4.3: OO activation as a function of use cue (absence vs. presence) and time ...... 67

Figure 4.4: OO activation as a function of social cue (individual vs. group cues) and time ...... 68

Figure 4.5: OO activation as a function of social cue (individual vs. group cues) and use cue

(absence vs. presence) and time ...... 69

Figure 4.6: CS activation as a function of use cue (absence vs. presence) and time ...... 71

Figure 4.7: CS activation as a function of social cue (individual vs. group cues) and time ...... 72

Figure 4.8: CS activation as a function of social cue (individual vs. group cues) and use cue

(absence vs. presence) and time ...... 73

Figure 4.9: Positive emotion as a function of social cue (individual vs. group cues) and use cue

(absence vs. presence) ...... 75

Figure 4.10: Negative emotion as a function of social cue (individual vs. group cues) and use cue

(absence vs. presence) ...... 77

Figure 4.11: STRT latency as a function of social cue (individual cue (S-) vs. group cue (S+)), use cue (absence (U-) vs. presence (U+)) and placement of probe within message ...... 82

Figure 4.12: Visual criterion bias as a function of social cue (individual cue (S-) vs. group cue

(S+)) and use cue (absence (U-) vs. presence (U+)) ...... 90

Figure 4.13: Heart rate as a function of use cue (absence (U-) vs. presence (U+)) and time ...... 95

xi Figure 4.14: Heart rate as a function of social cue (individual cue (S-) vs. group cue (S+)) and time ...... 96

Figure 4.15: Heart rate as a function of social cue (individual vs. group cues) and use cue

(absence vs. presence) and time ...... 97

xii

Dedication

This dissertation is dedicated gratefully to my beloved parents.

xiii

CHAPTER ONE

INTRODUCTION

Overview

Currently more than a third of U.S. adults are obese (Ogden, Carroll, Kit, & Flegal, 2014).

This epidemic brings with it not only reduced longevity for those suffering from obesity and its comorbid disorders (e.g., heart disease, type II diabetes), but also a societal financial burden via increased health care costs (Finkelstein, Trogdon, Cohen, & Dietz, 2009). Since 2013, health professionals have suggested treating obesity as a chronic disease (Jensen, Ryan, Apovian, Ard,

Comuzzie, et al., & Yanovski, 2014). While health experts and researchers warn that obesity is a risk factor for various types of disease, many people still have difficulty resisting food cravings, especially those triggered by cues such as those used in advertising.

The food information in mediated messages (e.g., food advertising, images, TV shows) available to people at home, at work, and in most shopping venues, and the availability of food in these places, influences eating behavior. When food is in our environments, we are automatically encouraged to eat. When we encounter food, automatic appetitive responses occur driving us to seek more information about the food and eventually consume it, if appropriate. Current research has indicated that exposure to food cues, which are images or signals placed in mediated messages (e.g., the texture and color of the food, social and emotional scenes) to elicit a particular response from viewers, can help create stronger motivational responses and more favorable attitudes toward foods, which then lead to increased purchase intentions and stronger craving tendencies (Bailey, 2015, 2016, 2017; Lawrence, Hinton, Parkinson, & Lawrence, 2012).

Moreover, it is generally accepted that viewing food related mediated messages such as food advertising can cause overeating (i.e., Avena & Gold, 2011; Cohen, 2008; Rosin, 2008). For

1 example, researchers have found that people consumed more food regardless of satiation following exposure to food advertising (e.g., Harris, Bargh, & Brownell, 2009). Cue-reactivity research attributes this effect to the impacts of food cues in food ads, which are pivotal when it comes to encouraging appetitive motivational responses in viewers (Bailey, 2016, 2017; Larsen,

Hermans, & Engels, 2012; Sobik, Hutchison, & Craighead, 2005). From a cue reactivity perspective, as the cues in food ads activate the motivation to eat, people are more likely to overeat during and/or after food adverting exposure.

The persuasive effect of food cues in food advertising has been well recognized as effective and is highly utilized by food marketers. As reported by the Yale Rudd Center for Food

Policy & Obesity (2013), fast food restaurants spent a total of $4.6 billion on all advertising (i.e. radio, magazine, television, newspaper, new media, outdoor advertising) in 2012. Considering the negative consequences of obesity and overeating on people’s health, some health experts want to control or ban advertising on unhealthy food (e.g., food or drink high in fat, salt, and/or sugar), especially to children and young adults (Boseley, 2016; Siddique, 2013). Researchers have proposed that controlling or banning exposure to food advertising can reduce this worldwide obesity prevalence (Alkharfy, 2011). Thus, many scholars and practitioners believe food cues in advertising can encourage problematic eating behaviors; however, it may be that food cues contain important nuances that should be explored prior to policy adjustment. For example, it is still not clear how different types of food cues food advertisements affect motivational activation and subsequent cognitive processes.

Though results from previous research have confirmed the cue-elicited motivational activation during television food advertising (Bailey, 2015, 2016; Bailey, Liu, Wang, Kaiser,

2016; Harris et al., 2009; P. Lang, Greenwald, Bradley, & Hamm, 1993; Lawrence et al., 2012),

2 research is lacking regarding how the contexts (i.e., social situations, biologically relevant situations) of these cues create different processing and experiential states. Thus, this dissertation tests human appetitive motivational and emotional reactivity to different types of food cues in selected food ads. As television advertising is the dominate channel for food marketing (Holt,

Ippolito, Desrochers, & Kelly, 2007), this dissertation uses televised food advertisements as the experimental stimuli to investigate how different types of food cues encourage eating and create different cognitive and emotional responding.

For this reason, this dissertation has three primary goals. The first goal of this dissertation is to examine how different types of food cues affect automatic cognitive resource allocation toward encoding and storing food ad information. Though previous research has suggested that food-related stimuli can be detected faster visually than non-food-related stimuli and are better stored for later recollection (Harrar, Toepel, Murray, & Spence, 2011; Morris, DeGelder,

Weiskrantz, & Dolan, 2001; Stoeckel, Cox, Cook, & Weller, 2007), it is unclear that how the presentation of different types of food cues in mediated messages will influence how much of the detail in these messages is accurately encoded and stored. The second goal is to examine if the food ads containing multiple types of cues elicit stronger, additive, affective and cognitive responding compared to other types of food advertisements. If multiple types of cues are present do they add to the appetitive motivational reaction to the ad, or do they inhibit one another? The third goal is to examine how the presence of different types of food cues in food advertising encourages buying tendencies toward the food products. The presence and co-presence of different types of cues in food ads may function differently for different types of individuals.

3 Contribution to Theory

In general, the primary theoretical importance of this study rests on its contribution to advancing the understanding of how cue-elicited motivational activation influences information processing during television food advertising viewing. Based on a dual-systems motivational model, any stimulus in the environment that is relevant to biological imperatives can automatically activate either one or both of two motivational systems, which have been called the appetitive and the aversive systems (Cacioppo & Berntson, 1994; Cacioppo & Berntson,

1999; Cacioppo & Gardner, 1999; Cacioppo, Gardner, & Berntson, 1997). Food is a primary biologically relevant motivator in our environment that encourages automatic attention and approach behaviors (Bradley, Codispoti, Cuthbert & Lang, 2001). As humans, we are motivated to procure food for survival and hedonic/emotional well-being purposes. From an affordance perspective (Gibson, 1986), humans are predisposed to visually detect and perceive either real food or mediated food cues in terms of food’s properties such as its palatability, nutrition, and energy levels. This may lead to subsequent food intake behavior, or behavioral intention to eat.

Current research has indicated that food ads (especially those advertising higher energy density foods) containing direct food cues (cues that visually depict perceptual characteristics of the food itself like color, texture, shine, etc.), can elicit pronounced appetitive physiological responses resulting in increased attention to the food cues that signal eating affordances and self- reported pleasant emotional feelings (Bailey, 2015, 2017). Thus, encountering ready-to-eat

(direct) food cues in the environment automatically activates the appetitive motivational system, which elicits stronger approach and consumption. However, little research has examined the nuance around these direct cues. For example, is there a difference if food is portrayed being eaten or simply being displayed?

4 Researchers in the field of substance addiction research have begun to develop and study this type of nuance. These researchers have developed and applied a cue-reactivity paradigm to measure individual cue-reactivity during substance-related information processing and have categorized substance cues into four different types based on the modes of cue presentation:

(1) Exteroceptive: visual representations of the actual substances, such as their smell, taste, and sight; (2) Interoceptive: refers to internal cues including individual emotional and somatic reactions (stress response, negative emotional feelings, and withdrawal state); (3) Temporal: time of day would as a cue for substance use, as in it is the proper time to consume the substance; and

(4) Chained: refers to a learned association between substance intake/addiction and substance- related cues (Drummond, 2000). These types of studies focus on how the cues might function differently in human emotional experience and motivated cognition. However, unlike studies on substance addiction, food cues have not been thoroughly investigated and categorized. Though the effects of food cues in advertising have been confirmed by current research (e.g., Bailey,

2015, 2016; Bailey et al., 2016; Harris et al., 2009; P. Lang et al., 1993; Lawrence et al., 2012), there are still no consistent definitions and categorizations with respect to different types of food- related cues in media messages.

Following Bailey (2015, 2017), this dissertation conceptualizes three types of cues using a Gibsonian affordance perspective (1986), which is particularly useful here as food is a primary biological motivator, or an object that is necessary for biological success (Bradley, et al., 2001;

Hull, 1943). These three types of food cues in adverting are: (1) Direct food cues: visual representations of the actual food products, which shares the perceptual properties of the actual food products (Bailey, 2015, 2017) and afford access to food; (2) Social cues: presence and activities of one individual or social groups (≥2 individuals), which afford social support to

5 encourage viewers to consume food; and (3) Use cues: actual food consumption behavior in food advertising which affords eating behavior. In this dissertation, direct food cue was a constant instead of a parameter across all types of food ads, while the influences of the presence of social and use cues in food ads were examined.

This division and categorization of these cues also is useful because it has been generally accepted that our food choices and food preferences are not only influenced by homeostatic energy balance requirements, but also by hedonic eating (see Lowe & Butryn, 2007). With this in mind, the availability and palatability of foods (direct food cues), interpersonal and social influences regarding our food choices (social cues), and actual eating behavior of others (use cue) in mediated messages are associated with whether foods will be favored or not. However, even though it has been commonly accepted that the hedonic properties of food influence appetitive motivational activation and emotional, cognitive and subsequent behavioral responses, it is unclear how the co-presentation of different types of food cues in advertising will function to alter motivational reactivity, emotional, cognitive and behavioral responding. Certain combinations may prove to be more appealing and influential than others.

Currently food advertisers and marketers prefer to use different combinations of cue types to attract customers, likely due only to creative license rather than theory-driven predictions about their effectiveness. For example, in a Burger King Cheesy Tot commercial, a man asks his friend for one of his cheesy tots while dining at a Burger King restaurant and then takes all of them because he thinks they are so delicious. In this ad, “the man and his friend” being present can be categorized as a social cue, or a social endorsement of the food, and their

“eating Burger King cheesy tots” can be categorized as a use cue, or the presented affordance of an object being edible and tasty, and the portrayal of Burger King cheesy tots themselves can be

6 viewed as direct food cues, indicating food is readily available. The question this dissertation seeks to answer is whether this type of ad is more effective due to greater motivational relevance than an ad that only depicted the cheesy tots. By examining how these cues by themselves and in co-presented combinations influence motivational, cognitive and emotional responses to different advertisements, this dissertation will add to the literature on cue-reactivity, primary biological motivators, mediated message processing and ecological perception of affordances.

Contribution for Practice

Beyond the theoretical importance of this dissertation, this dissertation also offers practical implications to both the media industry and health professionals. A great deal of research has demonstrated the effects of food advertising on overeating and obesity (i.e. Cohen,

2008; Avena & Gold, 2011; Rosin, 2008); however, most advertising executives and health professionals are not necessarily considering how the co-presence of different types of cues (i.e. social cue, use cue) in targeted food advertising and obesity prevention Public Service

Announcements (PSAs) are working to influence attention and emotion at a more automatic level. Processing food ads (either in an automatic way or in a controlled fashion) can be thought of as a dynamic interaction between mediated contents, the targeted audiences, and the environment. This study will offer some systematic knowledge about the dynamics of individual motivational, emotional, and cognitive responding toward food advertising containing different types of food cues. Identifying the patterns of affective and cognitive responding during exposure to food-related cues is meaningful and crucial to designing and producing more effective messaging. Considering how different types of cues could be used in food advertising to create more powerful food ads which better hold consumers’ attention and increase their buying intentions is one of the potential practical implications of this dissertation.

7 Further, the findings from this dissertation also offer suggestions for health professionals trying to create effective healthy eating interventions. In recent years, most healthy eating campaigns aim to educate people about the positive effects of self-monitoring, physical activity, and goal setting regarding weight management (Michie, Abraham, Whittington, McAteer, &

Gupta, 2009). However, for those people with less self-efficacy for healthy eating and less intention to be active, these current healthy eating intervention strategies are less effective

(Lappalainen, Saba, Holm, Mykkanen, Gibney, & Moles, 1997; Lupton, 1996). Creating obesity prevention PSAs that do not rely only on conscious motivation is another way to encourage people to better manage their health. In this case, practitioners creating public health media campaigns have to consider which contextual factors may be more effective and persuasive when developing mass media messages targeted at individuals with problematic eating behaviors and, perhaps just as importantly, which factors they should avoid including (i.e., which types of content may create counter-productive behavioral intentions).

For example, a study conducted by Bailey and her colleagues (under review) demonstrated the appetitive effects that social eating cues in obesity prevention PSAs have on viewers. Individuals showed increased pleasant feelings and more self-efficacy after exposure to social eating cues in the PSAs, but they reported less intention to actually eat in healthy ways.

Based on their findings, it may be important to omit social eating cues to avoid developing counter-productive obesity prevention messages. In line with this research, this dissertation was designed to deepen the understanding of how different types of food cues influence message processing and later food-selection behavior, and how these relationships may be moderated by individual and group differences. The findings provide some insights into obesity prevention message design and tailoring for those who are hoping for promoting healthy eating behavior

8 through mass and targeted communication as well as lay groundwork for potential food advertisement regulation.

CHAPTER TWO

REVIEW OF LITERATURE

“The goal of contemporary marketing is not simply to expose young people to ads, but rather to foster ongoing engagement – by encouraging them to interact with, befriend, and integrate brands into their personal identities and social worlds.”

- Montgomery & Chester, 2011, p.8

Obesity and The Power of Food Marketing

According to data released by the National Center for Health Statistics (2017), currently, more than 37.9% of adults aged 20 years or over and 17.2% of adolescents aged 12-19 are obese in the United States. Obesity is associated with multiple negative health consequences, such as poor quality of life, heart disease, and type II diabetes. As a major national health challenge, obesity is also a financial burden via increasing health care costs (Cawley & Meyerhoefer, 2012;

Finkelstein et al., 2009). In recent years, the estimated annual healthcare costs of obesity related illness ranged from $147 billion to nearly $210 billion every year in the U.S (The State of

Obesity, 2016). The costs will keep increasing if the obesity rates keep rising. In 2013, the

American Medical Association officially recognized obesity as a chronic progressive disease, which can be caused by multiple environmental and genetic factors. The National Institutes of

Health estimates that $12.5 billion was spent in the same year on treatment and prevention research of cardiovascular disease, type 2 diabetes, obesity, and hypertension (LoDolce, 2015).

However, obesity within the U.S. population is not only associated with lack of exercise and poor eating habits; it also results indirectly from excessive advertising and marketing of unhealthy and high energy density food products. Many food marketing professionals have realized the importance of advertising, which can be a powerful tool to persuade masses of

10 people to change their food choices. These marketers employ various persuasive strategies when creating food ads to attract their potential consumers, such as including food-related cues in the messages they created. In 2014, the food industry spent a total of $15 billion on all food, beverage, and restaurant marketing in the United States. Among all the food and beverage manufacturers, the fast food industry spent a significantly larger amount of money on advertising.

For example, McDonald’s spent $998 million in 2013 to maintain their extensive advertising, which is $2.7 million per day (Hume, 2014).

By most accounts, this advertising is working quite well. Current research has found that frequent media exposure is associated with a greater likelihood of poor eating habits (Davison,

Marshall, & Birch, 2006; Crespo, Smit, Troiano, Bartlett, Macera, & Andersen, 2001; Tucker &

Bagwell, 1991; Tucker & Friedman, 1989; Viner & Cole, 2005; Wiecha, Peterson, Ludwig, Kim,

& Gortmaker, 2006). For example, Wiecha et al. (2006) found that for each hour of television viewing a person will consume an extra 167 kcal per day, with an increase in the intake of the foods commonly advertised on television. Tucker and his colleagues (Tucker & Bagwell, 1991;

Tucker & Friedman, 1989) also found that time spent watching television was directly related to a greater likelihood of obesity. Specifically, males in the United States who spent over three hours watching television exhibited more than double the prevalence of obesity compared to those who watched television for less than three hours per day; while females in the United

States who watched television equal to or greater than four hours per day had more than twice the prevalence of obesity compared to those who spent less time in front of the television.

Food advertising and marketing seem to have even more influence when it comes to children’s food choices. Previous research suggests that children, teens, and young adults have become a main target audience in food advertising (Duffey, Gordon-Larsen, Jacobs, Williams, &

11 Popkin., 2007; Paeratakul, Ferdinand, Champagne, Ryan & Bray, 2003). Further, young adults and children have been categorized as a vulnerable population which can be easily influenced by food advertising and marketing (Borzekowski, & Robinson, 2001; Chester, & Montgomery,

2011; John, 1999; Lobstein, & Dibb, 2005; Livingstone & Hesper, 2006). As younger children have a limited understanding regarding the intent of food advertising, they are more likely to think advertised information is interesting, informative, and unbiased (Strasburger, 2001; John,

1999; Kunkel & Gantz, 1992). For example, Livingstone and Helsper (2006) examined the effect of food advertising on children by applying a dual process model of cognitive persuasion. They found that younger children with lower media literacy were more likely to be attentive to peripheral cues in food ads, such as the use of a celebrity spokesperson, jingles, colorful images, as well as other attractive physical features; while older children with greater media literacy tended to be persuaded by central cues in food ads, which offered more persuasive appeals such as arguments for why the food should be consumed. The influences of this advertising seem to result in lasting problematic eating behaviors as well. Crespo and his colleagues (2001) found that girls aged 8 to 16 years who watched 5 or more hours of television per day consumed an additional 172 kcal per day compared to the girls with less television viewing. Researchers also found that higher weekend television viewing in early childhood was associated with higher

BMIs in adulthood — each hour of weekend television viewing was associated with a 7% increase in risk of adult obesity (Viner & Cole, 2005).

As obesity and overeating has become a major public concern, a number of health professionals and researchers have advocated to limit televised food advertising to reduce its negative influences. For example, according to Sheridan (2016), “Without increased governmental regulation of food advertising and a shift in public views on all of this marketing,

12 the obesity epidemic will continue to expand.” However, the public health community’s battle against food advertising is currently without resolution, which may be due to the way health professionals and researchers are asking for limits. Most calls are for limiting food advertising overall (e.g., National Advertising Review Council, 2004; Center for Informed Food Choices,

2005), which amounts to commercial free speech infringement. However, more targeted and specific calls for limitations within food ads may be more valuable, if research can support their effectiveness.

One particular line of research in this area has called for the limitation of so-called direct food cues, or depictions of food that share certain perceptual characteristics with actual food (e.g., color, texture, etc.) in ads (Bailey, 2015, 2017; Bailey et al., 2016; Bailey & Muldrow, 2018).

This line of work argues that because these cues provide the directly perceivable affordance

(Gibson, 1986) of eating food, and food is a primary biological motivator, appetitive motivation and eating behavior are automatically potentiated when viewing these ads (Bailey, 2015, 2017;

Bailey et al., 2016, Bailey & Muldrow, 2018). From a biological perspective, humans are evolved and motivated to consume food for survival and hedonic/emotional well-being purposes

(Bradley et al., 2001; Hull, 1943). A host of empirical research has indicated that, indeed, viewing food ads can elicit appetitive motivational activation and increase the desire to eat

(Bailey, 2015, 2016; Bailey et al., 2016; Harris et al., 2009; P. J. Lang, et al, 1993). However, this work does not adequately address the nuances within advertising food cues.

Most food ads not only show food in its best light, they also associate the food with different biologically- and socially-relevant contexts. Therefore, it is important to understand the influences of different types of food-related cues on a range of responses including motivational, affective, cognitive, and behavioral. The different modes in which the cues are presented in food

13 ads may create different responses. For example, Bailey and her colleagues (under review) found that social eating cues (i.e., people eating as a group) in obesity prevention public service announcements (PSAs) elicited stronger positive emotional evaluations of the ads compared to those PSAs with a single person eating, but these ads also elicited less intention to eat in healthy ways. While this indicates that social cues paired with food cues can alter motivational and behavioral responses, there is very little research in this area. Further, there are no consistent definitions and categorizations for different types of food-related cues. This dissertation conceptualizes of three different types of food cues and investigates the influences of two of them (i.e., use cues and social cues) while holding the other constant across all messages (i.e., direct cues) on dynamic information processing and motivational, affective, cognitive, and behavioral responding utilizing the cue reactivity paradigm from addiction research as a guide.

The cue-reactivity paradigm, which has been widely applied in substance addiction research, may provide useful ways to consider automatic reactions toward conditioned and unconditioned food-related cues.

Cue Reactivity in Substance Addiction Research

The cue-reactivity paradigm, which was developed to measure individuals’ reactions when exposed to one particular cue or multiple types of cues, was first proposed to explain alcohol and drug addiction. This paradigm has been widely applied in the field of addiction research over the past several decades (Drummond, Tiffany, Glautier, & Remington, 1995). Cue- reactivity researchers generally use self-reported and psychophysiological measurements to test motivational, affective, and cognitive states during exposure to a wide-range of substance cues, such as drug-use situations, the presentation of substances, etc. Past research has identified that cue-reactivity responses include emotional responding (e.g., craving, anxiety, pleasure),

14 physiological responding (e.g., drug-like, withdrawal-like, appetitive), and behavioral changes

(e.g., food intake) (Drummond et al., 1995). For example, Rush first suggested that alcohol- related cues in the environment could activate appetitive drive to encourage problematic drinking behavior in 1789. He claimed that exposure to alcohol-related cues could lead to stronger craving tendency and increased risk of relapse, especially for those suffering from alcohol addiction.

This notion has continued to find support across different types of addiction research. For example, past research also demonstrated that exposure to smoking cues could activate the appetitive motivational system associated with increased smoking urges in former and current smokers (Robinson & Berridge, 1993; Tiffany, 1990).

This also seems to be the case for cues presented in mediated contexts, even those intending to discourage craving and use (Clayton, Leshner, Bolls, & Thorson, 2017; Clayton,

Leshner, Tomko, Trull, & Piasecki, 2017; Kang, Cappella, Strasser, & Lerman, 2009; Lee &

Cappella, 2013; Lee, Cappella, Lerman, & Strasser, 2011, 2013; Liu & Bailey, 2018; Sanders-

Jackson, Cappella, Linebarger, Piotrowski, O’Keeffe, Strasser, 2011). For example, Sanders-

Jackson et al. (2011) found viewers paid more motivationally biased attention (less variation in visual fixation) to the smoking cues when processing antismoking PSAs. Further, Clayton and his colleagues (Clayton, Leshner, Bolls, & Thorson, 2017; Clayton, Leshner, Tomko, Trull, &

Piasecki, 2017) suggests that health professionals should consider omitting smoking-related cues when creating anti-tobacco PSAs as they found smoking-related cues in PSAs could automatically elicit pleasant feelings and approach tendencies. In another study, Liu & Bailey

(2018) found that substance cues present in highly arousing smoking prevention fear appeal messages elicited greater attention to the message but poorer memory for the message contents.

15 This indicates that the effectiveness of substance abuse prevention PSAs may decreases when these messages contain cues for the substances they are trying to discourage.

Overall, the appetitive influence of substance cues has been supported across a host of studies. Carter and Tiffany (1999) meta-analyzed 41 studies and found that substance-related cues in both mediated and real contexts elicited significant increases in self-ratings of craving tendency and modest increases in physiological responding, such as decreases in skin temperature, and increases in heart rate and skin conductance. They also noted that these cues could automatically generate approach tendencies in smokers and even cause relapse in smokers attempting to remain abstinent. Thus, past research suggests that individuals, especially individuals with substance abuse and addiction history, tend to exhibit stronger reactions during the presence of substance-related cues (Niaura, Rohsenow, Binkoff, Monti, Pedraza, & Abrams,

1988; Payne, Schare, Levis, & Colletti, 1991; Robbins & Ehrman, 1992; Rohsenow, Childress,

Monti, Niaura, & Abrams, 1991). However, these responses have many likely moderators, including cue availability, types of substance cues, and individual differences.

Cue availability is the actual presence of the substance: the substance is physically there and available for use. Carter and Tiffany (2001) showed that cue reactions have ups-and-downs in response to manipulations of cue availability. They found smokers exhibited stronger self- ratings of craving tendency and physiological responses as the possibility of cigarette availability increased. Thus, investigating perceptions of cue availability before assessing cue reactions may be necessary to understand the nuances in these responses.

