Motivated Selective Attention during Political Ad Processing: The ...

Motivated Selective Attention during Political Ad Processing:

The Dynamic Interplay between Emotional Ad Content and Candidate Evaluation

Zheng Wang

Alyssa C. Morey

Jatin Srivastava

Zheng Wang (Ph.D., Indiana University-Bloomington, 2007). Assistant Professor, School of Communication, The Ohio State University; Address: 3149 Derby Hall, 154 N. Oval, Columbus, OH 43210; Telephone: (614)787-6969; Email: [email protected] Alyssa C. Morey, a doctoral student in the School of Communication at The Ohio State University. Address: 3016 Derby Hall, 154 N. Oval Mall, Columbus, OH, 43210. Telephone: (614) 940-6959. Email: [email protected]. Jatin Srivastava (Ph.D., Ohio State University, 2010). Assistant Professor, E.W. Scripps School of Journalism, Ohio University, Address: E.W. Scripps School of Journalism, Ohio University, Park Place & Court Street, Athens, Ohio 45701-2979, Telephone: (785) 317-6259; Email: [email protected]

February 2012

To appear in Communication Research. This work was partially supported by the National Science Foundation (Grant No. SES 0818277) to the first author. We thank the three anonymous reviewers for their valuable comments.

Running Head: Motivated Selective Attention to Political Ads

Motivated Selective Attention to Political Ads | Page 1

Motivated Selective Attention during Political Ad Processing:

The Dynamic Interplay between Emotional Ad Content and Candidate Evaluation

Abstract

This study examines the dynamic, real time interplay between the emotional content of

political television ads and individuals’ political attitudes during ad processing based upon the

Dynamic Motivational Activation (DMA) theoretical framework. Time-series cross-sectional

models were developed to test the effects of three motivational inputs of emotional ads (arousing

content, positivity, and negativity) and viewers’ evaluation of the featured candidates on four

psychophysiological responses (HR, SCL, corrugator EMG, and zygomatic EMG). As predicted

by the DMA, physiological responses during ad viewing were affected by their own first and

second order dynamic system feedback effects. These results not only support the predicted

dynamic nature of the physiological system, but also help disentangle message effects from the

moderating and accumulating effects of the physiological system itself. Also as predicted,

message motivational inputs interacted with viewers’ political attitudes to determine

psychophysiological responses to the ads. Supporters of opposing political candidates showed

cardiac-somatic response patterns indicative of disparate attention to the advertised information.

Attentional selectivity can be a critical component in determining how information processing

influences campaign message reception and effects.

Key words: dynamic processing, motivated attention, cardiac-somatic coupling,

psychophysiology, emotional political ads, time series


Participatory democracy is founded on the assumption of an informed public (Dahl,

1989), where understanding of cross-cutting political viewpoints is believed to be essential

(Barber, 1984). To the laments of political scholars, however, political ignorance is one of the

most well-known and consistent findings of contemporary political science research (Delli

Carpini & Keeter, 1996). Lack of knowledge is caused, in part, by self-selective exposure to

information, both mass-mediated and interpersonal, which reinforces rather than challenges their

beliefs (Iyengar & Hahn, 2009). In fact, selective exposure has led to the counter-intuitive

phenomenon that political polarization increases with increased availability of diverse

information sources (Jacobson, 2006).

In addition to selectivity in behavioral choices, political beliefs and attitudes may bias the

actual processing of political information. This selective processing may further widen political

divides and contribute to polarization in the public’s knowledge and perceptions. For instance,

those who support socially protective political policies show stronger physiological responses to

threatening pictorial information (Oxley et al., 2008). Thus, media reports with threatening

images might create a divergence in threat perceptions among liberals and conservatives.

Additionally, the phenomenon called “hostile media” suggests that partisans on either side of a

political issue perceive media coverage, rated neutral by non-partisans, as hostile to their point of

view (Vallone, Ross, & Lepper, 1985; Gunther & Liebhart, 2006). Perceptions of media bias

could contribute to more extreme political beliefs among opposing partisans.