Further, and of central importance to the work reported here, the type of cue has been identified as important in accessing cue responses. Drummond (2000) suggests that cues can be generally categorized into four types based on the modes of cue presentation: 1. exteroceptive

16 (e.g., visual, auditory, olfactory cues); 2. interoceptive (e.g., mood, dream, cognition, craving); 3. temporal (e.g., time of day); and 4. chained relationships (e.g., the interaction of drug-related cues and the environment can influence the motivational salience of drug-related cues). Among these types of cues, exteroceptive cues, which present the perceptual characteristics of the objects (e.g., visual, olfactory cues), are the most studied type in cue-reactivity research (e.g.,

Carter & Tiffany, 2001; Schacht, Anton, & Myrick, 2013; Stewart, deWit, & Eikelboom, 1984), especially as they concern conditioned compensatory reactions (Craft, Kathleen, & Lustyk,

2013).

Craft et al. (2013) conceptualized exteroceptive cues as those that are observable and external to the organism and cause conditioned compensatory reactions. For example, if a person generally takes over-the-counter pain relief medication whenever he or she feels a headache, a cue that signals “I can take the drug to relieve the pain,” can actually make the person more sensitive to the pain caused by headache. Because the person knows a pain suppressant will soon be taken, the body becomes more sensitive, anticipating that “the drug will soon take care of it.”

Such conditioned compensatory reactions in turn decrease the impact of the drug on the body due to the pain sensitization.

These compensatory reactions help strengthen addiction: more substance is needed to overcome this response each time. Therefore, the conditioned incentive effect (dopamine release) of ingesting a substance plays a central role to initiate and maintain drug-seeking behavior

(Stewart et al., 1984). As human beings are evolved to search for appetitive stimuli in the environment, drug-seeking behavior is difficult to quit because it is a reward-driven behavior reinforced via this response. This compensatory response is likely even more problematic in the creation of food addiction, as food is required for biological success.

17 Research has indicated that food craving causes enhanced release of striatal dopamine, which is the neurotransmitter associated with appetitive motivation and positive reinforcement in much the same way as substance craving (Kelley & Berridge, 2002). Food addiction is increasing in prevalence, arguably because more highly energy dense foods which are

“hyperpalatable” are available with little effort and/or cost to obtain (Meule, 2011). The presence of food stimuli in general triggers not only regular food intake but also overeating (Jansen, 1998).

Food craving is likely to be harder to control and avoid compared to substance addiction because of the easy access of food resources in the environment. High energy dense foods (i.e. high fat and high sugar foods) such as fast foods are the foods most typically associated with reports of food craving and food addiction (Chao, Grilo, White, & Sinha, 2014). Thus, the responses to food stimuli in the environments may not create only craving for unhealthy foods (driven by biolotical needs of energy conservation), but may also create circumstances that strengthen food addiction.

Cue-Reactivity in Food Research

While the cue-reactivity perspective has been widely applied to study addictive behaviors in illicit substance use, relatively fewer research studies have applied it to examine the affective and psychophysiological processing of food-related cues, especially in the context of mediated food messaging (Bailey, 2015, 2016; Bailey et al., 2016; Harris et al., 2009; P. Lang et al., 1993;

Lawrence et al., 2012; Mackillop, Miranda, Monti, Ray, Murphy, Robsenow, et al., 2010; Nijs,

Franken, & Muris, 2008; Papachristou Nederkoorn, Havermans, van der Horst, & Jansen, 2012;

Pelchat, Johnson, Chan, Valdez, & Ragland, 2004; Rohsenow et al., 1991; Sobik et al., 2005;

Tiffany, Cox, & Elash, 2000). It is commonly assumed that food addiction is less serious than substance addiction — food addiction is legal, after all. But, in fact, food addiction shares many

18 characteristics with substance abuse behaviors, such as diminished control and continued use despite negative outcomes (Gearhardt, White, & Potenza, 2011). In alignment with the cue- reactivity literature in the area of substance addiction, current research has found that overeating can be cue-controlled as well (Jansen, 1998). Therefore, the cue-reactivity paradigm is also applicable to food addiction.

Empirical findings have demonstrated that when confronted with food-related pictures, more cognitive resources will be allocated to motivationally salient food-related information and, thereby, elicit the motivation to eat, or even overeat. (e.g., Lawrence et al., 2012; Nijs et al., 2008;

Pelchat et al., 2004; Sobik et al., 2005). For example, Sobik, Hutschison, and Craighead (2005) found that exposure to food cues could elicit stronger craving tendency for food. They presented actual foods to their participants and those participants were told to only focus on the foods but not taste them. Cue-elicited craving for food corresponded with greater activation and neuronal release of brain dopamine, which is the neurotransmitter associated with pleasant feelings. In another study conducted by Lawrence et al. (2012), researchers observed pronounced food-cue reactivity in the nucleus accumbens, which is part of key brain regions associated with pleasure and reward, during exposure to food images. They confirmed that food cue reactivity could be stimulated when processing mediated food-related messages as well. Other related work has generally indicated that food-relevant cues in both real and mediated environments can elicit stronger motivational responses similar to those found in drug craving (Mackillop et al., 2010;

Nijs et al., 2008; Papachristou et al., 2012; Pelchat et al., 2004; Rohsenow et al., 1991; Sobik et al., 2005; Tiffany et al., 2000). But, less is clear about how and what types of cues are functioning in mediated food advertising.

19 A number of researchers have content analyzed television food ads and found that food marketing professionals commonly use certain types of cues as persuasive appeals in food ads, such as hedonic appeals (e.g., fun, humor, pleasant), palatability appeals (e.g., taste, flavor, texture, appearance), social appeals (e.g., product in social context), celebrity endorsement appeals, practicality appeals (e.g., product performance, price), and sexual appeals (Appelbaum

& Halliburton, 1993; Buijzen, 2003; Buijzen & Valkenburg, 2002; Cheong, Kim, & Zheng, 2010;

Kunkel, & Gantz, 1992; Roberts & Pettigrew, 2007; Stitt, & Kunkel, 2008). Buijzen (2003) conceptualized advertising appeals as a specific strategy chosen by food marketers to relate to certain tastes and preferences of their target audience, which mirrors the idea of conditioned stimuli, or cue.

Buijzen and Valkenburg (2002) identified there were over 75 appeals or cues which had been recognized and used by food advertising and marketing, and among these appeals, in general, humor, product quality and newness of the product, pleasant taste, fun, play, action- adventure, having the best, saving money, physical attractiveness, seizing opportunities, and convenience were used most often in televised commercials. In particular, they also found that advertising aimed at adults were more likely to use particular types of appeals, including convenience, financial security, health, tidiness, love, physical attractiveness, etc.; while commercials for children and teenagers were significantly more likely to use play, action- adventure, fun, being modern, being cool, etc. Kunkel and Gantz (1992) conducted similar studies to examine which types of appeals had been applied in food ads, especially the food ads aimed at children. Based on their findings, the most prevalent appeals included fun/happiness, taste/flavor/smell, product performance, and product in social context. Researchers also suggested that the use of appeals in advertising could also be affected by cultural factors

20 (Appelbaum & Halliburton, 1993; Cheong et al., 2010). Based on their results, certain types of appeals including individualism appeals, food tastes, texture, product performance appeals, products in social contexts appeals, nutrition and health appeals, and sexual appeals were more favored by some groups than others. In sum, though much research has been done to examine the use of different types of appeals and cues in advertising, there are no consistent definitions and categorizations for different types of cues, and even less is understood regarding their effects and influences.

In coalition with this previous research, this dissertation conceptualizes three types of cues using a Gibsonian affordance perspective (1986), which focuses on an ecological view of what are perceived as possible behaviors resulting from exposure to the stimulus. Gibson (1986) proposed the theory of affordance to indicate the inherent interactionist relationship between the environment and how animate objects perceive the world and behave. In Gibson’s The

Ecological Approach to Visual Perception, he posited that as the environment presents opportunities and threats, organisms tend to adapt to and alter their environments and the stimuli within them in order to make affordances better suit their needs. In other words, organisms dynamically perceive and interact with the substances, surfaces, objects, and other persons and animals in the environment in terms of what their current needs and current perceptions of those environmental stimuli indicate may be possible. As Gibson (1986, p. 119) proposed, “The affordances of the environment are what it offers the animal, what it provides or furnishes, either for good or ill.” Gibson and others further noted that the perception of an affordance occurs in both real and mediated environments (Gibson, 1986; Reeves & Nass, 1996). This has been supported across a several studies (e.g., Bailey, 2015; A. Lang, Bailey, & Connolly, 2015; A.

Lang, Sanders-Jackson, Wang, & Rubenking, 2013; A. Lang, Park, Sanders-Jackson, Wilson,

21 Wang, 2007; A. Lang, Yegiyan, 2008; Wang, Lang, & Busemeyer, 2011). Thus, there is converging evidence indicating that individuals perceive and respond to environmental cues and contexts as though they are real and as though they offer both opportunities and threats. This is particularly the case when the opportunities and threats are of primary biological relevance (A.

Lang et al., 2015).

With this in mind, this dissertation identified one type of food cue from previous literature and conceptualized two more which are common in advertising. Each of these cue types offers different affordances surrounding eating behavior:

Direct food cues are the visual representations of the actual food products, and this type of cue shares the perceptual properties of the actual food products, including their actual appearance in terms of shape, color, texture, and other surface properties, which would afford the possibility of readily available food consumption (Bailey, 2015; 2017). Overall, direct food cues in mediated messages help to present the hedonic and palatability features of food products, just as exteroceptive cues as defined in the substance cue reactivity literature (Drummond, 2000).

Bailey (2015) conceptualized this type of advertising food cue and found that the presence of direct food cues in televised food ads generally elicits more pronounced appetitive physiological response (increased orbicularis oculi activation) and increased favorable self-reported attitudes toward those food products. In another study conducted by Lawrence et al. (2012), the presence of food images (containing direct food cues) triggered elevated activity in the nucleus accumbens, which is associated with pleasure and reward, and increased subsequent snack consumption.

Social cues in food advertising depict activities of individuals or social groups (more than

2 people). The presence of these people affords social support to encourage viewers to consume food. Past research has confirmed that the influences of social support in terms of providing

22 encouragement, building connection, providing accountability, and modeling a target behavior in real environments on subsequent food consumption behavior (e.g., Cullen, Baranowski,

Rittenberry, & Olvera, 2000; Limbert, 2010; Sallis, Grossman, Pinski, Patterson, & Nader, 1987;

Wonderlich-Tierney & Vander Wal, 2010). For example, the presence of social cues in food advertising may encourage viewers to eat the advertised food products as the actor(s) — either one individual or a social group — in food advertising emphasize how tasty/healthy the foods are. It is important to note that the presence of people does not necessarily mean they will eat the foods, but merely that they are endorsing the behavior. This endorsement by presence is expected to generally increase appetitive activation.

Use cues are actual food consumption behavior in food advertising. In other words, the presence of a use cue in food advertising means that someone in the ad is actually eating the advertised food products, which directly presents the affordance of eating. Some evidence supports that people eat differently when they eat with other people compared to eating alone

(Clendenen, Herman, & Polivy, 1994; De Castro, 1990, 1991; De Castro et al., 1990; Herman,

2015, 2017; Higgs & Thomas, 2016; Klesges et al., 1984). For example, researchers have found that the presence of more people in the eating context increases positive affect and that this effect is independent of the direct effect of group size on food intake, which increases as well (De

Castro, 1990). Further, the influence of use cues in mediated messages has been confirmed as well. Bailey and her colleagues (under review) found that individuals reported greater positive emotional responses and less intentions to eat in healthy ways when presented with messages that contained more individuals eating unhealthy foods. Therefore, it seems more eating in a message should create greater appetitive response in general.

23 In this dissertation, direct food cues are presented in all of the food ads, meaning the presence of direct food cues in the food ads is not being manipulated. Instead, this dissertation adds more nuance to the definition of a direct cue by paying particular attention to how the direct cue is portrayed in terms of its use: being eaten or not being eaten by individuals or by groups.

Further, the influence of social support in eating and no eating contexts is also investigated by examining the influence of number of individuals depicted (social cues).

As discussed above, the presence of others, especially multiple and influential others, provides social support for performing different behaviors and/or holding certain attitudes, including those regarding food. Further, the presence of others eating tends to increase appetitive reactions and consumptions behaviors. However, the fact that a person (or other animal) must be present in order for a use cue to be presented means that these two food cue types are often conflated. For example, de Castro (1990, 1991) examined eating behavior of individuals when lesser or fewer others were also present and eating. Bailey, Liu & Wang (under review), also found that more people vs. fewer people present and eating in obesity prevention ads created greater intentions to eat unhealthy foods. However, social cues and use cues likely have independent and interactive effects on appetitive responses and behaviors.

Firstly, the presence of social cues in mediated messages may provide more and greater social endorsements for different types of behaviors, including eating behaviors. Past research has examined the influences of social support on encouraging unhealthy eating patterns (Avena

& Gold, 2011; Berge, Wall, Larson, Eisenberg, Loth, & Neumark-Sztainer, 2014; Crandall, 1988;

Cohen, 2008; Field, Camargo, Taylor, Berkey, Roberts, & Colditz, 2001; Fletcher, Bonell, &

Sorhaindo, 2011; Harris et al., 2009; Liberman, Gauvin, Bukowski, & White, 2001; Rosin, 2008;

Simons-Morton, Haynie, Crump, Eitel, & Saylor, 2001). These studies confirm the influences of

24 close knit social groups like sororities (Crandall, 1988), friend groups (Fletcher et al., 2011), and parents (Berge et al., 2014) on eating behaviors ranging from overeating and binge eating to excessive dieting. These social influences likely function not only through unintentional viewing of social modeling (Bandura, 1977; 2001) but also through the purposeful acquisition of social status by behaving like aspirational others. For example, Field et al. (2001) found that children and adolescents were more likely to be highly concerned with desirable bodyweight if their mothers were frequently on diets. By contrast, Crandall (1988) found that sorority members’ binge eating patterns were intensified as group pressures to conform increased in strength. This means social cues in food advertisements, by their sheer ability to create positive behavioral models, are likely to influence attitudes toward and intentions to eat different types of advertised foods.

Secondly, the addition of use cues to these social cues may create stronger social endorsements while also eliciting greater appetitive motivational activation. For example, watching a video advertisement of multiple people eating a food product in a positive social context may provide better information about the affordances available: viewers get more detailed information (e.g., smell, taste) about the affordances that the advertised food provides via the actors’ reactions and they also get more information about the social affordances (e.g., belonging, fun, friendship) that may be afforded them in the situation. Thus, it is likely that concurrently presented social and use cues information in food ads may elicit greater appetitive motivational responses toward advertised food products, compared to social cue only cues (just providing social affordances) and use cue only conditioning (just providing eating affordances).

Overall, this dissertation categorizes food cues in a more nuanced way than previous research in this line and investigates the influences of use and social cues on motivational,

25 affective, cognitive, and behavioral responding from a cue reactivity perspective. In order to best ground predictions regarding the influences of these cues, theories of motivated cognition and dimensional emotion, and psychophysiological assessment of these constructs are used.

Assessing Dynamic Affective and Cognitive Processing

Overall, this dissertation adopts a situated embodied dynamic perspective (Beer, 2014), which argues that an organism is dynamically interacting with its environment (including advertising) to maximize its fitness (i.e., survival and reproductive success). According to Beer

(2014), “behavior is a property of the entire coupled brain-body-environment system, and cannot in general be properly attributed to any one subsystem in isolation from the others.” From this perspective, brains, bodies, and their environments are coupled dynamical systems; behaviors are the outcomes generated by the flow from the agent to the environment, while the flow from the environment to the agent leads to the generation of perception. Based on this situated embodied dynamic perspective, a dual independent systems model of biological motivation is used as the theoretical guide regarding the creation of behavior within the environment. Cacioppo and his colleagues advanced the bivariate evaluative plane (BEP) model which posits that emotional and cognitive processing are a function of underlying activation in two independent motivational drive systems (Cacioppo & Berntson, 1994; Cacioppo & Berntson, 1999; Cacioppo & Gardner,

1999; Cacioppo, Gardner, & Berntson, 1997). The BEP model is a dual-systems model that explains that exposure to environmental stimuli relevant to biological imperatives will automatically activate two independent motivational drive systems — the appetitive and the aversive motivational systems. The activation of the appetitive system potentiates approach for opportunities available in the environment, especially the most biologically relevant, such as

26 tasty foods and opportunities for reproductive success. Activation of the aversive system predisposes us to avoid threats, such as attack, disease, noxious objects, etc.

Though the two motivational systems are independent, they can be activated at the same time. Cacioppo and his colleagues suggest there are multiple modes of activation in the appetitive and aversive systems: 1. Reciprocal activation, which refers to increased activation in one system and decreased activation in the other; 2. Coactivation – which means both systems are activated; and 3. Uncoupled activation, which refers to activation in one system being unrelated to activation in the other (Cacioppo, Gardner, & Berntson, 1997; 1999). Overall, it has been generally supported that the activation of the appetitive and aversive systems creates changes in emotional and cognitive responses and later behavioral intentions (Bradley et al.,

2001; Lawrence et al., 2012; Lubman, Peters, Mogg, Bradley, & Deakin, 2000).

Past research using a dual-systems motivational framework has shown that the presence of food related stimuli triggers stronger appetitive motivational activation resulting in more positive and arousal emotional feelings, greater cognitive efforts, along with stronger approach behavior tendencies (e.g., Bailey, 2015, 2016, 2017; Bailey et al., 2016; Bailey & Muldrow,

2018; Larsen et al., 2012; Lawrence et al., 2012; Sobik, Hutchison, & Craighead, 2005). Based on prior knowledge, this dissertation aims to examine the effects of two specific types of food related cues (i.e. use and social cues) in food ads on food cue reactivity associated with appetitive motivational responses.

Emotional Responses to Motivational Activation

In terms of emotion, it is assumed that the activation in these motivational systems is the precursor to dimensional emotional states (Bradley et al., 2001). Stimuli that activate the appetitive motivational system lead to positive emotions, whereas stimuli that activate the

27 aversive system lead to negative emotions. Empirical evidence supports this contention (e.g.,

Bradley et al., 2001; Greenwald, Cook, & Lang, 1989; P. Lang et al., 1993; Bolls, Lang, & Potter,

2001). For example, Bradley et al. (2001) asked participants to view seventy-two pictures chosen from the International Picture System (IAPS; P. Lang, Bradley, & Cuthbert, 1999). Self-reported emotional feelings and physiological responses indicative of particular types of emotions were recorded for three types of normed emotional pictures (i.e., positive, negative, neutral). The results of this study demonstrated that individuals exhibited increased skin conductance, more pronounced cardiac deceleration and potentiated startle reflex potentiation during exposure to the pictures depicting threat, violent death, and erotica. They also found that viewers exhibited increased zygomaticus major and orbicularis oculi EMG activity when viewing pleasant pictures

(e.g., food pictures) while increased corrugator supercilii EMG activity were observed during exposure to unpleasant pictures (e.g., body mutilations pictures). Similar results have been obtained during exposure to valenced audio messages (Bolls, Lang, & Potter, 2001). A. Lang and her colleagues (1999) also found that higher skin conductance level, an indicator of emotional arousal, was elicited during exposure to fast pace content and more arousing content. Overall, past findings have also shown that facial electromyography and skin conductance are significantly correlated with self-reported emotional feelings along the two dimensions of valence and arousal (Greenwald et al., 1989; P. Lang et al., 1993). Following this line of research, this dissertation adopted a dimensional emotion perspective and employed both psychophysiological (including skin conductance, and facial electromyography: zygomaticus major and orbicularis oculi) and self-report measures to examine emotional and motivational responses when processing the food ads with different types of cues.

28 As discussed above, it is expected that ads which contain use cues will create increased appetitive motivational activation. Thus, it was predicted that these cues would result in increases in psychophysiological and self-reported indices of appetitive motivational activation and positive emotion. Further, it was expected that greater social support in food ads would create increased appetitive motivational activation, also resulting in greater psychophysiological and self-reported indices of appetitive motivational activation and positive emotion. If social and use cues are presented concurrently (i.e., multiple individuals are eating in an advertisement) this presents multiple affordances: both the affordance of eating and the affordance of sociality and potential belonging, which likely creates an additive appetitive or positive response. Thus, it is possible that the addition of use to group context provides a multiply-appetitive context that produces more positive and aroused emotional feelings compared to group cue only messages, individual cue only messages, and individual and use cues messages. Thus:

H1a: Food ads containing use cues will elicit stronger appetitive activation resulting in pronounced changes in EMG (higher OO and lower CR), greater SCL, and a positive self- reported emotional response (increased positivity and arousal ratings, decreased negativity ratings) compared to those ads without use cues.

H1b: Food ads containing group cues will elicit stronger appetitive activation resulting in pronounced changes in EMG (higher OO and lower CR), and SCL (higher SCL) than those with individual cues.

H1c: Food ads containing use and group cues will elicit the greatest appetitive activation resulting in pronounced changes in EMG (higher OO and lower CR), and SCL (higher SCL) compared to other types of food ads.

29 H2a: Food ads containing use cues will facilitate more positive and intense and less negative self-reported emotional feelings toward the given food products than those without use cues.

H2b: Food ads containing group cues will facilitate more positive and intense and less negative self-reported emotional feelings toward the given food products than those without individual cues.

H2c: Processing food ads containing both group and use cues will elicit more positive and intense and less negative self-reported emotional feelings toward the given food products than those without individual cues.

Cognitive Responses to Motivational Activation

Lang used the BEP model (Cacioppo & Berntson, 1994; Cacioppo & Berntson, 1999;

Cacioppo & Gardner, 1999; Cacioppo, et al., 1997) to propose the Limited Capacity Model of

Motivated Mediated Message Processing (LC4MP; A. Lang, 2006a, 2006b, 2009) to further elaborate expected influences of motivational activation on cognition. The LC4MP assumes that human beings (as information processors) only have a limited pool of cognitive resources, and that the structure and content within mediated messages will influence how this limited pool of resources is used to adaptively process information during exposure because these structure and content elements have motivational relevance. Further, the model proposes that individuals allocate cognitive resources in two ways: controlled and automatic. Controlled resource allocation is believed to occur when people intend to find or remember certain things from mediated messages. People will allocate more cognitive resources when they have goals.

Conversely, automatic resource allocation occurs for two reasons: 1. Individuals are motivated to pay attention to primary (e.g., food, sexual information, threats) and secondary (e.g., positive and

30 negative emotional information) motivators and structural features (e.g., novel and signal features like sound effects and camera changes) in mediated messages (A. Lang, 2000).

Research across the last two decades has indicated that cognitive resource allocation generally follows particular patterns based on the valence and arousal of the stimuli being processed (e.g., Lang, 2006; Lang, Bolls, Potter, & Kawahara, 1999; A. Lang et al., 2007; A.

Lang, Shin, & Lee, 2005). At low levels of emotional arousal, encountering positive, motivationally relevant stimuli will create greater appetitive activation leading to greater attentional resource allocation. As arousal increases, more attentional resources will be allocated to encoding and storing the positive stimulus and more positive feelings are elicited at a fairly gradual rate (A. Lang, 2006). When negative information is encountered, on the other hand, the aversive system activates more rapidly and strongly to aid quick response. As the intensity of activation increases, organisms expend more cognitive resources to identify and categorize the threat. However, at high levels of negativity, the primary goal of the organism is protection. At this level, cognitive resources will be automatically shifted away from encoding (i.e. figuring out what the threat is) to actually dealing with the threat itself. Thus, cognitive resources can be allocated from our limited capacity resource pools to processing information in various ways, but the amount and type of resources can be fairly well predicted based on the circumstances in the environment.

A. Lang and Basil (1998) suggest that there are four primary pieces in this limited capacity cognitive resource pool: resources allocated, resources required, resources remaining, and resources available. When people are motivated to process certain mediated messages, they allocate attention to the cognitive tasks in both automatic and controlled ways, as discussed above. When the cognitive task is simple to process, or if people only allocate a certain amount

31 of attention to a task, people may have resources remaining in the total resource pool. However, resources allocated are rarely equivalent to resources required to process the messages.

“Available resources” refers to the difference between resources allocated and resources required and can be negative, or too few to process information adequately, or positive, more than enough to process the information adequately. Secondary Task Reaction Time (STRT) has been used to index these available resources in coalition with recognition metrics, which are used to index adequacy of processing. When using STRT measurement, participants are told to pay attention to the primary task (media viewing) and respond to the STRT probes as quickly as possible each time they hear or see a secondary task stimulus (usually a beep sound or a flash of light).

In general, past research has found that available resources decrease when messages are arousing, fast-paced, and contain more complex, complicated information (Fox, Park & Lang,

2007; A. Lang, Bradley, Park, Shin & Chung, 2006; A. Lang, et al., 2007) as well as contexts where more arousing, primary biological motivators and hedonic stimuli are present (Leshner &

Cheng, 2009; Liu & Bailey, 2018). Following this previous research, it is predicted that food ads which contain group cues (a group present in the ads) or use cues (eating present) will elicit greater appetitive motivational activation, and as such, will leave fewer available resources for processing.

Though LC4MP predicts individuals allocate more attentional resources to biologically relevant information that are considered primary motivators or reinforcers, limited research has examined the interaction effect of concurrently presented appetitive cues (social and use cues in particular) on cognitive resource allocation. However, based on the predictions of LC4MP (A.

Lang, 2006a, 2006b, 2009), the addition of use cues to two different social contexts (individual vs. group) are assumed to cause varied motivational and cognitive responses. Building upon this

32 previous knowledge, this dissertation also predicted that, compared to food ads with group cue only, food ads with individual cue only, and food ads with individual and use cues, the concurrent processing of use and group cues will trigger greater appetitive motivational activation, and as such, will leave fewer available resources for processing. Therefore:

H3a: Individuals will exhibit slower STRTs (fewer available resources) for food ads containing use cues compared to those without.