Extending the observation of overt selective exposure to covert selective processing, we

propose that even when exposed to the exact same information, individuals, driven by their

political beliefs and attitudes, process the information in a highly selective manner. This study

uses real-time physiological measures and dynamic modeling to examine disparate attention to


televised political campaign ads as a function of message emotional content and viewers’

attitudes toward party candidates in a presidential election. Attention is defined as the allocation

process of limited mental resources, leading to selectivity in sensory information intake.

Selective attention to political ads warrants scrutiny for several reasons. First, political

ads are a crucial component of our information environment during political campaigns (Cho,

2008). The most recent Wisconsin Advertising Project (2010) reports that in 2008 in the United

States, more than 2.1 million political television ads were aired in the largest 100 media markets

at a cost of over $1.13 billion. Political ads offer important information to the public, such as

candidates’ issue positions, and encourage further information seeking and interpersonal

conversation (e.g., Benoit, Hansen, & Holbert, 2004; Pan, Shen, Paek, & Sun, 2006). Second, the

majority of political ads are strategically placed in critical battleground states in an effort to

inform and mobilize the apathetic, undecided, or opposing voters (Wisconsin Advertising

Project, 2010). Importantly, this investment model has not carefully calculated how selective

attention of voters may influence the effects of ad bombardment. Lastly, it has been suggested

that saturation and dissemination of televised political ads creates a less self-selective

information context than news media (Cho, 2008). Citizens are more likely to encounter

dissimilar political views through news media than interpersonal communication contexts (Mutz

& Martin, 2001). As control over ad exposure is presumably more difficult than that over news,

televised political ads seem to be one of the most important information channels for minimizing

selective exposure and broadcasting political views across lines of ideological difference.

In this paper, we begin with a brief review of emotional political ads and motivated

attention. We argue that cognition and media processing should be theorized and analyzed as a

dynamic system. A dynamic motivational model is introduced to formalize psychophysiological


responses (indicative of real time attention) as dynamic responses to the interactions between

message motivational inputs and individuals’ attitudes toward the two major party candidates in

the 2008 presidential election. Data from an experiment are used to test this model.

Emotional Political Ads

Emotional appeals are prevalent in political ads. Political ads are often broadly

categorized as positive, negative, or comparative. Positive ads highlight the supported

candidate’s merits and strengths, whereas negative ads focus on the weaknesses of the opposing

candidate (Shapiro & Rieger, 1992). Comparative ads highlight both the supported candidate’s

strengths and the opponent’s weaknesses (Shah et al., 2007). Although more specific distinctions

exist, this emotional valence-based categorization is the most common. Research based upon this

categorization has generated valuable insights on the effects of the emotional appeals of political

ads (e.g., Brader, 2006; Westen, 2007).

Although this categorization is advantageous for its simplicity, it ignores important

emotional dynamics across a message. For example, an ad categorized as negative is not simply

negative throughout the ad. President Lyndon Johnson’s notorious negative ad during the 1964

campaign, Daisy Girl, serves as a good example. The ad opens with a young girl standing in a

field, counting as she picks off the petals of a daisy. Birds chirp in the background. A narrative

voice then launches into an ominous countdown, the end of which signals a nuclear explosion.

Thus, as the ad unfolds across time, the emotional tone and intensity change remarkably. The

first half of the ad is peaceful and pleasant. Then the countdown commences, adding suspense

and emotional intensity. Finally, the nuclear explosion at the end is one of the most infamously

negative scenes from a political ad. Emotional content in this ad changes from positive to

negative, and from calm to frighteningly intense. Similarly, other political ads might start off


negative and depressing by, for instance, presenting economic problems, only to become positive

and encouraging at the end of the ad, promoting promises and solutions offered by the candidate.

In addition, the tone of an ad might be negative throughout but with varying intensity. In other

words, when we simply categorize an ad as positive, negative, or comparative, we have

overlooked a rich amount of information on the dynamic changes of the emotional content. One

negative ad can be exceedingly different from another. Furthermore, even if we keep the

emotional content of ads the same or similar, we can induce notably different viewer responses

by simply altering the sequential order of the emotional content (Harvey, 1990; Wang, A. Lang,

& Busemeyer, 2011). Imagine, for example, how viewers’ responses to the Daisy Girl ad might

change if the nuclear explosion were moved to the beginning of the ad.

In addition to potential over-simplification, the positive/negative/comparative ad

terminology derives from a focus solely on the candidates featured in the ads or sponsoring the

ads (i.e., intended emotional responses) without considering the viewers. The viewers’

predispositions should influence actual evoked responses. A negative ad attacking a particular

candidate may elicit negative responses from those who support the candidate, but may cause a

sense of positive agreement among those who oppose the candidate. In other words, viewers may

experience complex and perhaps mixed feelings. The motivational model of emotion, reviewed

below, provides a parsimonious conceptualization of this intricate experience.

Emotion, Motivation, and Motivated Attention

Emotions serve an adaptive function, evolving to facilitate organisms to appropriately

interact with their environments. Emotions are dispositions to act, although they do not always

manifest in overt behavior (Frijda, 1987). Many discrete emotions (e.g., fear, anxiety, and

happiness) can be mapped onto a dimensional space reflecting the fundamental appetitive and


aversive motivational systems that have evolved to help organisms survive and thrive in their

environments (for a review, see M. Bradley, 2000; for similar theories, see Eysenck, 1967; Gray,

1981; Fowles, 1993). The two motivational systems are activated by emotional inputs from

direct experience with the physical world as well as indirectly through mediated experiences (A.

Lang, 2006; Reeves & Nass, 1996), such as televised political ads (S. Bradley, Angelini, & Lee,

2007). The valence (positive, negative) of the stimulus determines which motivational system(s)

is (are) activated, whereas the arousing content (i.e., intensity of the emotion) of the stimulus

determines the strength of activation. Motivational activation, in turn, “initiates a cascade of

sensory and motor processes, including mobilization of resources, enhanced perceptual

processing, and preparation for action.” (M. Bradley, 2009, p.1). The appetitive system is

activated by positive stimuli, which facilitates approach tendency or behaviors, including

mobilizing attentional resources for sensory intake. The aversive system is activated by negative

stimuli such as threats. With increased intensity of negative stimuli and greater activation of the

aversive system, the attentional pattern often switches from information intake (i.e., initially to

identify the threat) to information rejection (i.e., preparation for fight or flight) (A. Lang, 2006).

In many situations, the two systems are negatively correlated. However, theoretically they are

independent (Cacioppo & Bernston, 1994); and depending on the context, their correlations can

vary from -1 to 0 and may even be positive (e.g., Zautra, Berkhof, & Nicolson, 2002).

This motivational interpretation of emotion and the theoretical framework of motivated

attention have been widely used in basic neuropsychological, psychological, and applied

communication research. The two dimensions of message emotion, valence and arousing

content, have been useful in parsimoniously explaining the effects of emotional messages on

attentional, affective, and behavior responses (for reviews, see Ravaja, 2004; A. Lang, 2006). A


recent study (Wang et al., 2011) using stochastic dynamic analysis found that in a user-controlled

entertainment television viewing context, continuous inputs of negativity, positivity, and

arousing content of programs explained the majority of variance in real time psychophysiological

responses that indicate attentional effort and autonomic arousal. Applying a motivational

theoretical framework to political ads processing, S. Bradley et al. (2007) revealed that negative

television political ads elicited reflexive preparation to move away as indicated by

psychophysiological measures. A natural follow-up question is: How do viewers’ political

attitudes influence motivated selective attention?

If our information landscape is sculpted by the general appetitive and aversive nature of

the information stream, it is our individual motivational traits, emotional states, and attitudes that

orchestrate how we perceive and interact with this continuously changing landscape. Individual

differences in reactivity of the aversive and appetitive motivational systems moderate selective

attention to various emotional stimuli, including pictures and videos (e.g., A. Lang, Wang, & S.

Bradley, 2004). Additionally, differences in emotional states and attitudes can influence

attention. For example, those who are anxious and depressed show an attentional bias to negative

information (Mogg, B. Bradley, & Hallowell, 1994).