H3b: Individuals will exhibit slower STRTs (fewer available resources) for food ads containing group cues compared to those with individual cues.

H3c: Individuals will exhibit the slowest STRTs (fewer available resources) for food ads containing use and group cues compared to other types of food ads.

Though fewer available resources are likely to be present, adequacy of processing may remain high. Recognition metrics using signal detection analysis methods are generally used to index adequacy of processing (Fox, 2004; Shapiro, 1994). Within the paradigm of signal detection methods, encoding performance can be evaluated using two measures – memory sensitivity (assessing recognition memory strength) and criterion bias (assessing biases in recognition memory judgment). These metrics are calculated using hit rate (rate of items correctly identified as previously viewed) and false alarm rate (rate of items incorrectly identified as previously viewed) in order to index whether a specific answering strategy is being used by a participant. For example, if a participant identifies all items, both targets and foils, in a recognition task as items he or she saw before, he or she would be perfectly accurate if one only takes into account the hit rate. Thus, accuracy and signal detection metrics should be used together to assess adequacy of processing.

33 The combination of these metrics with STRTs indexing available resources give researchers a better understanding of the cognitive processes occurring during viewing of a message. Typically, signal detection metrics are used in coalition with STRTs to examine whether cognitive overload has occurred (Fox et al., 2007; Lang et al., 2007; Liu & Bailey,

2018). If STRTs are fast, the task could be simple, or the task could be so difficult that available resources are negative. Examining memory sensitivity generally bears this distinction out.

Cognitive overload is, therefore, indicated by fast STRTs, poor memory sensitivity, and negative criterion bias (Fox et al., 2007; Liu & Bailey, 2018). In contrast, higher memory sensitivity and more liberal criterion bias with relatively fast STRTs indicate the cognitive task is simple and individuals still have a lot of available resources (Fox et al., 2007; Liu & Bailey, 2018).

As discussed above, in the case of purely positive food advertisements, the addition of social cues and use cues are likely to create additive calls for cognitive resources and increases in appetitive activation. Based on LC4MP predicted patterns of cognitive resource allocation, this should create greater encoding and storage. However, previous research has indicated that when substance cues were present in messages, visual encoding decreased, regardless of arousing content level of the messages, which generally plays an important role in how encoding resources are allocated (Liu & Bailey, 2018). One possible reason for the poor encoding performance is that viewers may have stayed in perceptual contact with information in those

PSAs containing substance cues rather than encoding it. Within the framework of Dynamic

Human-Centered Communication Systems Theory (DHCCST) (A. Lang, 2014), viewers are likely to attend to certain motivationally relevant information, but not encode it, when they feel this information is relatively stable in the environment. This has been supported in previous work

(A. Lang & Bailey, 2015). Therefore, based on these limited previous data, it was predicted that

34 individuals would not achieve cognitive overload when faced with both highly social cues and use cues, but would not adequately process the information enough to facilitate visual encoding in the ads either. In this case, individuals should exhibit few available resources, as discussed above, but should exhibit poor encoding and more conservative criterion bias as previous research has identified that as messages create circumstances of scarcer available resources, individuals should exhibit more conservative criterion bias up to the point of overload (Fox et al.,

2007).

H4a: Individuals will exhibit poorer encoding performance (less visual and audio recognition accuracy, less sensitivity and more conservative criterion bias) for the food ads with use cues compared to those without.

H4b: Individuals will exhibit poorer encoding performance (less visual and audio recognition accuracy, less sensitivity and more conservative criterion bias) for the food ads with group cues compared to those with individual cues.

H4c: Individuals will exhibit the poorest encoding performance (less visual and audio recognition accuracy, less sensitivity and a more conservative criterion bias) for the food ads with both use and group cues compared to other types of food ads.

Overall, the combination of STRTs and recognition procedures should tell researchers much about the encoding of the information in mediated messages. However, as previous research suggests, encoding cannot always predict storage (A. Lang, et al., 1999) of information for later use. That is, stimuli that are encoded may be not be remembered later. Storage is conceived of as a process in which new and old information are linked in semantic and episodic memory networks. The newer information is linked with the older information, if any exists. If older information does exist there is greater likelihood the new information can be stored (A.

35 Lang, 2000), memory proactive and retroactive interference notwithstanding. Cued recall tests index how much information has been stored during a learning context, including media viewing.

In cued recall tests, participants receive a unique cue to help them find the appropriate memory trace and recall that particular information in a process known as retrieval (A., Lang, 2000).

As discussed above, in this dissertation, it was predicted that the use and group cues in food ads may elicit slower STRTs but poor encoding performance compared to other types of food ads. Thus, it was predicted there would be fewer available resources at encoding for the information in those food ads. Considering the encoding performance, the presence of use and social cues may also cause poor storage performance. Therefore:

H5a: Individuals will exhibit poorer storage performance (decreased cued recall for direct food cues, and social cues) for the food ads with use cues compared to those without.

H5b: Individuals will exhibit poorer storage performance (decreased cued recall for direct food cues, use cues, and social cues) for the food ads with group cues compared to those with individual cues.

H5c: Individuals will exhibit the poorest storage performance (decreased cued recall for direct food cues, use cues, and social cues) for the food ads with both use and group cues compared to other types of food cues.

Dynamic resource allocation to processing the messages will also be indexed via heart rate changes. Within the framework of a dual motivational systems model, the activation of appetitive motivational system has been found to drive more attention for information intake (P.

Lang, Bradley, & Cuthbert, 1997). Overall, heart rate change has been used as a reliable index for attention across many media studies (e.g., Abrams, Monti, Carey, Pinto, & Jacobus, 1988;

Bailey, Wang, & Kaiser, 2018; Bolls et al., 2001; Clayton, Leshner, Tomko, Trull, & Piasecki,

36 2017). In particular, the deceleration of heart rate has been generally used as an indicator of more attention being paid to external stimuli (e.g., exteroceptive cues) during information processing, while acceleration can mean more sympathetic arousal, more resources allocated to internal processing (e.g., imagery task), or stimulus rejection (i.e., deliberate gating out of incoming information, generally because it is negative and/or distressing) (A. Lang, 1994). Following this line of research, it was predicted that the presence of appetitive cues in mediated messages would elicit greater motivated attention (i.e. heart rate deceleration) (e.g., Bailey et al., 2018; Kang et al., 2009; Sanders-Jackson et al., 2011). That is, viewers were expected to pay more attention to the food ads containing use cues and more social cues (i.e., group cues). Furthermore, when use and group cues were concurrently present in food ads, it was expected that this influence would be additive, yielding even greater heart rate deceleration.

H6a: Food ads containing use cues will elicit stronger appetitive activation resulting in pronounced heart rate deceleration compared to those without use cues.

H6b: Food ads containing group cues will elicit stronger appetitive activation resulting in pronounced heart rate deceleration compared to those without individual cues.

H6c: Food ads containing use and group cues will elicit the greatest appetitive activation resulting in pronounced heart rate deceleration compared to other types of food ads

Behavioral Responses to Motivational Activation

Previous cue-reactivity research has supported the positive effects of food-related cues at large in food ads on motivation, information processing, and subsequent behaviors (e.g., Bailey,

2016, 2017). Individuals were more likely to indicate a behavioral intention to eat the food advertised when the ad contained appetitive food-related cues as these cues activate the motivation to eat. Thus, food advertisements containing different types of appetitive cues (i.e.,

37 social and use cues) may function through their ability to elicit stronger activation resulting in elevated purchase intention, perceived social support, and attitude toward the given food products, and less perceived behavioral control to consume. Thus,

H7a: Food ads containing use cues will create elevated purchase intention, perceived social support, and attitude toward the given food products, and less perceived behavioral control to consume compared to those without use cues.

H7b: Food ads containing group cues will create elevated purchase intention, perceived social support, and attitude toward the given food products, and less perceived behavioral control to consume compared to those without individual cues.

H7c: Food ads containing use and group cues will create the most elevated purchase intention, perceived social support, and attitude toward the given food products, and less perceived behavioral control to consume compared to other types of food ads.

Individual Differences and Motivational Activation

Certain individual differences may predispose different types of motivational activation responses, yielding different forms of food cue reactivity (i.e. emotional, cognitive, and behavioral responses). Empirical studies have demonstrated that impulsive behavioral tendency is positively associated with increased food intake, obesity, eating disorders, and elevated food cue reactivity (Claes, Vandereycken, & Vertommen, 2005; Fischer & Smith, 2008; Hou, Mogg,

Bradley, Moss-Morris, Peveler, & Roefs, 2011; Nasser, Gluck, & Geliebter, 2004; Strimas,

Davis, Patte, Curtis, Reid, & McCool, 2008; Tetley, Brunstrom, & Griffiths, 2010). For example,

Claes, Vandereycken, and Vertommen (2005) conducted a study to examine the relationships between eating disorders and impulsive behavioral tendency based on the UPPS Impulsive

Behavior Model (White & Lynam, 2001). Based on their results, patients with bulimia nervosa

38 were found to show more urgency and sensation seeking, and less premeditation and perseverance compared to the patients with restrictive anorexia nervosa. Other research showed that impulsivity also had an effect on individuals without eating disorders (Hou et al., 2011;

Strimas, Davis, Patte, Curtis, Reid, & McCool, 2008). In particular, individuals high in impulsive behavioral tendencies exhibited attentional bias when viewing food pictures with exteroceptive or direct food cues (Hou et al., 2011); further, greater impulsivity was found to be related to higher external eating tendency, or the tendency to be attracted to exterocecptive or direct food cues (Strimas et al., 2008). According to Fisher and Smith (2008), higher impulsivity (urgency) creates vulnerability to different types of addictive behaviors including binge eating and problem drinking. If impulsivity is a key factor in driving behavior when individuals are exposed to certain types of cues, investigating how this individual difference factor influences responses to social and use cues is important to fully understand their impacts. Therefore, following this line of research, this dissertation also examined if the main and interaction effects of social and use cues on food cue reactivity in terms of motivational, emotional, and behavioral reactions vary after controlling for impulsive behavioral tendency. Thus, the following research question was developed:

Thus, the following research question was developed:

Research Question 1: When accounting for impulsive behavioral tendency, how do use cues

interact with social cues to influence food cue reactivity in terms of motivational, emotional,

and cognitive, and behavioral reactions toward the different types of food cues?

In sum, it is expected that both use and social cues in food advertisements will elicit appetitive responses, especially when they appear together. This dissertation utilizes multiple dependent variables to examine the influence of this appetitive activation on emotional, cognitive,

39 and behavioral outcomes. Specifically, self-report measures are used to examine subjective feeling of emotional states, and later behavioral intentions (including attitude toward the food product, perceived social support, behavioral control and purchase intention), while secondary task reaction time and psychophysiological measures are used to index real time changes in cognitive responses during food advertising information processing. These hypotheses will be tested in two experiments that manipulate exposure to use cues and social cues in food advertisements.

CHAPTER 3

METHODS

Experiment 1

Purpose of Experiment 1

Experiment 1 aims to achieve the following goals: 1. Pretest and select stimuli (i.e., select stimuli that control for information introduced and arousing content level); 2. To investigate both global (whole message) and local (components within messages) encoding performance during food ads that vary in use and social cues.

Stimuli Selection

Four 30-second food ads in each of the 4 categories created by fully crossing the factor levels of use cue and social cue were selected. All selected food ads were in English and appeared recently on national television in the United States. Specifically, ads were for fast food brands and were collected from YouTube.com based on the type of cues depicted: all of these messages depicted direct food cues while the presence of use and social cue varied.

Sixteen food advertisements were pretested in order to identify the most appropriate stimuli available. Self-reported emotion of these 16 video clips was collected from N = 163 undergraduate students. 12 out of 16 food ads were selected based on the arousing content rating level, which was indexed via self-report ratings on a scale from 1 (low arousing) to 7 (high arousing). Table 3.1 contains self-reported emotion descriptives.

41 Table 3.1 Summary Statistics of Selected Food Ads Message Type Positivity Negativity Arousal M (SD) M (SD) M (SD) Food ad with social cue (S+U-) S+U- Rep1 3.94(1.75) 2.01(1.50) 3.48(1.87) S+U- Rep2 4.85(1.75) 1.83(1.38) 4.04(1.74) S+U- Rep3 4.29(1.89) 2.42(1.80) 3.56(1.83) Food ad with social and use cues (S+U+) S+U+ Rep1 5.02(1.82) 1.77(1.37) 4.08(1.87) S+U+ Rep2 5.16(1.73) 1.88(1.60) 3.99(1.85) S+U+ Rep3 5.04(1.65) 1.62(1.21) 4.14(1.82) Food ad with individual cue (S-U-) S-U- Rep1 4.08(2.02) 2.60(1.94) 3.34(2.02) S-U- Rep2 4.28(1.71) 2.18(1.66) 3.32(1.70) S-U- Rep3 4.32(1.70) 1.96(1.39) 3.51(1.79) Food ad with individual and use cues (S-U+) S-U+ Rep1 4.62(1.65) 1.70(1.21) 3.62(1.68) S-U+ Rep2 3.74(2.02) 1.74(1.32) 4.29(1.77) S-U+ Rep3 4.31(1.80) 1.92(1.37) 3.58(1.89)

The three messages which were most alike in terms of arousing content level ratings were selected, and a multilevel model to test the effect of different ads on arousal ratings was conducted. Prior to multilevel modeling analysis, social cue (individual vs. group) was effect- coded such that individual cue was coded with “-1” and group cue received a “1”; use cue

(absent vs. present) was effect coded such that the absent of use cue was “-1” and the presence of food cue in food ad was “1”. The use of effect coding here means that the intercept is the overall grand mean. The coefficients of the effect coded variables signify the deviation of the means of their corresponding groups from the grand mean.

The results shown in Table 3.2 indicate there were no significant differences between messages within each category in terms of arousal ratings (multilevel modeling test, Food ads

42 with group cues: t(326) = 0.52, p = 0.61; Food ads with use and group cues: t(326) = 1.03, p =

0.30; Food ads with individual cues: t(326) = -0.50, p = 0.71; Food ads with individual and use cues: t(326) = -0.23, p = 0.82). Thus, any differences between categories arguably are not due to one particular message within the category.

Table 3.2. Estimates for Multilevel Models of Self-reported Arousal Message Type Null Model Food ads with group cues Intercept (g00) 3.70 3.62*** (0.11) (0.03) Repetition 0.04 (0.08) Additional information ICC 0.412 -2Log likelihood (FIML) 1901.1 1900.8 N of estimated parameters 3 4 Pseudo R2 0 0.0003

Food ads with use and group cues Intercept (g00) 4.07 4.01*** (0.12) (0.19) Repetition 0.03 (0.07) Additional information ICC 0.488 -2Log likelihood (FIML) 1876.5 1876.3 N of estimated parameters 3 4 Pseudo R2 0 0.0002

Food ads with individual cues Intercept (g00) 3.39 3.22*** (0.11) (0.19) Repetition 0.08 (0.08) Additional information ICC 0.372 -2Log likelihood (FIML) 1927.3 1928.3 N of estimated parameters 3 4 Pseudo R2 0 0.0003

Food ads with individual and use cues Intercept (g00) 3.83 3.87*** (0.11) (0.19) Repetition -0.018 (0.08) CONTINUED

43 Additional information ICC 0.380 -2Log likelihood (FIML) 1901.8 1901.8 N of estimated parameters 3 4 Pseudo R2 0 0.00007 Note* Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

Previous research has indicated that message structural complexity and information density play key roles in influencing the allocation of cognitive resources (Lang et al., 2006; A.

Lang, Bradley, et al., 2007). Therefore, effects of different types of cues in selected food ads could be misinterpreted if structural complexity and information density are not controlled. In order to achieve this control, all clips were coded for camera changes and total information introduced according to the i-squared coding system (A. Lang, Bradley, et al., 2007). Two main elements were coded, camera changes and information introduced:

Camera changes. A camera change was defined as a cross-cut in a video message, in which the camera cut away from the current visual scene to a new visual scene. As A. Lang and her colleagues (2006) suggest, camera changes result in more resource allocation for encoding the message stimulus via the orienting mechanism.

Information introduced. Information introduced in each camera change for a given food ad stimulus was coded and categorized based on seven dimensions compared to the scenes before the camera changes: 1. Emotionally different; 2. A new central object; 3. Relatedness; 4.

A new focal object; 5. Distance to the camera; 6. Seen from a new perspective; 7. Form change

(e.g. a cut from black-and-white to color). For every information introduced category, a score of

1 was given if the criterion was satisfied, and 0 if it was not present. This measurement was developed by A. Lang and her colleagues (A. Lang, Bradley, et al., 2007). The coding scheme has been attached in Appendix 1. In order to ensure control for information introduced, the value

44 of total information introduced for each food ads was summed across the seven dimensions explained above. The results indicated that there were no significant differences in the total scores of information introduced across the 4 message categories, t(12) = 0.33, p = 0.75.

Design

A use cue (2) x social cue (2) x repetition of ad (3) fully crossed within-subjects factorial design was used in Experiment 1. Each of these factors was a manipulated variable.

Manipulated Independent Variables

Social Cues. Social cues were manipulated in this experiment by varying the number of people present in the selected food ads. Two levels of social cue were used in this dissertation: individual vs. a group of people. Thus, food ads with individual cues contain only one person; while food ads with social cues include equal to or greater than 2 persons present. It is important to note that the presence of social cues does not necessarily mean food consumption behavior.

These social cues in mediated messages are considered to function as social support as well to attract viewers to consume food (e.g., Cullen et al., 2000; Limbert, 2010; Sallis et al., 1987;

Wonderlich-Tierney & Vander Wal, 2010). See exemplars below:

Individual Cue Group Cue

Figure 3.1. Social Cue Exemplars.

45 Use cues. Use cues were manipulated in this experiment by varying the presence of food consumption behavior in food advertising (i.e., were people actually eating food or not). In this study, there were two levels of use cue: absence vs. presence.

Repetition. Within each of the social cue (2) x use cue (2) manipulation levels, there were 3 selected food ads presented. This was done to be able to generalize ratings and responses to a type of ad circumstance (e.g., individual less use or group more use) rather than a specific ad for a specific product.

Dependent Variables

Secondary task reaction times (STRTs). Available cognitive resources were indexed using

STRT measurement. STRTs were measured by recording the time in milliseconds from STRT probe onset to the moment the participant pressed the space bar on the computer keyboard to respond to the probe. The STRT probe, which is a 200 millisecond 1,000 Hz audio tone binaurally presented to participants via headphones, were placed in three randomly selected points in each message with one probe occurring in each third of each food ad stimulus.

Randomness of the probe placement ensures that the probe is unpredictable for participants which is important to ensure the validity of the STRT measure. Further, the placement of the

STRT probes also followed rules developed by A. Lang and her colleagues (2006) in order to avoid the confounding effects of message onset, offset, and camera change: 1. The probes are placed at least 5 seconds after the message onset, and the last probe is placed no less than the last

5 seconds; 2. The probes are placed at least 500 milliseconds after a camera change; 3. There are at least a 10-second gap between the probes.

Self-reported emotion. Following Lee and Lang (2015), and Wang, Morey, and Srivastava (2014), self-reported emotions were measured after each food ad on single item scales. Perceived

46 experienced arousal level, positive and negative feelings toward the food ads were collected using a 7-point Likert-type rating scale (from not at all to extremely). The questions include: 1.

Please rate how aroused/excited this food ad makes you feel; 2. Please rate how positive/pleasant this food ad makes you feel; 3. Please rate how negative/unpleasant this food ad makes you feel.

The overall means and standard deviations for self-reported emotion are as follows: Arousal

(Mean = 3.75, SD = 1.84), positivity (Mean = 4.38, SD = 1.89), and negativity (Mean = 1.97, SD

= 1.52).

Recognition Accuracy (Hit Rate). In order to examine recognition accuracy, forced choice

(yes-no) audio and visual-recognition tests were employed. Two target screenshots/audio clips from each food ad were randomly selected from each of the manipulated food clips, one from each half of the message. Foils were selected from similar ads that the participants did not see to match the targets thematically and in terms of (visual & audio) content complexity. Participants were asked to respond as quickly as possible “yes” if they thought they had seen/heard the screenshots/audio clips from the previously viewed food ads, or “no” if they thought the screenshots/audio clips were not from the previously viewed food ads. The pictures were randomly presented for no more than 50ms using Direct RT software (Jarvis, 2014). In the forced-choice yes-no tests, half of the screenshots/audio clips were targets while another half were foils. These items were presented in a random order. Further, the visual and audio recognition tests were also delivered to participants in a random order such that about half the participants completed the visual task first. Hit rate was a computed proportion of the number of targets correctly identified as previously viewed (i.e., hits) out of possible hits.

Signal Detection Measures. In the signal detection paradigm, researchers suggest that only considering hit rates is not enough to assess encoding performance, incorporating signal

47 detection measures is necessary because individuals make memory judgements based on a familiarity criterion that can be very liberal, very conservative or anywhere in between (Fox,

2004; Macmillan & Creelman, 1991; Shapiro, 1994). Therefore, memory sensitivity (assessing recognition memory strength) and criterion bias (assessing recognition memory judgment) were computed for signal detection measures. Specifically, memory sensitivity is evaluated based on the statistic d' (or say, d-prime) – the larger the d' value is, the more sensitive the viewer is at discriminating between targets and foils. The statistic P(c), or criterion bias, is based on how viewers are making their decisions. Viewers will have both fewer false alarms and hits when adopting a more conservative (more positive) criterion bias (i.e., they say no more overall), and but more hits and false alarms when adopting a more liberal (more negative) criterion bias (i.e, they say yes more overall) (Fox, 2004; Macmillan & Creelman, 1991):

d’ = z(Hit rate) – z(False alarm)

P(c) = 0.5 + (Hit rate – False alarm)/2

Cued recall. Cued recall indexes storage of previously encoded information (A. Lang, 2000).

Cued recall tasks are generally performed by providing some sort of cue to the participants that helps them locate the memory trace and report other encoded and stored information. This dissertation indexed cued recall by having participants answer an open-ended question after they were cued with the brand of each food in the ads. Specifically, for each food ad, the participants were given the brand name of a food ad they viewed and asked to recall and recount what they remembered about the ad. For example, participants were given the following message about the

Burger King ad: “you just saw a message about Burger King, please think about everything you remember that was shown in this particular ad and type it into the box below.” The prompts for each food ad were presented randomly. Answers were coded for the presence or absence of

48 mentions of social cues, and use cues separately. For example, if participants mentioned anything related to the man and his friend in the aforementioned Burger King Cheesy Tots commercial, the response was coded a “1” for social cues. If the participants mentioned the actual eating of the cheesy tots, it was coded a “1” for use cues. If the response did not contain mentions of these items, it was coded “0.” All other answers such as responses of “don’t know” and irrelevant or unrelated responses were coded as “0”.

Purchase Intention. A modified version of self-reported purchase intention questionnaire

(Bredahl, 2001) was used in this study. Participants answered a set of questions to indicate their purchase intention scaled from (1) agree strongly to (7) disagree strongly, including: 1. If this food item was available in the shops, I would intent to buy it; 2. I am strongly for buying this food item. Results of the Pearson correlation indicated that there was a significant positive correlation between these two items, r(164) = 0.861, p < 0.001.

Perceived Social Support. This variable was measured via answering the question that “most people who influence what I do think that I should definitely buy this food item” ranging from (1) agree strongly to (7) disagree strongly (Bredahl, 2001).

Perceived Behavioral Control. Participants answered a set of questions (Bredahl, 2001) to indicate their perceived behavioral control scaled from (1) agree strongly to (7) disagree strongly, including: 1. Whether I will eventually buy this ____ is entirely up to me, 2. If this ____ were available in the shops, nothing would prevent me from buying it, and 3. How much control do you have over whether you will eventually buy a ____ like this one?. The Cronbach’s alpha for this purchase intention scale was 0.88.

Attitude toward the product. A modified version of self-reported product attitude questionnaire

(Heise, 1970; Kamins & Marks, 1987; Smith & Swinyard, 1983) was used in this study to index

49 attitude toward the food product. Participants answered a set of questions to indicate their product attitude scaled from (1) agree extremely to (7) disagree extremely, including: 1. This food item is good; 2. This food item is pleasant; 3. This food item is agreeable; 4. This food item is satisfactory. The Cronbach’s alpha for this scale in this sample was 0.969.

Statistical Power Analysis

G*Power (Faul, Erdfelder, Lang, & Buchner, 2007) was used to calculate the initial sample estimation based on an alpha of .05. The results indicated that the proposed repeated measures design requires N = 120 participants to yield an 80% chance of obtaining statistical significance for the proposed design (η2 = .20).

Because some researchers posit that G*Power is not fully competent to estimate sample size for nested or repeated designs or multilevel models, the sample size was estimated again by using the formula below. A pilot study was conducted in order to obtain the pilot estimates of: 1.

The variance of random intercept; 2. The variance of random slope; 3. Residual variance. The number of participants for this pilot study will be 10% of the sample estimation (Cann, Connolly,

Ruuska, MacNeil, Birmingham, Vandervoort, & Callaghan, 2008), which is a total of N of 12. lmmpower(delta =NULL, t = seq(0, 2, by =1), sig2.i = NULL, sig2.s = NULL, sig2.e = NULL, cov.s.i =0.8*sqrt(sig2.i)*sqrt(sig2.s), power = 0.80)

Table 3.3 Brief Introduction for the Formula. pct.change The percent change in the pilot estimate of the parameter of interest. delta The change in the pilot estimate of the parameter of interest, computed from pct.change if left missing. t The vector of time points. sig2.i Pilot estimate of variance of random intercept. sig2.s Pilot estimate of variance of random slope. sig2.e Pilot estimate of residual variance. cov.s.i Pilot estimate of covariance of random slope and intercept. power 0.8.