In fact, attitudes have been interpreted and studied through the motivational model of

emotion. An attitude represents an evaluation of a stimulus, indicating a general orientation and

direction of action toward the stimulus. Valence (i.e., positive or negative evaluation of the

stimulus) and arousal (i.e., intensity or strength of the evaluation) have been recognized as

important aspects of attitudes, even characterized as “currently among the best understood

biological aspects of evaluation” (Cunningham, Packer, Kesek, & Bavel, 2009, p. 489). Similar

to the motivational interpretation of emotion, the direction of an action or action readiness


reflects attitudinal valence, whereas intensity corresponds to arousal. The peripheral nervous

system (PNS) prepares the body for actions and shows various patterns of responses, even if the

action is eventually inhibited (Cunningham et al., 2009; Cacioppo & Berntson, 1994). Therefore,

different patterns of PNS responses, such as heart rate (HR), skin conductance level (SCL), and

facial electromyography (EMG), should be affected by not only the general motivational inputs

from the stimulus, but also individuals’ attitudes to the stimulus. Divergent attitudes can lead to

divergent allocation of attentional resources to the exact same stimuli, resulting in information

intake or rejection patterns.

This study expands previous work by examining whether selective attentional processing

is determined by the interactions among emotional content features of messages and individual

differences in political attitudes. In a highly salient campaign, such as the 2008 presidential

election, the political attitudes most likely to moderate responses to political ads are evaluations

of the competing candidates. From a dynamic system viewpoint, the negativity, positivity, and

arousing content of ads are considered motivational inputs because they have motivational

significance that influences physiological system outputs. We analyze formal dynamic models

that include or exclude the interaction effects between message motivational inputs and viewers’

evaluations of the candidates. By comparing these models, we test the hypothesis that models

including interactions between message motivational inputs and audience attitudes better explain

(in terms of both model fit and theoretical parsimony) continuous physiological responses during

political ad processing compared to models including message motivational inputs only

(Hypothesis 1). More specifically, we propose that the interactions between message

motivational inputs and viewers’ attitudes to the candidates will lead to divergent physiological


responses among viewers with opposing attitudes, indicative of selective attention to the

advertised information (Hypothesis 2).

Dynamic Motivational Processing

As argued earlier, we may gain a richer understanding of the effects of political ads by

viewing them as dynamically changing inputs. A more compelling and essential reason for

formalizing political ad processing as a dynamic process is that cognitive functions are dynamic

and complex in nature. Dynamics are fundamental for understanding human cognition and

behavior (Kelso, 1995; Ward, 2002). Political ad processing is no exception. Extant studies of

message effects, however, typically employ static analysis, and may therefore overlook the

inherent self-generating function of human cognition. According to mounting evidence from

neuroscience and cognitive psychology, along with the reinvigorating complex system approach

to understanding our brains and cognition, “self-organization” is “a fundamental brain operation”

(Buzsáki, 2006, p.10). More specifically, “…most of the brain’s activity is generated from

within, and perturbation of this default pattern by external inputs at any given time often causes

only a minor departure from its robust, internally controlled program” (pp.10-11). This dynamic

system approach to cognition emphasizes the nonlinear relationship between the system’s

interconnected components, time dependence, and feedback loops (p.11). To accurately estimate

media effects, the endogenous self-organizing and self-generating feedback effects from our

cognitive systems need to be explicitly estimated and disentangled from the exogenous effects of

media inputs. Time also needs to be explicitly included in both theory and analysis to prevent

confounding of media effects per time unit with media effects aggregated by system feedback

effects over a certain duration. Intuitively, this argument is similar to rationales for research

examining effects of media contexts (e.g., Potter, LaTour, Braun-LaTour, & Reichert, 2006) and


extended exposure (e.g., Thomas, 1982). At any time point, affective and cognitive responses are

intimately linked to prior responses, and integrate with the prior responses to determine how an

individual is affected by an exogenous stimulus at the time. To accurately estimate the effects of

exogenous stimuli, such as the effects of media motivational inputs, dynamic models are needed

to tease apart the influence of message content, processing system feedback, and time.

Furthermore, with a solid understanding of the system elements at the level of per time unit, we

then can put all of these process components together to examine how they interact and integrate

with each other to generate outputs at the system level across time. A precise estimation of

message effects and processing feedback effects per time unit allows greater generalizability and

predictive power of the model across various manipulations and data sets, such as message

stimuli with different time durations and different motivational content inputs.

Dynamic Motivational Activation model (DMA, Wang & Busemeyer, 2007; Wang, A.

Lang, & Busemeyer, 2011; Wang & Tchernev, in press) takes a dynamic system approach to

studying media processing and choices. Built upon motivational processing theories as reviewed

earlier, DMA emphasizes the central role of motivational activation in information processing

and choice behavior, and aims to explain how the effects evolve dynamically across time. DMA

aims to specify and quantify physiological responses (indicating real time cognitive and affective

responses) during message processing as a dynamic function of the exogenous media or message

motivational inputs as well as the endogenous feedback effects of the physiological (cognitive

and affective) systems. In a recent study by Wang et al. (2011), a second order stochastic

difference equation model with delayed input effects was used to formalize several hypotheses of

DMA using HR, SCL, corrugator and zygomatic EMG measures. As predicted by DMA, large

variance in psychophysiological responses across time during the viewing of video clips was


explained by the dynamically changing motivational inputs of the video. In addition, Wang et al.

(2011) estimated the time it takes the motivational inputs to reach and produce an effect on the

physiological systems. Most importantly, the physiological responses showed significant first

and second order feedback effects (i.e., lag 1 and lag 2 autoregressive terms of the dependent

variable in the model), providing direct support for dynamic system message processing models.

These feedback effects integrate the system’s past to the present. Through the feedback terms,

responses to prior motivational inputs affect responses to the current inputs, which, in turn, affect

subsequent responses to the real time inputs. More specifically, the first order feedback effect

produces an inertial effect, while the second order feedback effect produces an oscillation effect.

These are consistent with the homeostasis characteristic of physiological systems (Stern, Ray, &

Quigley, 2001) and the oscillation patterns of brain activities (Buzsáki, 2006). System feedback

effects are critical in the evolution of message input effects. They determine the speed,

amplitude, and duration of the integrated outputs of the dynamic system (Harvey, 1990; Boker &

Wenger, 2007), while the integrated outputs are observable in which media effects researchers

are generally interested.

Following the formalization of DMA in Wang et al. (2011), this study further tests

whether the physiological systems have the first and second order feedback effects during

political ad processing (Hypothesis 3). Wang et al. have tested this hypothesis and found

supporting evidence in the context of entertainment television viewing. The viewing duration in

the 2011 experiment was relatively long (30 min), and viewers had continuous control over

which television program to watch. The current study provides a different media environment,

with shorter messages that aim to persuade rather than entertain viewers. Hence, it is necessary


to test in this new context whether the previous formalization of the physiological responses as a

second order dynamic system applies to political ads processing.

Method

Pretest and Stimuli

The study was conducted at a large Midwestern university in a battleground state in the

United States. For the pretest, 24 30-sec televised political ads were selected from the 2008

general presidential election campaign. The ads were selected using a 2 (Valence: positive,

negative) × 3 (Arousing Content Levels: arousing, moderate, calm) × 2 (Candidates: McCain,

Obama) factorial design, with two ads at each level. Featured candidate of an ad refers to which

candidate was presented in the ad, regardless of the sponsor of the ad. For example, an ad

criticizing McCain’s environmental policies is discussed as a “McCain ad.”

In total, 120 undergraduate students participated in the pre-test study, with a comparable

number of individuals favoring McCain, Obama, or neutral toward the two candidates.

Participants came to the lab, arriving in groups of 2-6, and completed the study using individual

desktop computers. Each participant rated the political ads using the continuous response

measurement (CRM, Biocca, David, & West, 1994) implemented by MediaLab software (Jarvis,

2008). While viewing each ad on the computer, the participant simultaneously pushed left and

right arrow keys on the keyboard to move a slider. The slider appeared right below the ad on the

screen and its movement indicated the participant’s judgment along one of three motivational

scales. The scales were: (1) Not at all arousing—Extremely arousing (Arousing Content), (2) Not

at all positive—Extremely positive (Positivity), and (3) Not at all negative—Extremely negative

(Negativity). All three scales were anchored by 1 and 100 when presented on the screen.