50 The above-mentioned formula was then used to estimate sample size and the results indicated that at least N = 144 participants were required in order to have enough statistical power for STRT and recognition data analysis.

Participants

A total of N = 163 undergraduate students from a large Northwestern state university participated in the experiment in exchange for course credit. Participants ranged in age from 18 to 26 years old with an average age of 19.94 (SD = 1.57). There were more female participants

(N = 97, 59.5%) than male participants (N = 66, 40.5%). The majority were white (N = 122,

70.9%), followed by Asian (N = 19, 11.1%), Hispanic (N = 17, 9.9%), African American (N = 13,

7.6%), and Native American/Alaskan (N = 1, 0.6%).

Procedure

Upon arrival to the lab, participants’ informed consent was obtained. Participants were instructed to pay attention to the food ads they were going to watch and press the space bar to respond to the STRT probes as quickly as possible each time they heard the “beep” sound. They were told that they would take a memory test at the end of this experiment. Participants were guided to wear headphones before they began the study. After a practice session, they viewed the

16 food ads in a random order. After each food ad, the participants were asked to complete the self-reported emotion, attitude and intention scales. After the completion of food ad viewing session, participants then answered demographic information questions (i.e. age, gender, race, education level, family income, and marital status) in order to clear their short-term memories.

Then, the participants were asked to complete the cued-recall test. Lastly, they completed the recognition tasks. Participants were randomly assigned to complete the visual or the audio

51 recognition task first. After all procedures were completed, participants were thanked, debriefed, and dismissed.

Experiment 2

Purpose of Experiment 2

Experiment 2 aimed to: 1. examine food cue reactivity in terms of motivational, emotional, and cognitive reactions indexed via psychophysiological metrics (heart rate, skin conductance, corrugator supercilii activation, and orbicularis oculi activation) toward the different types of food cues; 2. explore how use cue interact with social cues to influence food cue reactivity in terms of motivational, emotional, and cognitive reactions indexed via psychophysiological metrics after controlling for impulsivity.

Method

Design

Experiment 2 utilized a use cue (2) x social cue (2) x repetition (3) within-subjects factorial design. In the psychophysiological investigation, the addition of a time factor (30 seconds) was included in order to investigate these variables collected at each second of exposure to the messages, which were 30 seconds long.

Dependent Variables

Heart Rate (HR). HR was used as an indicator of attentional resource allocation (A. Lang,

1994). Raw pulse wave data were collected by using a wireless pulse transducer attached on participant’s middle figure to record the pulse pressure wave. Following the procedures recommended by Society of Psychophysiological Research (SPR) on heart rate measurement, the raw signal was collected as the transducer transmits changes in infrared reflectance resulting from varying blood flow. These data were transformed into beats per minute per second data

52 (Berntson, Thomas Bigger, Eckberg, Grossman, Kaufmann, Malik, Van Der Molen, 1997;

Jennings, Bberg, Hutcheson, Obrist, Porges, Turpin, 1981)

Skin Conductance Level (SCL). SCL data were used as an indicator of sympathetic nervous system arousal or intensity of motivational activation (Cacioppo, Tassinary, & Fridlund, 1990).

SCL were collected by placing a pair of standard 8mm Beckman Ag/AgCl disposable electrodes on the palm of the subject’s non- dominant hand. SCL were collected following procedures recommended by SPR (SPR Ad Hoc Committee on Electrodemal Measures, 2012). The raw

SCL signal was recorded using a Biopac MP150 wireless bio-amplifier that passes a constant measurement voltage of 0.5v. Data were averaged per sec Jennings ond.

Facial Electromyography (EMG). EMG data were collected as an indicator of the direction of motivational activation and emotion across exposure to the food ads. EMG was recorded over the left orbicularis oculi (OO; smiling) and corrugator supercilii (CS; frowning) muscle group using miniature 4mm Ag-AgCl electrodes. Activation in the OO muscle group is used to index appetitive motivational activation and positive emotional response (Ekman, Davidson, & Friesen,

1990). Activation in the CS muscle group is used to index aversive motivational activation and negative emotional response (Bradley et al., 2001). Facial EMG data were collected in accordance with recommendations of SPR (Fridlund Jennings & Cacioppo, 1986). The signal was amplified and filtered with a high-pass filter set at 90Hz and a low-pass filter set at 1000Hz.

The signal was rectified, integrated and sampled at 1000Hz. Data were averaged per second.

The remainder of the dependent variables (i.e., “Recognition Accuracy”, “Signal detection measures”, “Signal detection measures”, “Purchase Intention”, and “Attitude toward the product”) were also collected in Experiment 2 but were collected in an identical manner and are not discussed again here.

53 Covariate

Impulsive Behavioral Tendency. Impulsivity was collected using the 12-item subscale in the

UPPS Impulsive Behavior scale (Whiteside, & Lynam, 2003). The Cronbach’s alpha for this scale in this sample was 0.792. Responses to all items scored from (1) agree strongly to (4) disagree strongly. The index mean-centered scores for each participant were entered as a level-3 factor into the models.

Statistical Power Analysis

Sample size requirements were estimated using the procedure reported in Experiment 1.

At least N = 104 participants were required to have appropriate statistical power for the psychophysiological data analysis. At least N = 252 participants were required to have appropriate statistical power for the psychophysiological data analysis when accounting for impulsive behavioral intention. At lease N = 56 participants were required to have enough statistical power for the self-reported (emotion) data analysis.

Participants

A total of N = 164 undergraduate students from a large Northwestern state university participated in the experiment in exchange for course credit. Participants ranged in age from 18 to 35 years with an average age of 20.48 (SD = 2.08). Among these 164 participants, 99 (60.4%) of them were female, 64 (39%) of them were male, and 1 was transgender. The majority were white (N = 126, 76.8%), followed by Asian (N = 22, 13.4%), Hispanic (N = 22, 13.4%), African

American (N = 12, 7.3%), Native American/Alaskan (N = 4, 2.4%).

Procedure

Upon arrival to the lab, participants’ informed consent was obtained. Participants were briefed about data collection procedures. The researcher prepared participants with physiological

54 data collection sensors. Heart rate, skin conductance, orbicularis oculi muscle activity, as well as corrugator supercilii muscle activity were collected. A short training session was held to familiarize participants with the software and protocol procedures. They were given the time to ask the researcher questions before they began the experiment.

All participants were given access to a keyboard and a computer mouse on a movable desk above the participants’ laps. MediaLab software (Jarvis, 2014) was used to deliver the questionnaires. Participants viewed a total of 12 food ads and be asked to complete the same set of questions after each food ad. All psychophysiological measures were recorded continuously during the exposure to those food ads. After participants finished viewing all the food ads and completing the self-report measures, the researcher removed the sensors. As in Experiment 1, demographic information was collected before the cued-recall and audio and visual recognition performance tests, following the same procedures. Then, participants were thanked, debriefed and dismissed.

Data Cleaning, Quantification, and Analysis Strategies for Both Experiments

Before analysis, distribution and missing values of raw response latency data were checked. As expected, STRT response latency data were positively skewed due to some long reaction times, and this skewness was controlled by outlier treatment. If the participant showed no reaction, the missing data were replaced by a maximum allowable reaction time (3000ms).

Digital psychophysiology data were cleaned using interpolation procedures and extracted from the analog signals. The HR, SCL and EMG were analyzed as change scores to better control for carryover and order effects using the subtractive method, as recommended by Potter

& Bolls (2012). Change scores were calculated using the first second of the identified segment as the baseline score.

55 Multilevel modeling (MLM) analyses were then applied. Compared to the repeated measures ANOVA, the multilevel modeling approach is more flexible and especially appropriate for any “nested” data in which one should control for high individual variation (e.g., psychophysiology, memory response latency). Though previous research in this area has utilized a repeated measures ANOVA (e.g., Bailey, 2015, 2016; Bailey et al., 2016; Bolls, Lang, & Potter,

2001; A. Lang, Sanders-Jackson, Wang, & Rubenking, 2013; A. Lang, Park, Sanders-Jackson,

Wilson, Wang, 2007; A. Lang, Yegiyan, 2008; Wang, Lang, & Busemeyer, 2011), this is a less appropriate approach for analyzing nested and longitudinal data for certain reasons. First, longitudinal data generally are highly interdependent. This is particularly the case for psychophysiological data, which tend to change in an analog rather than a digital fashion (Potter

& Bolls, 2012). In this case, it’s problematic to use repeated measures ANOVA as these data violate the independence assumption that may result in biased inferences (i.e., underestimation of standard errors of model parameters, and elevated type I error rates). Second, using repeated measures ANOVA to analyze longitudinal data are also susceptible to the violation of the sphericity assumption, or the assumption that the variances of the differences are equal across all possible pairs of within subject conditions, which can result in a biased F-test. Third, repeated measures ANOVA only considers the observations without missing data, and the observations are dropped if even one measurement is missing, which is referred to as “completer analysis”

(Gibbons, Hedeker, & DuToit, 2010). Alternatively, data have to be replaced using various estimation methods like interpolation. Losing cases presents multiple problems, including reducing statistical power, and also creating bias in the estimation of parameters. On the other hand, replacing missing values can create alternative explanations for your data.

56 Compared to repeated measures ANOVA, MLM is a more robust analytical tool that addresses these issues. First, it allows for handling repeated observations while modeling a non- independent covariance structure, which deals with autocorrelational and sphericity violation issues. Second, it allows for randomly distributed missing values (Gibbons, Hedeker, & DuToit,

2010). And lastly, MLM also allows for both within- and between- individual variance to be retained without having to sum across groups.

Because this dissertation has multiple outcome variables (i.e., chronometric data) that were collected at multiple points in time from multiple individuals, the MLM approach was selected as an analytical tool. 3-level models were used to predict changes in chronometric data.

In the 3-level models, time (L1) is nested under ad characteristics (i.e., social and use cues), and ad characteristics (L2) are nested under individuals (L3). These provide the opportunity to draw both between-person (L3) and between-ad (L2) inferences. Random intercept and slope models were generated to account for variance and predictors at various levels. These models allow for an intercept and slope to be estimated for each individual.

Chronometric Data (i.e. Response Latency and Psychophysiology Data)

Chronometric data (response latency and psychophysiology data) were modeled as a function of the level 1 (time), level 2 (social cue, use cue, repetition), and level 3 (impulsive behavioral intention) predictors and their cross-level interactions. Prior to analysis, social cue

(individual vs. group) was effect-coded such that individual cue was coded with “-1” and group cue received a “1”; use cue (absent vs. present) was effect coded such that the absent of use cue was “-1” and the presence of food cue in food ad was “1”, time was coded as: 1. “0” through “2” for response latency data; 2. “0” through “29” for psychophysiology data with the meaningful

57 zero point as onset of the food ad. This 3-level multilevel model used an unstructured covariance matrix and between-within method of estimating degrees of freedom.

Self-reported and Memory Data

Self-reported data and memory data including audio and visual recognition and cued- recall data were modeled as a function of social and use cues (L1) (when accounting for impulsive behavioral intention). 2-level models were used to predict changes in chronometric data. In the 2-level models, ad characteristics (L1) are nested under individuals (L2). When evaluating self-report and memory data, the variable “time” was not included in the models.

Prior to analysis, social cue (individual vs. group) was effect-coded such that individual cue was coded with “-1” and group cue was coded as “1”; use cue (absent vs. present) was effect coded such that the absence of use cue was “-1” and the presence was “1”. This 2-level multilevel model used a variance components covariance matrix and between-within method of estimating degrees of freedom.

It’s important to note that later behavioral intentions data (i.e., attitude toward the product, social support, behavioral control, and purchase intention) were combined from study 1 and study 2 data collections.

58 CHAPTER 4

RESULTS

Emotion

Skin conductivity level

3-level models were used to predict changes in skin conductivity level (SCL) during information processing of food advertising nested within individuals. By using SCL data as an example, the process of how the 3-level models were generated for each chronometric DV is explained below.

The first step was to estimate the null model. This null model grouping by individuals without any fixed-effects predictors would be represented generally by the following equations:

Model 1.

SCLij = ß0j + eij

ß0j = b00 + u0j

SCLij (combined)= b00 + u0j + eij

In this null model (Model 1), SCLij represents a change in SCL for stimulation i within individual j, and eij is the random errors of prediction for this intercept-only model. The intercept was 0.03, indicating the overall mean SCLs (across individuals) was estimated as 0.03 µS. The mean for individual j is estimated as (0.03 + u0j) µS, where u0j was the individual residual. The variance partition coefficient (VPC) was 0.39, which indicated that 39% of the variance in SCL was at within-person level.

The second step was to extend the null model (Model 2) by adding one level-1 variable –

“time”. See Model 2. In particular, in this level 1 model, ß0j represents the intercept for SCL for individual j, and ß1j is the slope representing the relationship between dependent variables and

59 time for individual j, and eij is the random error of prediction for the level 1 model. The results indicated that a one unit increase in time was associated with 0.005 (SE = 0.0004) decrease in

SCL and the intercept was 0.10 (SE = 0.05). A likelihood ratio test (Dχ2) was then conducted and the results demonstrated that the addition of “time” suggested a better fitting model compared to the null model (likelihood ratio test, Dχ2 = 169.89, d.f. = 1, p < 0.0001). Variable “time” was found to be a significant predictor (multilevel modeling, g = -0.005, SE = 0.0004, t(58610) = -

13.04, p < 0.0001).

Model 2.

SCLij = ß0j + ß1j(Time) + eij

ß1j = b10 + u0j

SCLij (combined) = b00 +b10(Time) + u0j + eij

Next, multivariate models were generated. This 3-level model (Model 3) contained certain additional cross-level interaction parameter including Use cue x Time (H1a), Social Cue x Time (H1b), and Use cue x Social Cue x Time (H1c), and the results suggested that the model fit was significantly improved compared to Model 2 (likelihood ratio test, Dχ2 = 84.73, d.f. = 7, p

< 0.0001). The results for the 3-level random effects analysis of skin conductivity level are shown in Table 4.1.

Table 4.1. Estimates for Three-level Multilevel Models of Skin Conductivity

Model LEVEL AND VARIABLE Null Random Cross-level Covariate Intercept and Interaction Fixed Slope Level 1 Intercept 0.03 0.10* 0.12*** 0.12*** (0.05) (0.05) (0.01) (0.01) Time -0.005*** -0.005 -0.005 (0.0004) (0.003) (0.003) Level 2 Use Cue 0.02** 0.02** (0.007) (0.007) Social Cue -0.002 -0.002 (0.007) (0.007) Repetition -0.007 -0.007 (0.004) (0.004) Social Cue x Use cue -0.02* -0.02* (0.007) (0.007) Cross-level interaction Use Cue x Time -0.0002 -0.0002 (0.0004) (0.0004) Social Cue x Time 0.002*** 0.002*** (0.0004) (0.0004) Social Cue x Use Cue x Time 0.0008 0.0008 (0.0004) (0.0004) Covariate Impulsivity -0.02*** (0.001) Variance components Within-person variance 0.688 0.686 0.781 0.778 Intercept variance 0.432 0.432 Slope variance 0.001 0.001 Additional information ICC 0.386 -2Log likelihood (FIML) 145674 145504 145419 152883 N of estimated parameters 3 4 11 12 Pseudo R2 0 0.002 0.003 0.01 Note* FIML: Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

61 Model 3.

SCLij (combined) = b00 +b10(Time) +b20(Social Cue) +b30(Use Cue) +b40(Repetition) + b50(Social

Cue x Use Cue) + b60(Social Cue x Time) + b70(Use Cue x Time) +b80(Social Cue x Use Cue x

Time) + u0j + u1j (Time) + eij

Overall, it was predicted that individuals would exhibit significant increases in SCL during food ads with use cues (H1a), food ads with group cues (H1b), and food ads with both use and group cues (H1c). These predictions would call for significant effects of use cue and social cue, the interaction of social cue and use cue, and their cross-level interactions with time.

As predicted, the main effect of use cue was significant on skin conductivity (b = 0.02,

SE = 0.007, t(58610) = 2.84, p = 0.005), but the interaction effect of use cue and time on skin conductivity level was not significant (b = -0.0002, SE = 0.0004, t(58610) = -0.382, p = 0.703).

In general, individuals exhibited greater SCL when use cues were present in ads, as predicted

(H1a). Also as predicted, though the main effect of social cue was not significant (b = -0.002, SE

= 0.007, t(58610) = -0.3, p =0.764), the interaction effect of social cue and time was (b = 0.002,

SE = 0.0004, t(58610) = 3.65, p < 0.0001). Individuals exhibited overall more (less decreasing) skin conductivity when watching food ads with group cues (t(29) = 8.16, p < 0.0001) compared to those with individual cues (H1b). See Figure 4.1.

Figure 4.1. Skin conductivity as a function of social cue (individual vs. group cues) and time

Further, though the cross-level interaction effect of social cue, use cue and time was not significant (b = 0.0008, SE = 0.0004, t(58610) = 1.88, p = 0.061), the interaction effect of use and social cues was found to be significant (b = -0.02, SE = 0.007, t(58610) = -2.28, p = 0.023).

Thus, the slopes of the ad types varied, but not by time. Simple slopes were tested within each condition of the interaction, which indicated that group and use cue ads had a less steep slope than individual and use cue ads (b = 0.025, p < 0.0001) and group without use cue ads (b =

0.0178, p < 0.0001). It was predicted that group and use cue ads would elicit the greatest SCL responses across time, which was supported. See Figure 4.2.

63 S+U+ S+U- 0.2 S-U+ S-U- 0.15

S) 0.1 µ 0.05

0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

SCL Activation ( Activation SCL -0.05

-0.1

-0.15 Time (Second)

Figure 4.2. Skin conductivity as a function of social cue (individual vs. group cues) and use cue (absence vs. presence) and time

Model 4.

SCLij (combined) = b00 +b10(Time) +b20(Social Cue) +b30(Use Cue) +b40(Repetition) + b50(Impulsive behavioral tendency) + b60(Social Cue x Use Cue) + b70(Social Cue x Time) + b80(Use Cue x Time) +b90(Social Cue x Use Cue x Time) + u0j + u1j (Time) + eij

The addition of impulsive behavioral tendency in the model (Model 4) significantly the model fit compared to Model 3 (likelihood ratio test, Dχ2 = 221.2, d.f. = 1, p < 0.0001). The results for the 3-level random effects analysis of skin conductivity level are shown in Table 4.1.

After accounting for impulsive behavioral tendency, the main, interaction, and cross level interaction effects that were found to be significant in Model 3 were still significant in Model 4

64 (RQ1). Further, impulsive behavioral tendency was found to have a significant effect on SCL (b

= -0.016, SE = 0.001, t(54110) = -14.89, p < 0.0001). Interestingly, individuals high in impulsive behavioral tendency exhibited overall less skin conductivity when viewing the food ads compared to those low in impulsive behavioral tendency.

Thus, the H1 set of hypotheses was generally supported in the SCL data.

Orbicularis oculi (OO) activation

Likelihood ratio tests were firstly conducted in order to examine the model fit, and the results indicated that that the addition of “time” suggested a better fitting model compared to the null model (likelihood ratio test, Dχ2 = 143.66, d.f. = 1, p < 0.0001). Variable “time” was found to be a significant predictor (b = 0.0004, SE = 0.00003, t(58610) = 11.99, p < 0.0001). Further, the level-2 model with variables social cue, use cue, repetition, and time further improved the model fit compared to the level-1 model (likelihood ratio test, Dχ2 = 366.28, d.f. = 7, p < 0.0001).

The results for the two level random effects analysis of OO activation are shown in Table 4.2.

65 Table 4.2. Estimates for Three-level Multilevel Models of Orbicularis Oculi Activation

Model LEVEL AND VARIABLE Null Random Intercept Cross-level and Fixed Slope Interaction Level 1 Intercept 0.08 0.002 0.003** (0.003) (0.003) (0.0009) Time -0.0004*** 0.0004* (0.0003) (0.0002) Level 2 Use Cue 0.002** (0.0006) Social Cue -0.0008 (0.0006) Repetition -0.0006 (0.0004) Social Cue x Use cue 0.001* (0.0006) Cross-level interaction Use Cue x Time 0.0002*** (0.00003) Social Cue x Time 0.0002*** (0.00003) Social Cue x Use Cue x Time -0.000002 (0.0003) Variance components Within-person variance 0.005 0.005 0.000004 Intercept variance 0.001 0.001 Slope variance 0.000004 Additional information ICC 0.215 -2Log likelihood (FIML) -141639 -141783 -142149 N of estimated parameters 3 4 11 Pseudo R2 0 0.002 0.007 Note* FIML: Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

Overall, it was predicted that individuals would exhibit greater OO activation during food ads with use cues (H1a), food ads with group cues (H1b), and food ads with both use and group cues (H1c). These predictions would call for significant effects of use cue and social cue, the interaction of social cue and use cue, and their cross-level interactions with time.

66 As predicted, the main effect of use cue was significant on OO activation (b = 0.02, SE =

0.006, t(58610) = 3.26, p = 0.001). In general, individuals exhibited greater OO activation when use cues were present in ads, as predicted (H1a). Further, the predicted interaction effect of use cue and time was also found to be significant (b = 0.0002, SE = 0.00003, t(58610) = 4.68, p <

0.0001). Individuals exhibited more OO activation when watching food ads with use cues compared to those without use cues (t(29) = 6.77, p < 0.0001) (H1a). See Figure 4.3.

Figure 4.3. Orbicularis oculi activation as a function of use cue (absence vs. presence) and time

H1b predicted that individuals would exhibit more positive emotions when viewing the food ads with group cues compared to those with individual cues. The predicted main effect of social cues on OO activation was not significant (b = -0.0008, SE = 0.0006, t(58610) = -1.43, p =

0.1), but the interaction effect of social cue and time on OO activation was found to be significant (b = 0.0002, SE = 0.00003, t(58610) = 4.682, p < 0.0001). Individuals exhibited more

67 OO activation when processing food ads containing group cues (g = 0.0004, p < 0.0001) compared to those with individual cues (H1b). See Figure 4.4.

Group cue 0.025 Individual cue V) µ 0.02

0.015

0.01

0.005 Orbicularis oculi activation ( activation oculi Orbicularis 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 Time (Second)

Figure 4.4. Orbicularis oculi activation as a function of social cue (individual vs. group cues) and time

Further, though the predicted interaction effect of time, social cue and use cue was not significant (b = -0.000002, SE = 0.00003, t(58610) =-0.062, p = 0.95), the interaction effect of use and social cues was found to be significant (b = 0.001, SE = 0.0006, t(58610) = 2.22, p =

0.026). This indicates that the slopes of the ads differed but not over time. Simple slopes were tested within each condition of the interaction. Results indicated that the group and use cues had a steeper slope (b = 0.003, p < 0.0001) compared to those with group cues without use cue ads, and individual and use cue ads (b = 0.002, p < 0.0001). Thus, individuals exhibited the most OO activation when processing the food ads with group and use cues compared to other types of messages, as predicted. See Figure 4.5.

68 S+U+ S+U- 0.03 S-U+ S-U- V)

µ 0.025 0.02 0.015 0.01 0.005 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 -0.005 Orbicularis oculi activation ( activation oculi Orbicularis Time (Second)

Figure 4.5. Orbicularis oculi activation as a function of social cue (individual vs. group cues) and use cue (absence vs. presence) and time

Thus, the H1 set of hypotheses for OO activation were generally supported.

The addition of the factor “Impulsive behavioral tendency” did not significantly improve model fit compared to the cross-level interaction model (likelihood ratio test, Dχ2 = 0.06, d.f. = 1, p = 0.80) (RQ1). Therefore, the model results are not reported.

Corrugator supercilii (CS) activation

Likelihood ratio tests were firstly conducted in order to examine the model fit, and the results indicated that that the addition of “time” suggested a better fitting model compared to the null model (likelihood ratio test, Dχ2 = 134.8, d.f. = 1, p < 0.0001), and the level-2 model with variables social cue, use cue, repetition, and time further improved the model fit compared to the level-1 model (likelihood ratio test, Dχ2 = 142.43, d.f. = 7, p < 0.0001). The results for the two level random effects analysis of CR activation are shown in Table 4.3.

Table 4.3. Estimates for Three-level Multilevel Models of Corrugator Supercilii Activation

Model LEVEL AND VARIABLE Null Random Intercept Cross-level and Fixed Slope Interaction Level 1 Intercept 0.0001 0.006* 0.006*** (0.003) (0.003) (0.001) Time -0.0004*** -0.0004* (0.00004) (0.0002) Level 2 Use Cue 0.0002 (0.0006) Social Cue 0.0008 (0.0006) Repetition 0.0001 (0.0004) Social Cue x Use cue -0.003*** (0.0006) Cross-level interaction Use Cue x Time -0.00009* (0.00004) Social Cue x Time -0.004*** (0.0006) Social Cue x Use Cue x Time 0.00004 (0.00004) Variance components Within-person variance 0.006 0.006 0.006 Intercept variance 0.002 0.002 Slope variance 0.000004 Additional information ICC 0.202 -2Log likelihood (FIML) -133170 -133305 -133448 N of estimated parameters 3 4 11 Pseudo R2 0 0.002 0.004 Note* FIML: Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

Overall, it was predicted that individuals would exhibit less CS activation during food ads with use cues (H1a), food ads with group cues (H1b), and food ads with both use and group cues

(H1c). These predictions would call for significant effects of use cue and social cue, the interaction of social cue and use cue, and their cross-level interactions with time.

The predicted main effect of use cue was not significant (b = 0.0002, SE = 0.0006, t(58610) = 0.31, p = 0.76), but the interaction effect of use cue and time was found to be significant (b = -0.00009, SE = 0.00004, t(58610) = -2.47, p = 0.013). Individuals exhibited the less CS activation when watching food ads with use cues compared to those without use cues, indicating more positive emotional feelings being elicited when viewing the food ads with use cues than those without use cues (H1a). See Figure 4.6.

0.008 Use cue

V) 0.006 µ No use cue 0.004 0.002 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 -0.002 -0.004 -0.006 -0.008 Corrugator supercilii activation ( activation supercilii Corrugator -0.01 Time (Second)

Figure 4.6. Corrugator supercilii activation as a function of use cue (absence vs. presence) and time

H1b predicted that individuals would exhibit more appetitive emotions when viewing the food ads with social cues compared to those with individual cues. Though the main effect of social cue was not significant (b = 0.0008, SE = 0.0006, t(58610) = 1.26, p = 0.21), the predicted interaction effect of social cue and time on CS activation was also found to be significant (b = -

0.0001, SE = 0.00004, t(58610) = -3.41, p < 0.0001). Overall, individuals exhibited less CS activation when processing the food ads containing group cues (t(29) = -5.19, p < 0.0001) than those with individual cues (H1b). See Figure 4.7.

0.008 Group cue V)

µ 0.006 Individual cue 0.004 0.002 0 -0.002 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 -0.004 -0.006 -0.008 -0.01 Corrugator supercilii activation ( activation supercilii Corrugator -0.012 Time (Second)

Figure 4.7. Corrugator supercilii activation as a function of social cue (individual vs. group cues) and time

Further, though the predicted interaction effect of time, social cue, and use cue was not significant (b = 0.00004, SE = 0.00004, t(58610) = 1.10, p = 0.27), the interaction effect of use and social cues was found to be significant (b = -0.004, SE = 0.0006, t(58610) = -5.90, p <

0.0001). This indicates that the slopes of CS for the ads were different, but did not vary over time.

Simple slopes were tested within each condition of the interaction. Ads with group and use cues had a steeper slope than individual and use cue ads (b = 0.002, p < 0.0001) and group without use cue ads (b = 0.002, p < 0.0001). Thus, individuals exhibited the least CS activation over time when processing the food ads with group and use cues. See Figure 4.8.

0.015 S+U+ S+U- V) µ 0.01 S-U+ S-U-

0.005

0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 -0.005

-0.01

-0.015

Corrugator supercilii activation ( activation supercilii Corrugator -0.02 Time (Second)

Figure 4.8. Corrugator supercilii activation as a function of social cue (individual vs. group cues) and use cue (absence vs. presence) and time

The addition of the level 3 factor “Impulsive behavioral tendency” did not significantly improve model fit compared to the level 2 model (likelihood ratio test, Dχ2 = 0.695, d.f. = 1, p =

0.40) (RQ1). Therefore, the model results are not reported.

Thus, the H1 set of hypotheses on corrugator supercilii were generally supported.

Self-reported emotion

The self-reported emotion data reported here are the data collected from study 2 as emotion ratings in study 1 were used to select ads for use in study 2. Multilevel modeling analysis was used to examine if the addition of use cue (H2a), group cue (H2b), and use and group cues (H2c) in food ads would facilitate more positive and intense and less negative self- reported emotional feelings toward the given food products. These predictions would call for significant effects of use cue and social cue, the interaction of social cue and use cue, and their cross-level interactions with time.

For the positivity ratings model, the addition of use and social cues significantly improved model fit over the null model (likelihood ratio test, Dχ2 = 96.51, d.f. = 3, p < 0.0001).

See Table 4.4.

Table 4.4. Estimates for Multilevel Models of Self-reported Positive Emotion

Model LEVEL AND VARIABLE Null Level-2 Model Intercept 4.20 4.20*** (0.07) (0.07) Use Cue 0.42*** (0.03) Social Cue 0.23*** (0.03) Social Cue x Use cue 0.08* (0.03) Variance components Within-person variance 0.715 0.736 Intercept variance 2.319 2.065 Additional information ICC 0.236 -2Log likelihood (FIML) 7494 7284.7 N of estimated parameters 3 6 Pseudo R2 0 0.08 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

Overall, individuals reported higher positivity ratings for the food ads with use cues compared to those without (b = 0.42, SE = 0.03, t(1804) = 12.89, p < 0.0001) (H2a). Further, individuals reported greater positivity for the food ads with group cues compared to individual cues (b = 0.23, SE = 0.03, t(1804) = 7.03, p < 0.0001) (H2b). Further, the interaction effect of use and social cues was also found to be significant (b = 0.08, SE = 0.03, t(1804) = 2.51, p =

0.01). Simple slopes were tested within each condition of the interaction. Results indicated that individuals reported higher positivity ratings for the food ads with group and use cues (b = 0.34, p < 0.0001) compared to those with group cues only. They also reported higher positivity ratings

for the food ads with group and use cues (b = 0.15, p = 0.001) compared to those with individual and use cues. See Figure 4.9. Thus, individuals reported the highest positivity ratings for the food ads with group and use cues compared to other message types.

Figure 4.9. Positive emotion as a function of social cue (individual vs. group cues) and use cue (absence vs. presence)

The addition of impulsive behavioral tendency did not significantly improve model fit compared to the level 2 model (likelihood ratio test, Dχ2 = 3.68, d.f. = 1, p = 0.06) (RQ1).

Therefore, the results of the model are not reported.

Thus, the H2 set of hypotheses for positivity ratings were supported.

For the negativity ratings model, the addition of use and social cues significantly improved model fit over the null model (likelihood ratio test, Dχ2 = 122.02, d.f. = 3, p < 0.0001).

See Table 4.5. The predicted main effect of use cue was found to be significant (b = -0.32, SE =

0.03, t(1804) = -10.59, p < 0.0001). Food ads with use cues were rated as less negative compared to those without (H2a). The predicted main effect of social cue was also found to be significant

(b = -0.08, SE = 0.03, t(1804) = -2.52, p = 0.01). Food ads with group cues were rated as less

negative compared to those with individual cues (H2b). Further, the interaction effect of use and social cues was also found to be significant (b = -0.08, SE = 0.03, t(1804) = -2.80, p = 0.005).

Simple slopes were tested within each condition of the interaction. Results indicated that individuals reported less negativity ratings for the food ads with group and use cues (b = -0.23, p

< 0.0001) compared to those with group cues only. They also reported less negativity ratings for the food ads with group and use cues compared to those with individual and use cues, though this was not statistically significant (b = 0.08, p = 0.85). See Figure 4.10. Thus, individuals reported the least negativity ratings for the food ads with group and use cues compared to other message types.

Table 4.5. Estimates for Multilevel Models of Self-reported Negative Emotion

Model LEVEL AND VARIABLE Null Level 2 Model Intercept 2.03 2.03*** (0.06) (0.06) Use Cue -0.32*** (0.03) Social Cue -0.08* (0.01) Social Cue x Use cue -0.08* (0.03) Variance components Within-person variance 1.876 1.754 Intercept variance 0.420 0.431 Additional information ICC 0.183 -2Log likelihood (FIML) 7037.5 6915.5 N of estimated parameters 3 6 Pseudo R2 0 0.05 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

Figure 4.10. Negative emotion as a function of social cue (individual vs. group cues) and use cue (absence vs. presence)

Again, the addition of impulsive behavioral tendency did not significantly improve model fit compared to the level 2 model (likelihood ratio test, Dχ2 = 1.15, d.f. = 1, p = 0.28) (RQ1). The results of the model are not reported.

Overall, the H2 set of hypotheses for negativity ratings were supported.

When examining how the presence of use and social cues affect self-reported arousal ratings, the addition of use and social cues significantly improved model fit over the null model

(likelihood ratio test, Dχ2 = 108.27, d.f. = 3, p < 0.0001). See Table 4.6. The predicted main effects of use cue (b = 0.312, SE = 0.03, t(1804) = 10.25, p < 0.0001), was significant such that suggesting that individuals reported more arousal for the food ads with use cues compared to those without (H2a). The main effect of social cue was also significant (b = 0.060, SE = 0.03, t(1804)= 1.64, p = 0.049), such that individuals reported more arousal for the food ads with group cues compared to those with individual cues. However, the interaction effect of use and

social cues on arousal ratings was not significant (b = 0.050, SE = 0.03, t(1804) = 1.64, p = 0.10)

(H2c).

Table 4.6. Estimates for Multilevel Models of Self-reported Arousal Emotion

Model LEVEL AND VARIABLE Null Level 2 model

Intercept 3.72 3.72*** (0.08) (0.06) Use Cue 0.31*** (0.03) Social Cue 0.06* (0.03) Social Cue x Use cue 0.05 (0.03) Variance components Within-person variance 1.937 1.824 Intercept variance 1.020 1.029 Additional information ICC 0.345 -2Log likelihood (FIML) 7212.2 7103.9 N of estimated parameters 3 6 Pseudo R2 0 0.03 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

The addition of impulsive behavioral tendency did not significantly improve model fit compared to the level 2 model (likelihood ratio test, Dχ2 = 3.66, d.f. = 1, p = 0.06) (RQ1), and it is not further reported.

The H2 set of hypotheses for arousal ratings were partially supported.

Resource Allocation (STRTs), Encoding, and Storage Performance

Individual encoding, indexed via recognition, and storage, indexed via cued recall, of media contents in the food ads were examined in both study 1 and study 2. However, only the results of individual encoding and storage of media contents in study 1 are discussed here along with STRT results, in order to best interpret cognitive processing states. It is ill-advised to collect

STRT and psychophysiological data concurrently (Lang, Potter, & Bolls, 2009; Potter & Bolls,

2012), and therefore, these two types of dependent variables were collected separately. STRTs were collected in study 1 and psychophysiology data were collected in study 2. Therefore, only the recognition data from study 1 are examined in coalition with the STRTs. Please see

Appendix 2 for the analysis of recognition data collected in study 2. The results are generally the same across the two studies.

For the STRT model, the addition of “time” suggested a better fitting model compared to the null model (likelihood ratio test, Dχ2 = 244.45, d.f. = 1, p < 0.0001) and the cross-level interaction model with variables social cue, use cue, repetition, and time further improved the model fit compared to the random intercept and fixed slope model (likelihood ratio test, Dχ2 =

75.05, d.f. = 7, p < 0.0001). The results indicated that the main effect of time was found to be significant (b = -76.99, SE = 9.09, t(167) = -8.47, p < 0.0001). See Table 4.7.

Table 4.7. Estimates for Three-level Multilevel Models of STRTs

Model LEVEL AND VARIABLE Null Random Intercept Cross-level and Fixed Slope Interaction Level 1 Intercept 290.41*** 367.40*** 403.97*** (15.13) (15.89) (19.69) Time -76.99*** -76.99*** (4.87) (6.33) Level 2 Use Cue 8.28 (6.16) Social Cue 24.80*** (6.16) Repetition -18.28*** (4.77) Social Cue x Use cue 9.12 (6.16) Cross-level interaction Use Cue x Time 7.93 (4.77) Social Cue x Time -12.32* (4.77) Social Cue x Use Cue x Time -20.21*** (4.77) Variance components Within-person variance 96897 92833 89109 Intercept variance 34604 34717 42812 Slope variance 2815 Additional information ICC 0.263 -2Log likelihood (FIML) 84454 84210 84135 N of estimated parameters 3 4 11 Pseudo R2 0 0.030 0.039 Note* FIML: Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

Overall, it was predicted that individuals would exhibit slower STRTs (fewer resources available) when viewing the food ads with use cues, the food ads with group cues, and the food ads with both use and group cues. The predicted effects were main effects of use cue, social cue, their interaction, and the interaction of each of these effects with time.

The results indicated that neither the main effect of use cue, (b = 8.28, SE = 6.90, t(5437)

= 1.20, p = 0.23), nor the interaction effect of use cue x time (b = 7.93, SE = 5.34, t(5437) = 1.48, p = 0.14) were statistically significant. See Table 4.5. Individuals did not exhibit significantly slower STRTs for the food ads with use cues compared to those without use cues. Thus, H3a was not supported.

H3b predicted that individuals would exhibit slower STRTs for the food ads with group cues compared with those with individual cues. The main effect of social cue on STRT was found to be significant (b = 24.80, SE = 6.90, t(5437) = 3.60, p < 0.0001), such that individuals exhibited overall slower STRTs (fewer resource available) when processing the food ads with group cues compared to those with individual cues. The predicted effect of a cross level interaction of social cue x time was also found to be significant (b = -12.32, SE = 4.84, t(5437) =

-2.58, p = 0.01). See Table 4.7. Individuals exhibited slower STRTs (fewer resource available) when processing the food ads with group cues compared to those with individual cues, especially at the beginning of the messages. Thus, H3b was supported.

H3c predicted that individuals would exhibit the slowest STRTs for the food ads with both group and use cues compared to other types of food ads. Though the interaction effect of social and use cues was not significant (b = 9.12, SE = 6.16, t(5437) = 1.46, p = 0.14), the predicted cross level 3-way interaction of social cue x use cue x time, which was found to be significant (b = -20.21, SE = 4.77, t(5437) = -4.23, p < 0.0001). STRTs were faster overall across

all time. However, as can be seen from Figure 4.11, individuals exhibited slower STRTs (fewer available resources) across time for food ads with group and use cues compared to other types of food ads, though at probe 3, individual use and group use ads converged. Thus, H3c was supported.

500 S+U+ S+U- 450 S-U+ S-U-

400

350

300

250

STRT Latency (Milliseconds) Latency STRT 200

150 0 1 2 Probe time

Figure 4.11. STRT latency as a function of social cue (individual cue (S-) vs. group cue (S+)), use cue (absence (U-) vs. presence (U+)) and placement of probe within message

The addition of impulsive behavioral tendency did not significantly improve model fit compared to the cross-level interaction model (likelihood ratio test, Dχ2 = 0.34, d.f. = 1, p = 0.56)

(RQ1), and the model is not reported.

Thus, the H3 set of hypotheses on STRTs were generally supported; however, use cues did not influence STRTs.

Following STRT analysis, encoding performance (audio and visual recognition accuracy, sensitivity, and criterion bias) were examined. Similar to the previous set of multilevel models, two-level models were sequentially developed to predict visual and audio encoding performance

of food advertising nested within individuals. However, multilevel logistic modeling analysis was used for the accuracy models as this dependent variable was a dichotomous indicator of whether the recognition response was true or false. Three separate multilevel analyses were performed in order to test how use cue (H4a), social cue (H4b), and the interaction of use and social cues (H4c) in the advertising would affect visual and audio recognition encoding performance. The predicted effects were main effects of use cue, social cue, their interaction, and the interaction of each of these effects with time.

The results indicated that the addition of use and social cues and repetition did significantly improve the model fit compared to the null model (likelihood ratio test, visual recognition data: Dχ2 = 14.19, d.f. = 4, p = 0.007; audio recognition data: Dχ2 = 228.36, d.f. = 4, p < 0.0001). See Table 4.8 for the results of recognition accuracy, and Table 4.9 for the results of signal detection measures (i.e., recognition sensitivity, and criterion bias).

Table 4.8. Estimates for Multilevel Logistic Models of Recognition Accuracy

Model LEVEL AND VARIABLE Null Level 1 model Dependent variable: Visual recognition accuracy Intercept 1.82 1.40*** (0.07) (0.13) Use Cue 0.09* (0.04) Social Cue 0.02 (0.04) Repetition 0.28** (0.09) Social Cue x Use cue -0.03 (0.04) Variance components Intercept variance 0.459 0.464 Additional information ICC 0.314 -2Log likelihood (FIML) 3325.8 3311.7 N of estimated parameters 2 6 Pseudo R2 0 0.003 Dependent variable: Audio recognition accuracy Intercept 0.07 -0.49*** (0.04) (0.09) Use Cue 0.30*** (0.03) Social Cue -0.19*** (0.03) Repetition 0.28*** (0.04) Social Cue x Use cue 0.28* (0.03) Variance components Intercept variance 0.059 0.078 Additional information ICC 0.314 -2Log likelihood (FIML) 5411 5182.6 N of estimated parameters 2 6 Pseudo R2 0 0.06 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

Table 4.9. Estimates for Two-level Multilevel Logistic Models of Encoding Performance

Model LEVEL AND VARIABLE Null Level 1 model Dependent variable: Visual recognition sensitivity Intercept 0.15 0.15 (0.10) (0.10) Use Cue 0.09 (0.05) Social Cue 0.07 (0.05) Social Cue x Use cue 0.09 (0.05) Variance components Within-person variance 1.956 1.926 Intercept variance 1.193 1.204 Additional information ICC 0.379 -2Log likelihood (FIML) 2458.8 2463.8 N of estimated parameters 3 6 Dependent variable: Visual criterion bias Intercept -0.15 -0.15*** (0.04) (0.004) Use Cue -0.006* (0.03) Social Cue -0.011*** (0.003) Social Cue x Use cue 0.007* (0.003) Variance components Within-person variance 0.006 0.006 Intercept variance 0.001 0.001 Additional information ICC 0.174 -2Log likelihood (FIML) -1349.5 -1372.2 N of estimated parameters 3 6 Dependent variable: Audio recognition sensitivity Intercept (g00) -0.47 -0.47*** (0.06) (0.06) Use Cue 0.04 (0.03) Social Cue 0.25*** (0.03) Social Cue x Use cue 0.09 (0.05)

Variance components Within-person variance 0.715 0.629 Intercept variance 0.340 0.364 Additional information ICC 0.322 -2Log likelihood (FIML) 1762.4 1702.1 N of estimated parameters 3 6 Dependent variable: Audio criterion bias Intercept (g00) -0.02 -0.02*** (0.003) (0.003) Use Cue 0.03*** (0.002) Social Cue 0.009*** (0.002) Social Cue x Use cue -0.002 (0.002) Variance components Within-person variance 0.003 0.002 Intercept variance 0.001 0.001 Additional information ICC 0.256 -2Log likelihood (FIML) -1727 -1372.2 N of estimated parameters 3 6 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

First, this study predicted individuals would exhibit a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, less sensitivity and more conservative criterion bias) for the food ads with use cues than those without (H4a). The prediction would be for a main effect of use cue. This was significant on visual encoding accuracy (z = 1.96, p = 0.049). The odds of having visual encoding performance accuracy when processing food ads with use cues was significantly higher (1.09 times as likely; 95% CI: 1.00 to

1.19 times) compared to those without use cues. These were opposite to the predicted direction.

Further, it was also predicted that individuals would exhibit less sensitivity and more

conservative criterion biases for decision making for food ads with use cues compared to those without use cues. The main effect of use cue on visual recognition sensitivity (b = 0.09, SE =

0.05, t(482.5) = 1.65, p = 0.101) was not significant. The predicted its main effect on visual criterion bias was found to be significant (b = -0.007, SE = 0.003, t(480) = -1.99, p = 0.047). The results indicated that individuals were liberal overall (less than 0). In particular, they were more liberal when making decisions for the food ads without use cues than those with use cues. In other words, individuals were more likely to say yes in general, but especially when ads contained use cues. This was reflected in high hit rates, which resulted in greater accuracy scores for use cues, but individuals were not actually more sensitive to the visual information.

In terms of audio encoding performance, it was predicted that individuals would exhibit poorer audio recognition accuracy for food ads with use cues than those without. The predicted effect was the main effect of use cue on audio recognition accuracy, which was found to be significant (z = 8.92, p < 0.0001). However, the odds of having accurate encoding performance when processing food ads with use cues was significantly higher (1.81 times as likely; 95% CI:

1.59 to 2.06 times) compared to those without use cues. Again, this was opposite to the predicted direction. It was also predicted that individuals would exhibit poorer audio recognition sensitivity and a more conservative criterion bias for decision making for food ads with use cues compared to those without. The main effect of use cue on audio recognition sensitivity was not significant (b = 0.04, SE = 0.03, t(475.1) = 1.26, p = 0.208). The predicted main effect of use cue on criterion bias was found to be significant (b = -0.03, SE = 0.002, t(463) = -18.695, p < 0.0001).

Individuals were most liberal when making decisions for the food ads containing use cues than those without. As with visual encoding, audio encoding decisions were, on average, made with a more liberal criterion, especially when use cues were present.

Second, this study predicted individuals would exhibit a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, less sensitivity and more conservative criterion bias) for the food ads with group cues than those with individual cues

(H4b). In terms of visual encoding performance, it was predicted that individuals would exhibit lower visual recognition accuracy for food ads with group cues than those with individual cues.

Therefore, the predicted effect is a main effect of social cue. This effect was not significant on visual recognition accuracy (z = 0.38, p = 0.71). It was also predicted that individuals would exhibit less sensitivity and a more conservative criterion bias for decision making for food ads with group cues compared to those without individual cues. The predicted main effect of social cue on criterion bias was found to be significant (b = -0.01, SE = 0.003, t(478.6) = -3.77, p <

0.0001). Participants exhibited a more liberal conservative bias when group cues were present.

In terms of audio encoding performance, it was also predicted that food ads containing group cues would elicit lower audio recognition accuracy than those without individual cues. The predicted effect was the main effect of social cue on audio recognition accuracy, which was found to be significant (z = -5.60, p < 0.0001). As predicted, the odds of having higher encoding performance accuracy when processing food ads with group cues was significantly lower (69%;

95% CI: 60% to 78%) compared to those without individual cues. It was also predicted that individuals would exhibit less sensitivity and a more conservative criterion bias for decision making for food ads with group cues compared to those with individual cues. The predicted main effect of social cue on audio recognition sensitivity (b = 0.25, SE = 0.03, t(477.2) = 7.80, p <

0.0001) and criterion bias (b = 0.009, SE = 0.002, t(459.1) = 5.857, p < 0.0001) were found to be significant. Against predictions, the results indicated that messages containing group cues

elicited greater audio recognition sensitivity and a more conservative criterion bias (though still overall liberal) for decision making compared to those with individual cues.

Third, this study also predicted individuals would exhibit the poorer encoding performance (i.e., less visual and audio recognition accuracy, less sensitivity and more conservative criterion bias) for the food ads with use and group cues compared to other types of food ads (H4c).

In terms of visual encoding performance, the predicted interaction effect of use and social cues was found to be marginally significant (z = -0.03, p = 0.5049) on visual accuracy. Results indicated that the log odds of visual accuracy for the food ads with group and use cues was significantly higher (1.04 times as likely) (b = 0.12, p = 0.08) compared to those with individual and use cues only.

It was also predicted that individuals would exhibit less visual sensitivity and a more conservative criterion bias for decision making for food ads with both group and use cues compared to other types of food ads. The predicted interaction effect of use and social cues on visual sensitivity was not significant (b = -0.01, p = 0.003). The predicted interaction effect of social and use cues on criterion bias was found to be significant (b = 0.007, SE = 0.003, t(480) =

2.27, p = 0.02). See Table 4.9. Simple slopes were tested within each condition of the interaction.

It was predicted that individuals would exhibit a more conservative criterion bias for decision making for food ads with group and use cues; however, results indicated that there were no significant differences between the food ads with group and use cues and those with individual and use cues (b = -0.005, p = 0.29). See Figure 4.12.

Figure 4.12. Visual criterion bias as a function of social cue (individual cue (S-) vs. group cue (S+)) and use cue (absence (U-) vs. presence (U+))

In terms of audio encoding performance, it was also predicted that food ads containing both group and use cues would create less audio recognition accuracy than other types of food ads. The predicted effect was an interaction effect of use and social cues on audio recognition accuracy (z = 8.47, p < 0.0001). Results indicated that audio recognition accuracy was higher for the food ads with group and use cues (1.77 times as likely) compared to those with group cues without use cues (b = 0.57, p < 0.0001), and messages containing group and use cues had a higher audio recognition accuracy (1.10 as likely) compared those with individual and use cues

(b = 0.09, p = 0.04). It was also predicted that individuals would exhibit less audio recognition sensitivity and a more conservative criterion bias for decision making for food ads with both group and use cues compared to other types of food ads. However, the predicted interaction effects of use and social cues on audio recognition sensitivity (b = 0.04, SE = 0.03, t(475.1) =

1.26, p = 0.208) and criterion bias (b = -0.002, SE = 0.002, t(459.1) = -1.16, p = 0.247) were not significant.

In addition, The addition of “impulsive behavioral tendency” does not significantly improve mode fit compared to the level 1 models (likelihood ratio test, visual recognition accuracy data: Dχ2 = 0.36, d.f. = 1, p = 0.55; audio recognition accuracy data: Dχ2 = 0.55, d.f. = 1, p = 0.46; visual recognition sensitivity: Dχ2 = 0.15, d.f. = 1, p = 0.70; visual criterion bias: Dχ2 =

0.01, d.f. = 1, p = 0.91; audio recognition sensitivity: Dχ2 = 1.18, d.f. = 1, p = 0.28; audio criterion bias: Dχ2 = 0.16, d.f. = 1, p = 0.70). These models are not discussed further.

Thus, the H4 set of hypotheses were not well supported. Overall, viewers were more likely to say yes (hit rates were high) in audio and visual recognition tests, especially when ads contained use and group cues. However, they were not more sensitive to the previously encountered audio and visual information based on the presence of use and group cues in general.

Another set of two-level models were then sequentially developed to examine storage performance of food advertising nested within individuals. Multilevel logistic modeling analysis was used here as the dependent variable was a dichotomous indicator of whether participants mentioned use/social cues when performing the cued recall test. Two separate multilevel analysis were performed in order to test the main effects of use cue (H5a) and social cue (H5b), and the interaction of use and social cues (H5c) on storage performance. Results indicated that the addition of use and social cues significantly improved model fit compared to the null model

(likelihood ratio test, Dχ2 = 133.35, d.f. = 3, p < 0.0001).

Overall, it was predicted that the addition of use and/or social cues in food ads (i.e., the food ads with use cues, the food ads with group cues, and the food ads with both use and group cues) would create poorer storage performance. Therefore, main effects of use and social cue and their interaction were predicted.

Table 4.10. Estimates for Multilevel Logistic Models of Storage Performance LEVEL AND VARIABLE Model Null Level 1 model Dependent variable: Social cue recall Intercept 0.32** 0.35** (0.10) (0.11) Use Cue -0.02 (0.05) Social Cue 0.60*** (0.05) Social Cue x Use cue 0.11* (0.04) Variance components Within-person variance 1.125 1.365 Additional information ICC 0.529 -2Log likelihood (FIML) 2488.9 2355.6 N of estimated parameters 2 5 Pseudo R2 0 0.05 Dependent variable: Use cue recall Intercept -1.05*** -1.31*** (0.09) (0.12) Social Cue 1.003*** (0.09) Variance components Within-person variance 0.298 0.671 Additional information ICC 0.230 -2Log likelihood (FIML) 1139.88 995.06 N of estimated parameters 2 3 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

H5a predicted that food ads containing use cues would create poorer storage performance than those without use cues. When examining how the presence of use cues affected social cue recall, the results indicated that the addition of social cues significantly improved model fit than the null model (likelihood ratio test, Dχ2 = 133.35, d.f. = 3, p < 0.0001). See Table 4.10. The

main effect of use cue on social cue recall was not significant (z = -0.45, p = 0.65). Thus, the presence of use cue did not affect the storage of social cues in the food ads.

H5b predicted that food ads containing group cues would create poorer storage performance compared to those with individual cues. The predicted main effect of social cue on use cue recall (z = 10.62, p < 0.0001) was found to be significant. See Table 4.10. The odds of recalling use cues for the food ads with group cues was significantly higher (7.43 times as likely;

95% CI: 5.18 to 10.88 times) compared to those with individual cues. The main effect of social cue on social cue recall was found to be significant (z = 11.04, p < 0.0001). The odds of recalling social cues (an individual or a group of people) when doing cued recall for the food ads with group cues was significantly higher (3.31 times as likely; 95% CI: 2.68 to 4.12 times) compared to those with individual cues, which makes sense. In general, it was predicted that group social cues would harm storage, but these findings indicate that group cues in food ads aided storage.

H5c predicted that food ads containing both use and social cues would create poorer storage performance (i.e. increased cued-recall) than other types of food ads. The predicted interaction effect of use and social cues on social cue recall was found to be significant (z = 2.04, p = 0.041). The log odds ratio of storage performance of social cue information for the food ads with group and use cues were higher (2.03 as likely) compared to those with individual and use cues (b = 0.71, p = 0.006). However, there were no significant difference between the food ads with group and use cues and those with group cues without use cues (b = 0.08, p = 0.28), and between the food ads with individual and use cues and those with individual cues without use cues (b = -0.13, p = 0.07).

The H5 set of hypotheses were not supported. Individuals were more likely to have better storage performance of use and social cues for the food ads with group cues compared to those with individual cues.

The addition of “impulsive behavioral tendency” did not significantly improve model fit compared to the level models (likelihood ratio test, social cue recall data: Dχ2 = 0.75, d.f. = 1, p =

0.38; use cue recall data: Dχ2 = 0.57, d.f. = 1, p = 0.45). This model is not discussed further.

Heart Rate

Heart rate data were collected to index cognitive resources allocated to processing the messages across time. A 3-level model was used to predict heart rate changes during information processing of food advertising nested within individuals. Likelihood ratio tests were conducted, and the results indicated that that the addition of “time” suggested a better fitting model compared to the null model (likelihood ratio test, Dχ2 = 2129.7, d.f. = 1, p < 0.0001). Time was found to have a significant effect on heart rate change (b = -0.17, SE = 0.004, t(58610) = -46.57, p < 0.0001). The level-2 model with variables social cue, use cue, repetition, and time further improved the model fit compared to the level-1 model (likelihood ratio test, Dχ2 = 532.65, d.f. =

7, p < 0.0001).

Overall, it was predicted that individuals would pay more attention to process food ads with use cues than those without (H6a); food ads with group cues than those with individual cues

(H6b); and food ads with group and use cues compared to other types of food ads (H6c). Thus, the predicted effects are main effects of social cue, use cue, and their interactions with each other and with time.

The main effects of time (b = -0.17, SE = 0.02, t(174) = -9.32, p < 0.0001) and use cue (b

= -0.16, SE = 0.06, t(58610) = -2.35, p = 0.019) were found to be significant. Overall, individuals

exhibited heart rate deceleration across time. Further, they exhibited more deceleration when use cues were present in the food ads compared to when they were not. Further, the interaction effect of use cue and time was found to be significant (b = -0.01, SE = 0.004, t(58610) = -3.26, p =

0.001). Individuals exhibited significantly more heart rate deceleration across time when processing food ads with use cues than those without use cues, suggesting more attention being paid to process the food ads containing use cues (H6a). See Figure 4.13. Thus, H6a was supported.

Figure 4.13. Heart rate change as a function of time and use cue (absence (U-) vs. presence (U+))

The main effect of social cue on heart rate change was not significant (b = 0.07, SE =

0.06, t(58610) = 1.13, p = 0.26), but the predicted interaction effect of social cue and time was

(b = -0.04, SE = 0.004, t(58610) = -10.80, p < 0.0001). Individuals exhibited significantly more

heart rate deceleration when processing food ads with group cues than those with individual cues, suggesting more attention being paid to process the food ads containing group cues (H6b).

Figure 4.14. Heart rate change as a function of social cue (individual cue (S-) vs. group cue (S+)) and time

The interaction effect of time, social cue, and use cue on heart rate change scores (per second) was not significant (b = 0.002, SE = 0.004, t(58610) = 0.54, p = 0.59) (H6c), though the interaction of social and use cues (multilevel modeling, t = 2.03, p = 0.04) was found to be significant. This indicates that the slopes of HR varied by ad, but not by ad over time. Simple slopes were tested within each condition of the interaction. Ads with group and use cues had a steeper slope than those with group cues without use cues (b = -0.18, p < 0.0001) and individual cues with use cues (b = -0.35, p = 0.003). It was predicted that individuals would exhibit the greatest heart rate deceleration for the food ads with use and social cues, which was supported.

See Figure 4.15.

Figure 4.15. Heart rate change as a function of social cue (individual vs. group cues) and use cue (absence vs. presence) and time

Table 4.11. Estimates for Three-level Multilevel Models of Heart Rate

Model LEVEL AND VARIABLE Null Random Intercept Cross-level and Fixed Slope Interaction Level 1 Intercept -2.54 -0.14 -0.23* (0.33) (0.33) (0.10) Time -0.17*** -0.17*** (0.004) (0.02) Level 2 Use Cue -0.14* (0.06) Social Cue 0.07 (0.06) Repetition 0.04 (0.04) Social Cue x Use cue 0.12* (0.06) Cross-level interaction Use Cue x Time -0.01** (0.004) Social Cue x Time -0.04*** (0.004) Social Cue x Use Cue x Time 0.002 (0.004) Variance components Within-person variance 57.70 55.65 0.049 Intercept variance 17.13 17.13 Slope variance 58.26 Additional information ICC 0.229 -2Log likelihood (FIML) 405878 403748 403215 N of estimated parameters 3 4 11 Pseudo R2 0 0.027 0.034 Note* FIML: Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

The addition of “impulsive behavioral tendency” did not significantly improve model fit on heart rate data compared to the level models (likelihood ratio test, social cue recall data: Dχ2 =

0.0001, d.f. = 1, p = 0.99). This model is not discussed further.

Thus, the H6 set of hypotheses were partially supported. Individuals exhibited significantly less heart rate when viewing the food ads with use cues and the food ads with group cues; however, they did not exhibit significantly higher heart rate deceleration for the food ads with group and use cues compared to other types of messages.

Later Behavioral Intentions

This study also examined how different types of cues (i.e. social and use cues) influenced later behaviors including attitude toward the product, social support, behavioral control, and purchase intention. Self-reported data on these dependent variables collected from study 1 and study 2 were analyzed together.

Overall, it was predicted that individuals would exhibit more favorable attitudes toward the product, greater feelings of social support, greater behavioral intention to consume, and less perceived self-control toward the food products after viewing the food ads with use cues (H7a), the food ads with group cues (H7b), and the food ads with both use and group cues (H7c).

Results indicated that the addition of use and social cues significantly improved model fit over the null model for these dependent variables (likelihood ratio test, attitude toward the product: Dχ2 = 282.74, d.f. = 3, p < 0.0001; social support: Dχ2 = 76.08, d.f. = 3, p < 0.0001; purchase intention: Dχ2 = 193.8, d.f. = 3, p < 0.0001; behavioral control: Dχ2 = 282.74, d.f. = 3, p

< 0.0001).

Table 4.12. Estimates for Multilevel Models of Favorable Attitude toward the Product

LEVEL AND VARIABLE Model

Random Intercept Covariate Null and Fixed Slope Intercept 18.89 18.89*** 18.89*** (0.22) (0.23) (0.22) Use Cue 1.18*** 1.18*** (0.08) (0.08) Social Cue 0.53*** 0.53*** (0.08) (0.08) Social Cue x Use cue -0.05 -0.05 (0.08) (0.08) Covariates Impulsivity -0.07* (0.35) Variance components Within-person variance 24.15 22.32 22.32 Intercept variance 15.12 15.27 15.07 Slope variance Additional information ICC 0.385 -2Log likelihood (FIML) 24331 24048 24044 N of estimated parameters 3 6 7 Pseudo R2 0 0.04 0.05 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

The main effects of use (t = 15.63, p < 0.0001) and social cues (t = 7.03, p < 0.0001) on attitude toward the product were found to be significant. Individuals self-reported more favorable attitudes toward the product after viewing the food ads with use cues compared to those without

(H7a), and the food ads with group cues compared to those with individual cues (H7b). However, the interaction of use and social cues was not significant (t = -0.69, p = 0.49) (H7c). Further, the addition of impulsivity significantly improved model fit compared to the level 1 model

(likelihood ratio test, Dχ2 = 3.96, d.f. = 1, p < 0.046). See Table 4.12. Results indicated that, after accounting for impulsivity, the main effects that were found to be significant in the level 1 model

100

were still significant (RQ1). Further, impulsive behavioral tendency was found to have a significant effect on attitude toward product (t(327) = -2.00 , p = 0.046). Interestingly, individuals high in impulsive behavioral tendency self-reported less favorable attitude toward the advertised junk foods compared to those low in impulsivity.

Table 4.13. Estimates for Multilevel Models of Social support

Model LEVEL AND Random Intercept Covariate VARIABLE Null and Fixed Slope Intercept 3.43 3.43*** 3.43*** (0.07) (0.07) (0.07) Use Cue 0.17*** 0.17*** (0.02) (0.02) Social Cue 0.08*** 0.08*** (0.02) (0.02) Social Cue x Use cue 0.01 0.01 (0.02) (0.02) Covariates Impulsivity -0.03** (0.01) Variance components Within-person variance 1.829 1.791 1.791 Intercept variance 1.645 1.648 1.598 Additional information ICC 0.437 -2Log likelihood (FIML) 14312 14236 14227 N of estimated parameters 3 6 7 Pseudo R2 0 0.01 0.02 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

The main effects of use (t = 7.91, p < 0.0001) and social cues (multilevel modeling, t =

3.73, p < 0.0001) on social support were found to be significant. Food ads with use cues compared to those without (H6a) and food ads with group cues compared to those with individual cues (H7b) facilitated higher perceived social support. However, the interaction of use and social cues was not significant (t = 0.63, p = 0.53). Further, the addition of impulsivity

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significantly improved model fit compared to the level 1 model (likelihood ratio test, Dχ2 = 9.20, d.f. = 1, p = 0.002). See Table 4.13. Results indicated that, after accounting for impulsivity, the main effects that were found to be significant in the level 1 model were still significant (RQ1).

Further, impulsive behavioral tendency was found to have a significant effect on social support

(t(327) = -3.06, p = 0.002). Individuals high in impulsive behavioral tendency self-reported less social support toward the advertised junk foods compared to those low in impulsivity.

Table 4.14. Estimates for Multilevel Models of Purchase Intention Model LEVEL AND VARIABLE Random Intercept Covariate Null and Fixed Slope Intercept 8.42 8.42*** 8.42*** (0.13) (0.13) (0.13) Use Cue 0.60*** 0.60*** (0.05) (0.05) Social Cue 0.23*** 0.23*** (0.05) (0.05) Social Cue x Use cue 0.05 0.05 (0.05) (0.05) Covariates Impulsivity -0.07*** (0.02) Variance components Within-person variance 8.673 8.218 8.218 Intercept variance 4.619 4.657 4.443 Additional information ICC 0.348 -2Log likelihood (FIML) 20267 20073 20060 N of estimated parameters 3 6 7 Pseudo R2 0 0.03 0.05 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

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Table 4.15. Estimates for Multilevel Models of Behavioral Control Model LEVEL AND VARIABLE Random Intercept and Null Fixed Slope Intercept 16.03 16.03*** (0.12) (0.12) Use Cue 0.21*** (0.03) Social Cue 0.09* (0.03) Social Cue x Use cue 0.003 (0.03) Variance components Within-person variance 4.927 8.218 Intercept variance 3.989 4.657 Additional information ICC 0.447 -2Log likelihood (FIML) 18169 18128 N of estimated parameters 3 6 Pseudo R2 0 0.006 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. * p < 0.05, ** p < 0.01, *** p < 0.0001

The main effects of use (t = 13.14, p < 0.0001) and social cues (t = 5.05, p < 0.0001) on purchase intention were found to be significant. Individuals exhibited greater purchase intentions after viewing the food ads with use cues compared to those without, and the food ads with group cues compared to those with individual cues. However, the interaction of use and social cues was not significant (t = 1.02, p = 0.31). Further, the addition of impulsivity again significantly improved model fit compared to the level 1 model (likelihood ratio test, Dχ2 = 13.35, d.f. = 1, p <

0.0001). See Table 4.14. Results indicated that, after accounting for impulsivity, the main effects that were found to be significant in the level 1 model were still significant (RQ1). Further, impulsive behavioral tendency was found to have a significant effect on purchase intention

(t(327) = -3.06, p = 0.002). Individuals high in impulsive behavioral tendency self-reported greater toward the advertised junk foods compared to those low in impulsivity.

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The main effects of use (t = 5.98, p < 0.0001) and social cues (t = 2.43, p < 0.0001) were found to be significant on behavioral control. Results indicated that food ads with use cues compared to those without and food ads with group cues compared to those with individual cues facilitated higher self-perceived behavioral control. The findings appeared to be opposite to the predicted direction. It is important to note that the overall self-reported behavioral control was found to be very high (M = 16.03, SD = 2.99), suggesting individuals feel they had stronger behavioral control for all advertised food products. The predicted interaction of use and social cues was not significant (t = 0.09, p = 0.93). See Table 4.15.

Further, results indicated that the addition of impulsivity did not significantly improved model fit compared to the level 1 model (likelihood ratio test, Dχ2 = 0.11, d.f. = 1, p = 0.73).

Thus, the H7 set of hypotheses were partially supported. Food ads containing use cues and the food ads containing group cues elicited more favorable attitude toward the product, stronger perceived social support, and purchase intentions toward the advertised food products.

However, the interaction effects of social and use cues on these outcome predictors were not significant. Further, the findings regarding behavioral control were against predictions; individuals reported more behavioral control in response to use and social cues. Results also indicated that individuals high in impulsive behavioral tendency self-reported less favorable attitude toward the advertised product and less social support, but greater purchase intentions.

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CHAPTER 5

DISCUSSION

Overall, these two studies were developed to examine how different types of cues in food advertisements affect human motivational, emotional, cognitive responses, along with their later behavioral intentions toward the advertised food products. In general, this dissertation had three goals. The first was to test how different types of food cues affect automatic cognitive resource allocation toward encoding and storing food ad information. The second goal was to examine if the food ads containing multiple types of cues elicited stronger, additive, affective and cognitive responding (indexed by psychophysiology measurements) compared to other types of food advertisements. The third goal was to investigate how the presence of different types of food cues in food advertising encouraged buying tendencies toward the food products.

Goal 1: Cognitive processing of Use and Social cues

This dissertation examined the effects of cue-elicited appetitive motivational activation on the allocation of cognitive resources to encoding and storage of information in the food ads.

As predicted, viewers exhibited fewer available cognitive resources when processing the food ads containing both use and group cues. However, and interesting pattern emerged such that individuals exhibited the most available cognitive resources left when people in the food ads did not engage in eating behaviors. This suggests that the presence of more individuals creates an automatic call for cognitive resources that are only taken up when those individuals actually perform a biologically relevant behavior (e.g., eating).

This study also predicted that the presence of group and use cues would create a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, smaller value of sensitivity and more conservative criterion bias). However, there was only weak

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evidence for this set of predictions. Findings showed that individuals generally exhibited greater audio and visual hit rates in experiments; however, as recognition accuracy does not provide a reliable measure of encoding performance (Macmillan & Creelman, 1991), signal detection data were also examined. These data revealed no greater sensitivity for use or social cues and further suggested that the accuracy results were driven by liberal answering strategies of the participants.

Individuals were more willing to say yes in general, but especially when the food ads contained use and group and use cues. According to Fox (2004), a more liberal criterion bias can reflect recognizing information that wasn’t seen before as the information they had seen previously, which seems quite likely in this case. All information seemed more familiar to the participants, especially when it contained the appetitive use and social cues. One notable exception was audio encoding performance. Individuals exhibited higher hit rates, more audio sensitivity, and a more conservative criterion bias when making decisions for the food ads with group cues compared to those with individual cues in the audio recognition test. Taken with the STRT data, these data indicate that group cues elicit greater calls for resource allocations, that aren’t automatically used up encoding those group cues, but rather are only spent when those groups are performing motivationally relevant behaviors.

This explanation is further supported by the cued recall and heart rate data which also indicated that the messages containing group cues, especially the messages containing group and use cues, created better storage performance and greater attentional resource allocation. In particular, the presence of group cues created better storage performance of social and use cues in the food ads. Heart rate deceleration was greatest when food ads contained both group and use cues compared to other types of messages. Thus, overall, it seems that the presence of group cues creates allocation of resources while the presence of use cues creates a draw on resources

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(resources required). This is in line with an LC4MP (A Lang, 2006) view as well as a Dynamic

Human-Centered Communication Systems Theory (DHCCST) (A. Lang, 2014) view, which both suggest that humans automatically allocate attentional resources to biologically relevant information that will increase their survival rates, though a DHCCST approach better explains the finding that the presence of more people in the ads doesn’t actually take up all the resources that it allocates unless they are eating. Instead, it seems these encoding resources are held in reserve for their use when these animate objects offer more complicated affordances (Bailey &

Lang, under review; A. Lang, 2014; A. Lang & Bailey, 2015).

Goal 2: Examining Additive Appetitive Responses of Use and Social Cues

The second goal of this study was to examine whether use and social cues created additive appetitive responses indexed via psychophysiological and self-report metrics indexing motivational and emotional response. It was predicted that the addition of use and group cues to the food ads would elicit greater OO activation and SCL, and less CR activation. As predicted, the findings of this study identified the additive effects of use and social cues on appetitive motivational activation across the board. Viewers exhibited more positive and less negative emotional feelings, and were more sympathetically aroused during the food ads with use cues and the food ads with social cues. These findings are consistent with previous social eating research done by Bailey and her colleagues (Bailey et al., under review). They found that obesity prevention PSAs with social eating cues (combined use and group cues) elicited more positive and less negative emotional feelings compared to the PSAs with individual and use cues

(combined use and individual cues).

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Goal 3: Later Behavioral Intentions

The third goal of this dissertation was to explore how different types of cues affected later behavioral intentions. The food ads with use cues and those with group cues were predicted to elicit more favorable attitude toward the product, greater perceived social support and purchase intentions, and less behavioral control toward the advertised food products due to their appetitive influences. As predicted, the results suggest that the food ads containing use cues and the food ads containing group cues elicit more favorable attitude toward the product, stronger perceived social support, and purchase intentions. These findings indicate that when food products were paired with use cues and social cues, individuals exhibited greater appetitive motivational activation associated with stronger approach tendencies which influenced emotions and behavioral intentions.

Interestingly, individuals also self-reported greater behavioral control toward the advertised food products after viewing the food ads with use cues and the food ads with group cues. It’s worth mentioning that the overall ratings of self-reported behavioral control toward all advertised food products were very high. In part, this may have something to do with food not actually being present, as previous research has indicated that cue availability can alter behaviors

(Carter & Tiffany, 2001). However, it may also have to do with appetitive motivation leading to positive emotion. As in the Bailey, Liu, & Wang (under review) study, individuals reported greater self-efficacy to eat in healthy ways but lesser actual intention to do so. Therefore, just as in this previous work, individuals may feel more positive and better able to do the “appropriate” thing, but are nonetheless, still less likely to do it.

Lastly, it was expected that individual differences in impulsive behavioral intentions would influence responses to food advertisements, based on previous research which has

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identified its positive association with elevated food cue reactivity (e.g., Claes et al., 2005;

Fischer & Smith, 2008; Hou et al., 2011; Strimas et al., 2008). However, the addition of impulsive behavioral intentions rarely improved model fits. One possible reason is this study does not have enough statistical power to evaluate the influences of impulsive behavioral tendency, especially when effect sizes were very small (e.g., psychophysiological data).

However, in a few important circumstances, impulsivity did improve model fit. More impulsive individuals tended to exhibit less favorable food attitudes and feelings of social support, but greater purchase intentions for the food ads with use and social cues compared to those low in impulsivity. This indicates that, though they recognized these types of food were unhealthy and people around them would not encourage them to consume, they still reported higher ratings of purchase intentions for these fast foods. The findings in the study generally were consistent with previous research (e.g., Claes et al., 2005; Fischer & Smith, 2008; Hou et al., 2011; Strimas et al., 2008). Thus, high impulsivity individuals are sensitive to different types of appetitive cues resulting in elevated food cue reactivity associated with attentional bias, stronger urge to eat, and craving tendencies (Hou et al., 2011; Strimas et al., 2008).

Limitations of This Dissertation

Although this dissertation advances the understanding of how cue-elicited motivational activation influences information processing and later behavioral intentions in a food advertising context, it is not without its limitations. First, this dissertation only examined how individuals process the advertisements of convenient, unhealthy, and high energy-dense food products (i.e., fast foods) from a cue reactivity perspective. Future research should apply similar methods to studying the effects of use and social cues in a larger variety of food advertising contexts (e.g., less energy dense, hedonically motivating foods).

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Second, as discussed above, this study does not have enough statistical power to make inferences about the effects of impulsivity based on the current dataset. Not enough data could be collected in the time allotted to satisfy this requirement. Because previous research has shown the effect of impulsivity on food cue reactivity and suggests that greater impulsive behavioral tendency creates vulnerability to display food addictive-like eating behaviors (e.g., Claes et al.,

2005; Fischer & Smith, 2008; Hou et al., 2011; Strimas et al., 2008), future research should follow through in this investigation.

An additional limitation concerns the generalizability of the sample. Though this dissertation used two experiments, which are concerned with generalizing to circumstances rather than groups of individuals, the sample consisted primarily of Caucasian college students, indicating the results may not be very well generalized to more diverse samples placed in similar circumstances. This college-aged sample may consume the advertised convenient, unhealthy, and high energy-dense food products more frequently compared to other individuals. Future research should consider including larger and more diverse samples to replicate the findings.

Despite these limitations, this dissertation has contributed meaningful and important information about the processing of concurrently presented types of appetitive cues in a food advertising context. Theoretical and practical implications are discussed as below.

Theoretical Implications

In general, the findings of this dissertation advance and deepen the understanding of what different types of food cues in food advertisements are creating in terms of motivational activation, emotional, motivated cognition, and behavioral tendencies. Specifically, though researchers in the field of substance addiction research have already categorized substance cues into different types and examined individual cue-reactivity toward these different types of cues,

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few research studies examined how the presence of different types of food cues in food ads affect motivational responses and information processing of media contents. Results of this dissertation offer evidence that the addition of use and social cues to food ads elicit stronger appetitive motivational activation that yield greater cognitive efforts, arousal, and positive emotional feelings, and approach responses.

The findings of this study, which are in line with the Limited Capacity Model of

Motivated Mediated Message Processing (LC4MP; Lang, 2006) view as well as a Dynamic

Human-Centered Communication Systems Theory (DHCCST) (A. Lang, 2014) view, add to important literatures focused on understanding how different types of food cues (i.e., social and use cues) in food related media messages (e.g. food ads, obesity prevention PSAs) influence emotional and behavioral responses and how these messages are cognitively processed and later remembered. Previous research using the LC4MP (A. Lang, 2006a, 2006b, 2009) as a framework has explored how different types of primary biological motivators (e.g., food, sex, danger) in mediated contexts guide information processing, emotional reactions and behaviors (e.g., Bailey,

2015, 2017; Clayton, Leshner, Tomko, Trull, & Piasecki; Clayton, Leshner, Bolls, & Thorson,

2017; Keene & Lang, 2006). Appetitive primary biological motivators (such as direct food cues) can trigger stronger positive emotional feelings and more cognitive resources allocated to encoding and storage (Bailey, 2015, 2017). Building upon previous knowledge, this dissertation examined two more nuanced types of cues (i.e., social and use cues). The results of this dissertation indicated that the presence of group social cues and use cues in food ads elicited stronger appetitive motivational activation and encouraged greater approach behavioral tendencies, as was predicted. Thus, the findings from this dissertation offer greater support to the

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LC4MP predictions, which suggest that individuals allocate more attentional resources to biologically relevant information that are considered primary motivators or reinforcers.

This dissertation also offers evidence for the DHCCST predictions. In alignment with previous literature operating from DHCCST (Bailey & Lang, under review; A. Lang, 2014; A.

Lang & Bailey, 2015), human beings are evolved to directly perceived the affordances available and optimize their behaviors to maximize goal achievement while expending as few resources as possible. The results from this dissertation supported this type of optimization of cognitive processing. Individuals allocated resources automatically to processing greater numbers of individuals, which would present greater uncertainty and multiple affordances themselves (see

Bailey & Lang, under review). However, in this context, those resources were not actually used to encode the food ads with multiple individuals (group cues) unless those depicted individuals were eating (the presence of both group and use cues). This makes sense as the encoding of the biologically relevant opportunity for later use is the most important element to later recall, or is as Bailey and Lang (under review) argue, the most “representation hungry.”

The primary theoretical importance of this study rests on its indication that the combination of use and group cues offers a different affordance (Gibson, 1986) than either alone, which elicits pronounced appetitive physiological responses resulting in greater cognitive efforts to process these appetitive cues and different behavioral intentions. Cognitive processing results indicated that the addition of use cues has more impact on resources required of a message while social cues have more impact on resources allocated. However, it seems that individuals attend to the appetitive motivational information during food ads with use and group cues rather than encode them for later retrieval. These findings offer more evidence for DHCCST’s propositions, which suggest that individuals pay selective attentional resources to process the cues in the

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environment, but they are likely to feel the information is less important to encode because the visual information is relatively stable in the environment. Therefore, individuals show greater cognitive effort to process group and use cues, but they will not spend additional energy to encode these message contents.

From a cue reactivity perspective, findings from this dissertation suggest that the presence of both social or use cues in food ads are effective elicitors to greater appetitive motivational activation. Further, appetitive motivational responses were especially stronger during food ads with both use and social cues. Taken together, these findings indicate that concurrently presented social and use cues in food ads trigger greater food cue reactivity compared to food ads with only one type of cue (either use or social cue). This has important implications for cue reactivity research as limited research has studied how the addition of use cues to social contexts alter the dynamics of motivated information processing. As argued previously, the combination of social and use cues provides multiple types of affordance, creating elevated food cue reactivity. It is important to create a better understanding of the effects of concurrently presented social and use cues because both food related human interaction and mediated information processing often have social cues embedded with other biologically important cues (such as use cues).

Another theoretical contribution of this dissertation is to offer more evidence to support the influences of social support on eating behaviors. Social support has been constantly considered as an important factor to elicit desired behavioral changes in health communication theories, such as Social Modeling Theory (Bandura, 1977), Theory of Planned Behavior (Ajzen,

1985, 1991), and Social Cognitive theory (Bandura, 2001). In alignment with the previous binge eating research (e.g., Avena & Gold, 2011; Berge, Wall, Larson, Eisenberg, Loth, & Neumark-

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Sztainer, 2014; Crandall, 1988; Cohen, 2008; Field, Camargo, Taylor, Berkey, Roberts, &

Colditz, 2001; Fletcher, Bonell, & Sorhaindo, 2011; Harris et al., 2009; Liberman, Gauvin,

Bukowski, & White, 2001), the findings from this dissertation further confirmed the effects of social support on food intake behavioral intentions. The findings also indicate that, the influences of social cues can be strengthened when adding use cues information into social contexts.

Practical Implications

Findings of this dissertation also contribute to the understanding of media message design.

Overall, this dissertation offers greater systematic knowledge to both the media industry and health professionals with regard to the effects of use and social cues in food advertising. The addition of use and group cues to food ads will help hold consumers’ attention and increase their purchase intentions. Therefore, message designers may consider embedding group and use cues into food advertisements to achieve their advertising goals.

However, the findings from this dissertation also offer suggestions for health professionals trying to create effective health eating interventions. If the goal is to educate people to avoid consuming to eat unhealthy foods, the addition of use and group cues depicting how people consume unhealthy food may be discouraging positive behavior changes. If the goal is to encourage the consumption of healthy foods, the addition of use and group cues to healthy food ads may be useful, though more research is needed to confirm these influences.

In conclusion, this dissertation identifies the possible persuasive effects of use and social cues in food ads. While previous studies have confirmed the effects of food cues on appetitive motivational activation, this dissertation was conducted to examine individual reactivity toward the presence and co-presence of different types of cues in food ads. The findings add to multiple theoretical literatures and also build conceptual knowledge that health practitioners and message

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designers to understand how particular cues elicit motivational reactions and to employ them consciously to encourage their desired outcomes.

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REFERENCES

Abrams, D. B., Monti, P. M., Carey, K. B., Pinto, R. P., & Jacobus, S. I. (1988). Reactivity to

smoking cues and relapse: two studies of discriminant validity. Behaviour research and

therapy, 26, 225-233.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp.

11-39). Springer Berlin Heidelberg.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision

processes, 50, 179-211.

Alkharfy, K. M. (2011). Food advertisements: to ban or not to ban?. Annals of Saudi

medicine, 31, 567.

Avena, N. M., & Gold, M. S. (2011). Food and addiction–sugars, fats and hedonic

overeating. Addiction, 106, 1214-1215.

Appelbaum, U., & Halliburton, C. (1993). How to develop international advertising campaigns

that work: the example of the European food and beverage sector. International Journal

of Advertising, 12, 223-241.

Bailey, R. L. (2015). Processing food advertisements: Initial biological responses

matter. Communication Monographs, 82, 163-178.

Bailey, R. L. (2016). Modern foraging: Presence of food and energy density influence

motivational processing of food advertisements. Appetite, 107, 568-574.

Bailey, R. L. (2017). Influencing Eating Choices: Biological Food Cues in Advertising and

Packaging Alter Trajectories of Decision Making and Behavior. Health communication,

32, 1183-1191.

Bailey, R. L., Liu, J., Wang, T., & Kaiser, C. (2016). Moderators of optimal foraging in a

116

modern context: Processing sex and food stimuli in the dark. Society for

Psychophysiological Research. Psychophysiology, 53, S70.

Bailey, R. L., Liu, J., & Wang, T. (under review). Social eating cues in obesity prevention fear

appeals create positive affect but inhibit healthy eating intentions. Submitted to Health

Communication.

Bailey, R., & Muldrow, A. (2018). Healthy Food Identification: Food Cues and Claims Affect

Speeded and Thoughtful Evaluations of Food. Health communication.

Bailey, R. L., Wang, T., & Kaiser, C. K. (2018). Clash of the Primary Motivations: Motivated

Processing of Emotionally Experienced Content in Fear Appeals About Obesity

Prevention. Health communication, 33, 111-121.

Bailey, R. L. Lang, A. (under review). The importance of being animiate.

Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological

review, 84, 191.

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual review of

psychology, 52, 1-26.

Beer, R. D. (2014). Dynamical systems and embedded cognition. The Cambridge handbook of

artificial intelligence, 812, 856-873.

Berntson, G. G., Thomas Bigger, J., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik,

M., … Van Der Molen, M. W. (1997). Heart rate variability: Origins, methods, and

interpretive caveats. Psychophysiology, 34, 623-648.

Berge, J. M., Wall, M., Larson, N., Eisenberg, M. E., Loth, K. A., & Neumark-Sztainer, D.

(2014). The unique and additive associations of family functioning and parenting practices

117

with disordered eating behaviors in diverse adolescents. Journal of behavioral medicine, 37,

205-217.

Bolls, P. D., Lang, A., & Potter, R. F. (2001). The effects of message valence and listener arousal

on attention, memory, and facial muscular responses to radio

advertisements. Communication Research, 28, 627-651.

Borzekowski, D. L., & Robinson, T. N. (2001). The 30-second effect: an experiment revealing

the impact of television commercials on food preferences of preschoolers. Journal of the

American Dietetic Association, 101, 42-46.

Boseley, S. (2016, February). Curbs on junk food ads NO 1 priority in fighting childhood obesity,

says study. Retrieved from https://www.theguardian.com/society/2016/nov/12/stop-junk-

food-advertising-curb-childhood-obesity-experts

Bradley, M. M., Codispoti, M., Cuthbert, B. N., & Lang, P. J. (2001). Emotion and motivation I:

defensive and appetitive reactions in picture processing. Emotion, 1, 276.

Bredahl, L. (2001). Determinants of consumer attitudes and purchase intentions with regard to

genetically modified food–results of a cross-national survey. Journal of consumer

policy, 24, 23-61.

Buijzen, M. (2003). Television advertising aimed at children: Intended and unintended

effects (Doctoral dissertation, Amsterdam School of Communications Research, ASCoR).

Buijzen, M., & Valkenburg, P. M. (2002). Appeals in television advertising: A content analysis

of commercials aimed at children and teenagers. Communications, 27, 349-364.

Carter, B. L., & Tiffany, S. T. (1999). Meta-analysis of cue-reactivity in addiction

research. Addiction, 94, 327-340.

Cawley, J., Meyerhoefer, C. (2012). The Medical Care Costs of Obesity: An Instrumental

118

Variables Approach. Journal of Health Economics, 31, 219-230.

Cacioppo, J. T., & Berntson, G. G. (1994). Relationship between attitudes and evaluative space:

A critical review, with emphasis on the separability of positive and negative substrates.

Psychological bulletin, 115, 401.

Cacioppo, J. T., & Berntson, G. G. (1999). The affect system architecture and operating

characteristics. Current directions in psychological science, 8, 133-137.

Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1997). Beyond bipolar conceptualizations

and measures: The case of attitudes and evaluative space. Personality and Social

Psychology Review, 1, 3-25.

Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1999). The affect system has parallel and

integrative processing components: Form follows function. Journal of Personality and

Social Psychology, 76, 839.

Cacioppo, J. T., Tassinary, L. G., & Fridlund, A. J. (1990). The skeletomotor system. In J. T.

Cacioppo & L. G. Tassinary (Eds.), Principles of psychophysiology: Physical, social, and

inferential elements., (pp. 325-384). New York, NY, US: Cambridge University Press.

Center for Informed Food Choices. (2005). Food marketing to children and the law. Retrieved

from: http://www.informedeating.org/events.html.

Chao, A., Grilo, C. M., White, M. A., & Sinha, R. (2014). Food cravings, food intake, and

weight status in a community-based sample. Eating behaviors, 15, 478-482.

Chester, J., & Montgomery, K. (2011). Digital Food Marketing to Children and Adolescents.

Cheong, Y., Kim, K., & Zheng, L. (2010). Advertising appeals as a reflection of culture: a cross-

cultural analysis of food advertising appeals in China and the US. Asian Journal of

Communication, 20, 1-16.

119

Claes, L., Vandereycken, W., & Vertommen, H. (2005). Impulsivity-related traits in eating

disorder patients. Personality and Individual differences, 39, 739-749.

Clayton, R. B., Leshner, G., Tomko, R. L., Trull, T. J., & Piasecki, T. M. (2017). Countering

Craving with Disgust Images: Examining Nicotine Withdrawn Smokers’ Motivated

Message Processing of Anti-Tobacco Public Service Announcements. Journal of Health

Communication, 22, 254-261.

Clayton, R. B., Leshner, G., Bolls, P. D., & Thorson, E. (2017). Discard the smoking cues—keep

the disgust: An investigation of tobacco smokers’ motivated processing of anti-tobacco

commercials. Health communication, 32, 1319-1330.

Clendenen, V. I., Herman, C. P., & Polivy, J. (1994). Social facilitation of eating among friends

and strangers. Appetite, 23, 1-13.

Cohen, D. A. (2008). Obesity and the built environment: changes in environmental cues cause

energy imbalances. International Journal of Obesity, 32, S137-S142.

Craft, B. B., & Kathleen, M., & Lustyk, B. (2013). Tolerance and Withdrawal. In Miller, P, M.

(Ed.), Principles of addiction: comprehensive addictive behaviors and disorders,

Academic Press.

Crandall, C. S. (1988). Social contagion of binge eating. Journal of personality and social

psychology, 55, 588.

Crespo, C. J., Smit, E., Troiano, R. P., Bartlett, S. J., Macera, C. A., & Andersen, R. E. (2001).

Television watching, energy intake, and obesity in US children: results from the third

National Health and Nutrition Examination Survey, 1988-1994. Archives of pediatrics &

adolescent medicine, 155, 360-365.

Cullen, K. W., Baranowski, T., Rittenberry, L., & Olvera, N. (2000). Social–environmental

120

influences on children's diets: results from focus groups with African-, Euro-and

Mexican-American children and their parents. Health Education Research, 15, 581-590.

Davison, K. K., Marshall, S. J., & Birch, L. L. (2006). Cross-sectional and longitudinal

associations between TV viewing and girls’ body mass index, overweight status, and

percentage of body fat. The Journal of pediatrics, 149, 32-37.

De Castro, J. M. (1990). Social facilitation of duration and size but not rate of the spontaneous

meal intake of humans. Physiology & Behavior, 47, 1129-1135.

Drummond, D. C., Tiffany, S., Glautier, S. P. & Remington, B. (1995). Cue exposure in under-

standing and treating addictive behaviours, in: DRUMMOND, D. C., TIFFANY, S. T.,

GLAUTIER S. P. & REMINGTON, B. (Eds) Addictive Behaviour: cue exposure theory

and practice, pp. 1–17 (Chichester, John Wiley & Sons).

Drummond, D. C. (2000). What does cue-reactivity have to offer clinical research? Addiction, 95,

S129-S144.

Duffey, K. J., Gordon-Larsen, P., Jacobs, D. R., Williams, O. D., & Popkin, B. M. (2007).

Differential associations of fast food and restaurant food consumption with 3-y change in

body mass index: the Coronary Artery Risk Development in Young Adults Study. The

American journal of clinical nutrition, 85, 201-208.

Ekman, P., Davidson, R. J., & Friesen, W. V. (1990). The Duchenne smile: Emotional

expression and brain physiology II. Journal of Personality & Social Psychology, 58,

342–353.

Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical

power analysis program for the social, behavioral, and biomedical sciences. Behavior

research methods, 39, 175-191.

121

Field, A. E., Camargo, C. A., Taylor, C. B., Berkey, C. S., Roberts, S. B., & Colditz, G. A.

(2001). Peer, parent, and media influences on the development of weight concerns and

frequent dieting among preadolescent and adolescent girls and boys. Pediatrics, 107, 54-

60.

Finkelstein, E. A., Trogdon, J. G., Cohen, J. W., & Dietz, W. (2009). Annual medical spending

attributable to obesity: payer-and service-specific estimates. Health affairs, 28, w822-

w831.

Fischer, S., & Smith, G. T. (2008). Binge eating, problem drinking, and pathological gambling:

Linking behavior to shared traits and social learning. Personality and individual

Differences, 44, 789-800.

Fletcher, A., Bonell, C., & Sorhaindo, A. (2011). You are what your friends eat: systematic

review of social network analyses of young people's eating behaviours and

bodyweight. Journal of Epidemiology & Community Health, 65, 548-555.

Fox, J. R. (2004). A signal detection analysis of audio/video redundancy effects in television

news video. Communication Research, 31, 524-536.

Fox, J. R., Park, B., & Lang, A. (2007). When available resources become negative resources the

effects of cognitive overload on memory sensitivity and criterion bias. Communication

Research, 34, 277-296.

Fridlund, A. J., & Cacioppo, J. T. (1986). Guidelines for human electromyographic research.

Psychophysiology, 23, 567-589.

Frijda, N. H. (1988). The laws of emotion. American Psychologist, 43, 349 –358.

Frijda, N. H. (1992). The empirical status of the laws of emotion. Cognition & Emotion, 6, 467–

477.

122

Gearhardt, A. N., Phil, M., White, M. A., & Potenza, M. N. (2011). Binge Eating Disorder and

Food Addiction. Current Drug Abuse Reviews, 4, 201-207.

Gibbons, R. D., Hedeker, D., & DuToit, S. (2010). Advances in analysis of longitudinal

data. Annual review of clinical psychology, 6, 79-107.

Gibson, J. J. (1986). The ecological approach to visual perception. Hillsdale, HJ: Erlbaum.

(Original work published 1979)

Greenwald, M. K., Cook, E. W., & Lang, P. J. (1989). Affective judgment and

psychophysiological response: Dimensional covariation in the evaluation of pictorial

stimuli. Journal of psychophysiology, 3, 51-64.

Harrar, V., Toepel, U., Murray, M. M., & Spence, C. (2011). Food’s visually perceived fat

content affects discrimination speed in an orthogonal spatial task. Experimental brain

research, 214, 351-356.

Harris, J. L., Bargh, J. A., & Brownell, K. D. (2009). Priming effects of television food

advertising on eating behavior. Health psychology, 28, 404.

Heise, D. R. (1970). The semantic differential and attitude research. In Attitude measurement,

Gene F. Summers, ed. Chicago: Rand McNally & Co., 235-253.

Holt, D. J., Ippolito, P. M., Desrochers, D. M., & Kelley, C. R. (2007). Children's Exposure to

TV Advertising in 1977 and 2004: Information for the Obesity Debate. Retrieved from:

https://www.ftc.gov/sites/default/files/attachments/educational-materials/cabecolor.pdf

Hull, C. (1943). Principles of Behavior. New York: Appleton-Century.

Hume, Scott. (2014, March 30). McDonald's spent more than $988 million on advertising in

2013. Retrieved from http://www.csmonitor.com/Business/The-

Bite/2014/0330/McDonald-s-spent-more-than-988-million-on-advertising-in-2013

123

Jansen, A. (1998). A learning model of binge eating: cue reactivity and cue exposure. Behaviour

research and therapy, 36, 257-272.

Jarvis, B. G. (2014). MediaLab Research Software (Version 2014). New York: Empirisoft.

Jennings, J. R., Bberg, W. K., Hutcheson, J. S., Obrist, P., Porges, S., & Turpin, G.

(1981). Publication guidelines for heart rate studies in man. Psychophysiology, 18, 226-

231.

Jensen, M. D., Ryan, D. H., Apovian, C. M., Ard, J. D., Comuzzie, A. G., Donato, K. A., Hu, F.

B., Hubbard, V. S., Jakicic, J. M., Kushner, R. F., Loria, C. M., Millen, B. E., Nonas, C.

A., Pi-Sunyer, F. X., Stevens, J., Stevens, V., Wadden, T. A., Wolfe, B. M., & Yanovski,

S. Z. (2014). 2013 AHA/ACC/TOS Guideline for the Management of Overweight and

Obesity in Adults. Circulation, 129, S102-S138.

John, D. R. (1999). Consumer socialization of children: A retrospective look at twenty-five years

of research. Journal of consumer research, 26, 183-213.

Kamins, M. A., & Marks, L. J. (1987). Advertising puffery: The impact of using two-sided

claims on product attitude and purchase intention. Journal of Advertising, 16, 6-15.

Kang, Y., Cappella, J. N., Strasser, A. A., & Lerman, C. (2009). The effect of smoking cues in

antismoking advertisements on smoking urge and psychophysiological reactions.

Nicotine & Tobacco Research, 11, 254– 261.

Keene, J. R., & Lang, A. (2016). Dynamic motivated processing of emotional trajectories in

public service announcements. Communication Monographs, 83, 468-485.

Kelley, A. E., & Berridge, K. C. (2002). The neuroscience of natural rewards: relevance to

addictive drugs. Journal of Neuroscience, 22, 3306-3311.

Kunkel, D., & Gantz, W. (1992). Children’s television advertising in the multichannel

124

environment. Journal of Communication, 42, 134-152.

Lappalainen, R., Saba, A., Holm, L., Mykkanen, H., Gibney, M. J., & Moles, A. (1997).

Difficulties in trying to eat healthier: descriptive analysis of perceived barriers for healthy

eating. European journal of clinical nutrition, 51, S36.

Lang, A. (Ed.). (1994). Measuring psychological responses to media. NJ: Lawrence Erlbaum

Assoc.

Lang, A. (2000). The limited capacity model of mediated message processing. Journal of

communication, 50, 46-70.

Lang, A. (2006a). Using the limited capacity model of motivated mediated message processing

to design effective cancer communication messages. Journal of Communication, 56, 557-

580.

Lang, A. (2006b). Motivated cognition (LC4MP): The influence of appetitive and aversive

activation on the processing of video games. In P. Messarsis & L. Humphries (Eds.),

Digital media: Transformation in human communication (pp. 237–256). New York: Peter

Lang Publishing.

Lang, A. (2009). The limited capacity model of motivated mediated message processing. In R. L.

Nabi & M. B. Oliver (Eds.), The Sage handbook of media processes and effects (pp. 99–

112). Los Angeles, CA: Sage.

Lang, A. (2014). Dynamic human-centered communication systems theory. The Information

Society, 30, 60-70.

Lang, A., & Bailey, R. L. (2015). Understanding information selection and encoding from a

dynamic, energy saving, evolved, embodied, embedded perspective. Human

Communication Research, 41, 1-20.

125

Lang, A., Bailey, R. L., & Connolly, S. R. (2015). Encoding systems and evolved message

processing: Pictures enable action, words enable thinking. Media and Communication, 3,

34-43.

Lang, A., & Basil, M. (1998). What do secondary task reaction times measure anyway? In M.

Roloff (Ed.), Communication Yearbook (Vol. 21, pp. 443–470). Beverly Hills, CA: Sage.

Lang, A., Bradley, S. D., Park, B., Shin, M., & Chung, Y. (2006). Parsing the resource pie:

Using STRTs to measure attention to mediated messages. Media Psychology, 8, 369–394.

Lang, A., Bradley, S. D., Sparks Jr, J. V., & Lee, S. (2007). The motivation activation measure

(MAM): How well does MAM predict individual differences in physiological indicators

of appetitive and aversive activation?. Communication Methods and Measures, 1, 113-

136.

Lang, A., Bolls, P., Potter, R. F., & Kawahara, K. (1999). The effects of production pacing and

arousing content on the information processing of television messages. Journal of

Broadcasting & Electronic Media, 43, 451-475.

Lang, A., Sanders-Jackson, A., Wang, Z., & Rubenking, B. (2013). Motivated message

processing: How motivational activation influences resource allocation, encoding, and

storage of TV messages. Motivation and Emotion, 37, 508-517.

Lang, A., Park, B., Sanders-Jackson, A. N., Wilson, B. D., & Wang, Z. (2007). Cognition and

emotion in TV message processing: How valence, arousing content, structural complexity,

and information density affect the availability of cognitive resources. Media

Psychology, 10, 317-338.

Lang, A., Shin, M., & Lee, S. (2005). Sensation seeking, motivation, and substance use: A dual

system approach. Media Psychology, 7, 1-29.

126

Lang, A., & Yegiyan, N. S. (2008). Understanding the interactive effects of emotional appeal

and claim strength in health messages. Journal of Broadcasting & Electronic Media, 52,

432-447.

Lang, P. J., Greenwald, M. K., Bradley, M. M., & Hamm, A. O. (1993). Looking at pictures:

Affective, facial, visceral, and behavioral reactions. Psychophysiology, 30, 261-273.

Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). International affective picture system

(IAPS): Technical manual and affective ratings. NIMH Center for the Study of Emotion

and Attention, 39-58.

Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1999). International affective picture system

(IAPS): Technical manual and affective ratings. Gainesville, FL: The Center for

Research in Psychophysiology, University of Florida.

Larsen, J. K., Hermans, R. C., & Engels, R. C. (2012). Food intake in response to food-cue

exposure. Examining the influence of duration of the cue exposure and trait

impulsivity. Appetite, 58, 907-913.

Lawrence, N. S., Hinton, E. C., Parkinson, J. A., & Lawrence, A. D. (2012). Nucleus accumbens

response to food cues predicts subsequent snack consumption in women and increased

body mass index in those with reduced self-control. Neuroimage, 63, 415-422.

Lee, S., & Cappella, J. N. (2013). Distraction effects of smoking cues in antismoking messages:

examining resource allocation to message processing as a function of smoking cues and

argument strength. Media psychology, 16, 154-176.

Lee, S., Cappella, J. N., Lerman, C., & Strasser, A. A. (2011). Smoking cues, argument strength,

and perceived effectiveness of antismoking PSAs. Nicotine & Tobacco Research, 13,

282-290.

127

Lee, S., Cappella, J., Lerman, C., & Strasser, A. (2013). Effects of smoking cues and argument

strength of antismoking advertisements on former smokers’ self-efficacy, attitude, and

intention to refrain from smoking. Nicotine & Tobacco Research, 15, 527–533.

Lee, S., & Lang, A. (2015). Redefining media content and structure in terms of available resources:

Toward a dynamic human centric theory of communication. Communication Research, 42,

599-625.

Leshner, G., & Cheng, I. H. (2009). The effects of frame, appeal, and outcome extremity of

antismoking messages on cognitive processing. Health Communication, 24, 219-227.

Lieberman, M., Gauvin, L., Bukowski, W. M., & White, D. R. (2001). Interpersonal influence

and disordered eating behaviors in adolescent girls: The role of peer modeling, social

reinforcement, and body-related teasing. Eating behaviors, 2, 215-236.

Limbert, C. (2010). Perceptions of social support and eating disorder characteristics. Health Care

for Women International, 31, 170-178.

Liu, J., & Bailey, R. L. (2018). Effects of substance cues in negative public service announcements on

cognitive processing. Health Communication.

Livingstone, S., & Helsper, E. J. (2006). Does advertising literacy mediate the effects of

advertising on children? A critical examination of two linked research literatures in

relation to obesity and food choice. Journal of communication, 56, 560-584.

Lobstein, T., & Dibb, S. (2005). Evidence of a possible link between obesogenic food

advertising and child overweight. Obesity reviews, 6, 203-208.

LoDolce, M. (2015). Food Marketing to Children: A Wolf in Sheep’s Clothing? Retrieved from:

http://www.obesityaction.org/wp-content/uploads/Food-Marketing-and-Children-

Summer-2015.pdf

128

Lowe, M. R., & Butryn, M. L. (2007). Hedonic hunger: a new dimension of

appetite?. Physiology & behavior, 91, 432-439.

Lubman, D. I., Peters, L. A., Mogg, K., Bradley, B. P., & Deakin, J. F. W. (2000). Attentional

bias for drug cues in opiate dependence. Psychological medicine, 30, 169-175.

Lupton, D. (1996). Food, the body and the self. London: Sage.

MacKillop, J., Miranda Jr, R., Monti, P. M., Ray, L. A., Murphy, J. G., Rohsenow, D. J., ... &

Gwaltney, C. J. (2010). Alcohol demand, delayed reward discounting, and craving in

relation to drinking and alcohol use disorders. Journal of abnormal psychology, 119, 106.

MacMillan, N., & Creelman, C. (1991). Detection theory: A user’s guide. Cam- bridge, UK:

Cambridge University Press.

Meule, A. (2011). How prevalent is “food addiction”?. Frontiers in psychiatry, 2, 61.

Michie, S., Abraham, C., Whittington, C., McAteer, J., & Gupta, S. (2009). Effective techniques

in healthy eating and physical activity interventions: a meta-regression. Health

Psychology, 28, 690-701.

Montgomery, K., & Chester, J. (2011, October). Digital Food Marketing to Children and

Adolescents. Retrieved from: http://www.foodpolitics.com/wp-

content/uploads/DigitalMarketingReport_FINAL_web_20111017.pdf

Morris, J. S., DeGelder, B., Weiskrantz, L., & Dolan, R. J. (2001). Differential

extrageniculostriate and amygdala responses to presentation of emotional faces in a

cortically blind field. Brain, 124, 1241-1252.

National Advertising Review Council. (2004). Guidance for food advertising selfregulation.

Retrived from: New York. http://www.narcpartners.org/reports/NARC_ White_Paper_6-

1-04.pdf.

129

National Center for Health Statistics. (2017). Health, United States, 2016: With Chartbook on

Long-term Trends in Health. Retrieved from:

https://www.cdc.gov/nchs/data/hus/hus16.pdf#053

Niaura, R. S., Rohsenow, D. J., Binkoff, J. A., Monti, P. M., Pedraza, M., & Abrams, D. B.

(1988). Relevance of cue reactivity to understanding alcohol and smoking

relapse. Journal of abnormal psychology, 97, 133.

Nijs, I. M., Franken, I. H., & Muris, P. (2008). Food cue-elicited brain potentials in obese and

healthy-weight individuals. Eating behaviors, 9, 462-470.

Ogden, C. L., Carroll, M. D., Kit, B. K., & Flegal, K. M. (2014). Prevalence of childhood and

adult obesity in the United States, 2011-2012. Jama, 311, 806-814.

Paeratakul, S., Ferdinand, D. P., Champagne, C. M., Ryan, D. H., & Bray, G. A. (2003). Fast-

food consumption among US adults and children: dietary and nutrient intake

profile. Journal of the American dietetic Association, 103, 1332-1338.

Papachristou, H., Nederkoorn, C., Havermans, R., van der Horst, M., & Jansen, A. (2012). Can’t

stop the craving: the effect of impulsivity on cue-elicited craving for alcohol in heavy and

light social drinkers. Psychopharmacology, 219, 511-518.

Payne, T. J., Schare, M. L., Levis, D. J., & Colletti, G. (1991). Exposure to smoking-relevant

cues: Effects on desire to smoke and topographical components of smoking

behavior. Addictive behaviors, 16, 467-479.

Pelchat, M. L., Johnson, A., Chan, R., Valdez, J., & Ragland, J. D. (2004). Images of desire:

food-craving activation during fMRI. Neuroimage, 23, 1486-1493.

Reeves, B., & Nass, C. I. (1996). The media equation: How people treat computers, television,

and new media like real people and places. Stanford, CA: CSLI Publications; New York:

130

Cambridge University Press.

Robbins, S. J., & Ehrman, R. N. (1992). Designing studies of drug conditioning in

humans. Psychopharmacology, 106, 143-153.

Robinson, T. E., & Berridge, K. C. (1993). The neural basis of drug cravings: An incentive-

sensitization theory of addiction. Brain Research Reviews, 18, 247–291.

Roberts, M., & Pettigrew, S. (2007). A thematic content analysis of children’s food

advertising. International Journal of Advertising, 26, 357-367.

Rohsenow, D. J., Childress, A. R., Monti, P. M., Niaura, R. S., & Abrams, D. B. (1991). Cue

reactivity in addictive behaviors: theoretical and treatment implications. International

Journal of the Addictions, 25, 957-993.

Rosin, O. (2008). The economic causes of obesity: a survey. Journal of Economic Surveys, 22,

617-647.

Rush, B. (1789) An Inquiry Into the Effects of Ardent Spirits Upon the Human Body and Mind

with an Account of the Means of Preventing Them and of the Remedies for Curing Them.

New York: Printed for Cornelius Davis.

Sallis, J. F., Grossman, R. M., Pinski, R. B., Patterson, T. L., & Nader, P. R. (1987). The

development of scales to measure social support for diet and exercise

behaviors. Preventive medicine, 16, 825-836.

Sanders-Jackson, A. N., Cappella, J. N., Linebarger, D. L., Piotrowski, J. T., O'Keeffe, M., &

Strasser, A. A. (2011). Visual Attention to Antismoking PSAs: Smoking Cues Versus

Other Attention-Grabbing Features. Human communication research, 37, 275-292.

Shapiro, M. (1994). Signal detection measures of recognition memory. In A. Lang (Ed.),

Measuring psychological responses to media messages (pp. 133-148). Hillsdale, NJ:

131

Lawrence Erlbaum.

Schacht, J. P., Anton, R. F., & Myrick, H. (2013). Functional neuroimaging studies of alcohol

cue reactivity: a quantitative meta-analysis and systematic review. Addiction biology, 18,

121-133.

Siddique, A. (2013, March). Banning Food Advertising to Children: Experts Call for

Regulations on Junk Food Commercials. Retrieved from

http://www.medicaldaily.com/banning-food-advertising-children-experts-call-

regulations-junk-food-commercials-244725

Sheridan, C. (2016, March). United States: Food Advertising and the Rise of Childhood Obesity.

Retrieved from: https://yaleglobalhealthreview.com/2016/03/17/united-states-food-

advertising-and-the-rise-of-childhood-obesity/

Smith, R. E., & Swinyard, W. R. (1983). Attitude-behavior consistency: The impact of product

trial versus advertising. Journal of Marketing Research, 257-267.

Sobik, L., Hutchison, K., & Craighead, L. (2005). Cue-elicited craving for food: a fresh approach

to the study of binge eating. Appetite, 44, 253-261.

Stitt, C., & Kunkel, D. (2008). Food advertising during children's television programming on

broadcast and cable channels. Health communication, 23, 573-584.

Stoeckel, L. E., Cox, J. E., Cook, E. W., & Weller, R. E. (2007). Motivational state modulates

the hedonic value of food images differently in men and women. Appetite, 48, 139-144.

Strasburger, V. C. (2001). Children and TV advertising: nowhere to run, nowhere to

hide. Journal of Developmental & Behavioral Pediatrics, 22, 185-187.

Stewart, J., De Wit, H., & Eikelboom, R. (1984). Role of unconditioned and conditioned drug

effects in the self-administration of opiates and stimulants. Psychological review, 91, 251.

132

SPR Ad Hoc Committee on Electrodermal Measures. (2012). Publication recommendations for

electrodermal measurements. Psychophysiology, 49, 1017-1034.

Tepper, R. (Dec, 2017). 8 out of 10 Americans eat fast food at least once a month, says Gallup

pool. Retrieved from: https://www.huffingtonpost.com/2013/08/06/fast-food-

poll_n_3714988.html

The State of Obesity. (2016). Obesity Rates & Trends. Retrieved from:

https://stateofobesity.org/rates/

Tiffany, S. T. (1990). A cognitive model of drug urges and drug-use behavior: Role of automatic

and nonautomatic processes. Psychological Review, 97, 147–168.

Tiffany, S. T., Cox, L. S., & Elash, C. A. (2000). Effects of transdermal nicotine patches on

abstinence-induced and cue-elicited craving in cigarette smokers. Journal of consulting

and clinical psychology, 68, 233.

Tucker, L. A., & Bagwell, M. (1991). Television viewing and obesity in adult females. American

Journal of Public Health, 81, 908-911.

Tucker, L. A., & Friedman, G. M. (1989). Television viewing and obesity in adult

males. American Journal of Public Health, 79, 516-518. van den Akker, K., Jansen, A., Frentz, F., & Havermans, R. C. (2013). Impulsivity makes more

susceptible to overeating after contextual appetitive conditioning. Appetite, 70, 73-80. van den Akker, K., Stewart, K., Antoniou, E. E., Palmberg, A., & Jansen, A. (2014). Food cue

reactivity, obesity, and impulsivity: are they associated?. Current Addiction Reports, 1,

301-308.

Viner, R. M., & Cole, T. J. (2005). Television viewing in early childhood predicts adult body

mass index. The Journal of pediatrics, 147, 429-435.

133

Wang, Z., Lang, A., & Busemeyer, J. R. (2011). Motivational processing and choice behavior

during television viewing: An integrative dynamic approach. Journal of

Communication, 61, 71-93.

Wang, Z., Morey, A.C., & Srivastava, J. (2014). Motivated selective attention during political ad

processing: The dynamic interplay between emotional ad content and candidate evaluation.

Communication Research, 41, 119-156.

Wiecha, J. L., Peterson, K. E., Ludwig, D. S., Kim, J., Sobol, A., & Gortmaker, S. L. (2006).

When children eat what they watch: impact of television viewing on dietary intake in

youth. Archives of pediatrics & adolescent medicine, 160, 436-442. Psychological

Review, 92, 548-57.

Wonderlich-Tierney, A. L., & Vander Wal, J. S. (2010). The effects of social support and coping

on the relationship between social anxiety and eating disorders. Eating behaviors, 11, 85-

91.

Yale Rudd Center for Food Policy & Obesity. (2013, November). Fast Food Facts 2013:

Measuring progress in the Nutritional Quality and Marketing of Fast Food to Children

and Teens. Retrieved from: http://www.rwjf.org/en/library/research/2013/11/fast-food-

facts-2013.htm

134

APPENDIX

Appendix 1 Sample Coding Sheet

Message Name ______Coder 1st 2nd

Message ID ______Length _____ Emotion? Neg 1 Pos 2 Both 3 Arousing ? none 0 some 1 a lot 2

Camera Emotion New? Un- Object Closer? Perspective Form Change change related? change change change Total

1 1 1 1 1 1 1 1 _____

2 1 1 1 1 1 1 1 _____

3 1 1 1 1 1 1 1 _____

4 1 1 1 1 1 1 1 _____

5 1 1 1 1 1 1 1 _____

6 1 1 1 1 1 1 1 _____

7 1 1 1 1 1 1 1 _____

8 1 1 1 1 1 1 1 _____

9 1 1 1 1 1 1 1 _____

10 1 1 1 1 1 1 1 _____

11 1 1 1 1 1 1 1 _____

12 1 1 1 1 1 1 1 _____

13 1 1 1 1 1 1 1 _____

14 1 1 1 1 1 1 1 _____

15 1 1 1 1 1 1 1 _____

Total information introduced: ______

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Appendix 2

Encoding and Storage Performance

Following physiology data analysis, encoding performance (audio and visual recognition accuracy, sensitivity, and criterion bias) were then examined again. Two-level models were sequentially developed to predict visual and audio encoding performance of food advertising nested within individuals. Multilevel logistic modeling analysis was used here as the dependent variable was a dichotomous indicator of whether the recognition response was true, or false.

Three separate multilevel analysis were performed in order to test how use cue, social cue, and the interaction of use and social cues in the advertising would affect visual and audio recognition accuracy. Multilevel modeling analysis was then conducted to examine the main and interaction effects of use and social cues on visual and audio recognition sensitivity and criterion bias for decision making for different types of food ads. Results indicated that the addition of use and social cues and repetition did significantly improve the model fit compared to the null model

(likelihood ratio test, visual recognition data: Dχ2 = 20.82, d.f. = 4, p < 0.0001; audio recognition data: Dχ2 = 43.26, d.f. = 4, p < 0.0001; visual sensitivity data: Dχ2 = 7.01, d.f. = 3, p = 0.07; visual criterion bias data: Dχ2 = 15.04, d.f. = 3, p = 0.002; audio sensitivity data: Dχ2 = 133.17, d.f. = 3, p < 0.0001; audio criterion bias data: Dχ2 = 75.53, d.f. = 3, p < 0.0001). See Table A2.1.

Overall, it was predicted that individuals would exhibit a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, smaller value of sensitivity and more conservative criterion bias) for the food ads with use cues, the food ads with group cues, and the food ads with both use and group cues.

First, this study predicted individuals would exhibited a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, less sensitivity and a

137

more conservative criterion bias) for the food ads with use cues than those without. To be specific:

In terms of visual encoding performance, it was predicted that individuals would exhibited lower visual recognition accuracy when processing food ads with use cues than those without use cues. The main effect of use cue was found to be significant (z = 4.36, p < 0.0001).

The odds of visual encoding accuracy when processing food ads with use cues was significantly higher (1.55 times as likely; 95% CI: 1.27 to 1.90 times) compared to those without use cues.

The results appeared to be opposite to the predicted direction. It was also predicted that individuals would exhibit less visual sensitivity and a more conservative criterion bias for decision making for food ads with use cues compared to those without use cues. The predicted effect of use cue on visual criterion bias was found to be significant (b = -0.009, SE = 0.003, t(491.3) = -3.18, p = 0.002). Individuals were liberal (all negative) overall; in particular, food ads without use cues had a smaller absolute value of conservative criterion bias for decision making compared to those with use cues, suggesting individuals were relatively more conservative when making decisions for the food ads with use cues than those without. The main effect of use cue on visual recognition sensitivity (b = -0.07, SE = 0.06, t(489.3) = -1.20, p = 0.23) was not significant. See Table A2.1.

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Table A2.1. Estimates for Two-level Multilevel Logistic Models of Encoding Performance Model LEVEL AND VARIABLE Random Intercept and Null Fixed Slope Dependent variable: Visual recognition sensitivity Intercept 0.30 0.30** (0.10) (0.10) Use Cue -0.07 (0.06) Social Cue 0.11* (0.06) Social Cue x Use cue -0.007 (0.06) Variance components Within-person variance 2.044 2.016 Intercept variance 1.049 1.054 Additional information ICC 0.339 -2Log likelihood (FIML) 2506.3 2499.2 N of estimated parameters 3 6 Dependent variable: Visual criterion bias Intercept (g00) -0.17 -0.17*** (0.004) (0.004) Use Cue -0.009** (0.003) Social Cue -0.005 (0.003) Social Cue x Use cue -0.005 (0.003) Variance components Within-person variance 0.006 0.006 Intercept variance 0.002 0.002 Additional information ICC 0.208 -2Log likelihood (FIML) -1376.3 -1391.3 N of estimated parameters 3 6 Dependent variable: Audio recognition sensitivity Intercept (g00) -2.30 -2.20*** (0.09) (0.08) Use Cue -0.16** (0.05) Social Cue -0.61*** (0.05) Social Cue x Use cue -0.16** (0.05)

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Variance components Within-person variance 1.507 1.002 Intercept variance 0.634 0.695 Additional information ICC 0.296 -2Log likelihood (FIML) 1537.5 1404.3 N of estimated parameters 3 6 Dependent variable: Audio criterion bias Intercept (g00) -0.17 -0.05*** (0.004) (0.004) Use Cue 0.0005 (0.003) Social Cue -0.02*** (0.003) Social Cue x Use cue -0.01*** (0.003) Variance components Within-person variance 0.006 0.003 Intercept variance 0.002 0.001 Additional information ICC 0.223 -2Log likelihood (FIML) -1049.3 -1124.9 N of estimated parameters 3 6 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

In terms of audio recognition performance, it was predicted individuals would exhibited poorer audio recognition accuracy for food ads with use cues compared to those without. The predicted effect was the main effect of use cue on audio recognition accuracy, which was found to be significant (z = -3.83, p < 0.0001). The odds of audio encoding performance accuracy when processing food ads without use cues was significantly higher (1.40 times as likely; 95% CI: 1.18 to 1.69 times) compared to those with use cues. It was also predicted that individuals would exhibit less audio sensitivity and a more conservative criterion bias for decision making for food ads with use cues compared to those without use cues. The predicted main effect of use cue on

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audio recognition sensitivity was found to be significant (b = -0.16, SE = 0.05, t(301.9) = -3.12, p

= 0.002). Individuals exhibited lower audio recognition sensitivity for food ads with use cues compared to those without use cues. However, the main effect of use cue on audio criterion bias was not significant (b = 0.0005, SE = 0.003, t(310.6) = 0.18, p = 0.86). See Table A2.1.

Second, this study also predicted individuals would exhibited a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, less sensitivity and more conservative criterion bias) for the food ads with group cues than those with individual cues. In particular:

It was predicted that individuals would exhibited lower visual recognition accuracy for food ads with group cues than those with individual cues. The predicted effect was the main effect of social cue on visual recognition accuracy was not significant (z = 0.19, p = 0.85). It was also predicted that individuals would exhibit less visual sensitivity and a more conservative criterion bias for decision making for food ads with group cues compared to those without individual cues. The predicted main effect of social cue on visual sensitivity was found to be significant (b = 0.11, SE = 0.06, t(490.4) = 2.05, p = 0.04). However, the findings appeared to be opposite to the predicted direction. Individuals exhibited more sensitivity for the food ads with group cues than the food ads with individual cues. However, the main effect of social cue on visual recognition sensitivity was not significant (b = -0.005, SE = 0.003, t(491.3) = -1.63, p =

0.10). See Table A2.1.

It was also predicted that food ads containing group cues would create lower audio recognition accuracy than those without individual cues. The predicted effect was the main effect of social cue on audio recognition accuracy, which was found to be significant (z = 4.87, p <

0.0001). The odds of audio encoding accuracy when processing food ads with group cues was

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significantly higher (1.55 times as likely; 95% CI: 1.30 to 1.86 times) compared to those with individual cues. It was predicted that individuals would exhibit less audio sensitivity and a more conservative criterion bias for decision making for food ads with group cues compared to those with individual cues. The predicted main effect of social cue on audio recognition sensitivity (b =

-0.61, SE = 0.05, t(346.9) = -11.69, p < 0.0001) and criterion bias (b = -0.02, SE = 0.003, t(304.2)

= -7.02, p < 0.0001) were found to be significant. Messages containing group cues had less audio sensitivity and more conservative criterion bias for decision making compared to those with individual cues. See Table A2.1.

Third, this study predicted individuals would exhibited a general pattern of poorer encoding performance (i.e., less visual and audio recognition accuracy, smaller value of sensitivity and more conservative criterion bias) for the food ads with both use and group cues compared to other types of food ads (H4c). In particular,

In terms of visual encoding performance, it was predicted that individuals would exhibit poorer visual recognition accuracy for decision making for food ads with both use and cues compared to those without use cues. The predicted effect was an interaction effect of use and social cues was not significant (z = -1.02, p = 0.31). See Table 2.1. It was also predicted that individuals would exhibit less visual sensitivity and a more conservative criterion bias for decision making for food ads with both social and use cues compared to other types of food ads.

However, the predicted interaction effects of social and use cues on visual recognition accuracy

(b = -0.07, SE = 0.06, t(489.3) = -1.19, p = 0.23) and criterion bias (b = -0.005, SE = 0.003, t(491.3) = -1.58, p = 0.12) were not significant.

It was also predicted that food ads containing both group and use cues would create poorer audio recognition accuracy) than other types of food ads. The predicted interaction effect

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of use and social cues on audio recognition accuracy was found to be significant (z = 3.29, p =

0.001). Results indicated that the log odds ratio of audio recognition accuracy for the food ads with group and use cues was higher (1.45 as likely) compared to those with individual and use cues (b = 0.37, p = 0.07).

It was also predicted that individuals would exhibited less audio sensitivity and a more conservative criterion bias for decision making for food ads with both group and use cues compared to other types of food ads. The predicted interaction effects of use and social cues on audio recognition sensitivity (b = 0.16, SE = 0.05, t(301.9) = -3.16, p = 0.002) and criterion bias

(b = -0.01, SE = 0.003, t(301) = -4.97, p < 0.0001) were found to be significant. See Table 2.1.

Audio encoding performance were pretty poor in general. In particular, in terms of audio recognition sensitivity, individual exhibited less audio sensitivity when making decisions for the food ads with group and use cues compared to the food ads with individual cues (b = -0.76, p =

0.07). They also exhibited less audio sensitivity when making decisions for the food ads with group and use cues compared to those with group cues only (b = -0.31, p < 0.0001). See Figure

A2.1.

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Figure A2.1. Audio recognition sensitivity as a function of social cue (individual cue (S-) vs. group cue (S+)) and use cue (absence (U-) vs. presence (U+))

In terms of audio criterion bias, individuals exhibited more conservative criterion bias when making decisions for the food ads with individual and use cues compared to those with group and use cues (b = -0.03, p < 0.0001). They also exhibited more conservative criterion bias when making decisions for the food ads with group cues (vs. food ads with group and use cues)

(b = -0.01, p = 0.0001), and for the food ads with individual and use cues (vs. food ads with individual cues) (b = 0.01, p = 0.004). See Figure A2.2.

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Figure A2.2. Audio recognition criterion bias as a function of social cue (individual cue (S-) vs. group cue (S+)) and use cue (absence (U-) vs. presence (U+))

Another set of two-level models were then sequentially developed to examine storage performance of food advertising nested within individuals. Multilevel logistic modeling analysis was used here as the dependent variable was a dichotomous indicator of whether participants mentioned use/social cues when doing cued recall test. Multilevel analysis was performed in order to test how use cue, social cue, and the interaction of use and social cues in the advertising would affect later storage performance again. Results indicated that the addition of use and social cues significantly improved model fit than the null model (likelihood ratio test: social cue recall:

Dχ2 = 96.29, d.f. = 3, p < 0.0001; use cue recall: Dχ2 = 144.82, d.f. = 1, p < 0.0001). See Table

A2.2.

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Table A2.2. Estimates for Multilevel Logistic Models of Storage Performance

Model LEVEL AND VARIABLE Null Random Intercept and Fixed Slope Dependent variable: Social cue (individual vs. group) cued recall Intercept 0.32** 0.35** (0.10) (0.11) Use Cue -0.02 (0.05) Social Cue 0.60*** (0.05) Social Cue x Use cue 0.11* (0.04) -2Log likelihood (FIML) 2488.9 2355.6 N of estimated parameters 2 5 Pseudo R2 0 0.05 Dependent variable: Use cue cued recall Intercept -1.05*** -1.31*** (0.09) (0.12) Social Cue 1.003*** (0.09)

N of estimated parameters 1139.88 995.06 Note* FIML = Full information maximum likelihood estimation. Values in parentheses are standard errors. t-statistics were computed as the ratio of each regression coefficient divided by its standard error. * p < 0.05, ** p < 0.01, *** p < 0.0001

It was predicted that food ads containing use cues would create poorer storage performance than those without use cues. However, the main effect of use cue on social cue recall was not significant (multilevel logistic regression, z = 0.14, p = 0.86).

It was also predicted that food ads containing group cues would create poor storage performance than those with individual cues. As predicted, the odds of recalling social cues when performing cued recall for the food ads with group cues was significantly higher (2.92 times as likely; 95% CI: 2.35 to 3.66 times) compared to those with individual cues. Besides, the predicted main effect of social cue on direct food cue cued recall (multilevel logistic regression, z

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= 10.62, p < 0.0001) was also found to be significant. The odds of depicting use cues when doing cued recall for the food ads with group cues was significantly higher (7.43 times as likely; 95%

CI: 5.18 to 10.88 times) compared to those with individual cues.

It was predicted that food ads containing both use and social cues would create poorer storage performance than other types of food ads. However, the predicted interaction effect of use and social cues on social cue recall not significant (multilevel logistic regression, z = 0.90, p

= 0.37).

The addition of impulsive behavioral tendency did not significantly improve model fit compared to the level 1 models (likelihood ratio test, visual recognition data: Dχ2 = 0.04, d.f. = 1, p = 0.84; audio recognition data: Dχ2 = 0.69, d.f. = 1, p = 0.41; visual sensitivity data: Dχ2 = 1.97, d.f. = 1, p = 0.16; visual criterion bias data: Dχ2 = 0.35, d.f. = 1, p = 0.55; audio sensitivity data:

Dχ2 = 1.48, d.f. = 1, p = 0.22; audio criterion bias data: Dχ2 = 0.54, d.f. = 1, p = 0.046).

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