MediaLab transferred the ratings onto a scale of 0-2 (rounded to the hundredth decimal place)


and recorded the data at 20Hz. Participants were randomly assigned to the combinations of ads

and rating scales, and the presentation order of ads was randomized for each participant. All

participants made ratings of arousing content, positivity, and negativity, and no participant

viewed the same ad more than once. Across participants, all 24 ads were rated on all three scales

by a comparable number of people. Based upon means and medians of the CRM ratings from the

pre-test study, final stimuli used in the psychophysiological experiment were selected. One ad

was selected at each of the 12 manipulation levels to ensure a large range of emotional content.

ANOVAs showed that the manipulation of the general valence and arousing content was

successful (ps < .05), which was not different between ads for the two candidates.

Participants and Procedures

The psychophysiological experiment was conducted during the last two weeks of October

2008—a few days before the 56th quadrennial U.S. presidential election. Six presentation orders

were constructed using the Latin square design to counterbalance the 12 ads. Participants were

randomly assigned to one of the six orders. Complete physiological data were obtained from 15

students from the same university. They shared similar demographic features as those in the

pretest. They were 20-32 years old (M = 22.07, SD = 2.87), around half (53.33%) were males,

and most were Caucasian (86.67%). None of them had participated in the pretest.

Experiments were conducted individually. After arriving at the lab and providing

informed consent, the participant was prepared for physiological measures. The participant

viewed two practice ads to get familiar with the experimental environment before watching the

12 stimulus ads. The participant’s physiological responses were recorded during ad viewing.

After watching all the ads, electrodes for physiological measures were removed from the

participant. Gender, race, and individual difference traits of motivational reactivity can affect


motivational processing (A. Lang et al., 2007; M. Bradley, 2000); and hence, these variables

were measured and controlled for in data analysis. Individual differences in motivational

reactivity were assessed by Approach System Activation (ASA) and Defensive System Activation

(DSA) measures (Lang, Kurita, Rubenking, & Potter, 2011). Candidate Evaluation was obtained

using modified questions from the American National Election Studies (ANES, 2006).

Participants were asked to rate each of the major party candidates (Obama and McCain)

“according to how negative or positive you feel about him” on a scale of 1 (very negative) to 9

(very positive). The order of Obama and McCain evaluation was randomized.

Physiological Dependent Variables

All physiological variables were collected using Coulbourn Instruments and a Scientific

Solutions Labmaster A/D board controlled by acquisition software VPM 12.6 (Cook III, 2007).

Heart Rate (HR) is controlled by the sympathetic nervous system (SNS, dominant during

mobilization) and the parasympathetic nervous system (PNS, dominant during rest). Cardiac

deceleration results from more dominant PNS over SNS activation, which indicates perceptual

information intake and orienting. Cardiac acceleration results from more dominant SNS over

PNS activity, which indicates sensory rejection, mentation or internal focus, and behavioral

response or tendency (Campbell, Wood, & McBride, 1997; Lacey, 1967; Graham & Clifton,

1966). This cardiovascular pattern of attention has been supported in media research (A. Lang,

2006). In the current experiment, HR data were collected using two 7-mm Ag/AgCl electrodes

placed on the forearms. The interval between heart beats was recorded and converted to beats per

minute (BPM).

Skin Conductance Level (SCL) measures SNS activation and is associated with

motivational activation intensity (M. Bradley, 2000). Higher SCL indicates increased


sympathetic arousal and greater motivational activation. Heightened skin conductance is a

typical component of an orienting response, but this response habituates quickly. Therefore, SCL

is interpreted as relating primarily to action preparation in response to the stimulus rather than

perceptual information processing of the stimulus after initial orienting response (M. Bradley,

2009). SCL data were acquired through two 7-mm Ag/AgCl electrodes placed on the non-

dominant palmar surface, sampled at 20 Hz.

Zygomatic and Corrugator EMG have been used as an indication of emotional responses

(Larsen, Norris, & Cacioppo, 2003). However, these measures are also associated with

attentional effort (e.g., Cohen, Davidson, Senulis, Saron, & Weisman, 1992). Zygomatic EMG

measures activities in the zygomaticus major muscle group which is located under the cheek.

This muscle group is responsible for tightening of the cheek, which may indicate communication

and speech tendency (e.g., P. Lang, Greenwald, M. Bradley, & Hamm, 1993; McGuigan &

Rodier, 1968). The corrugator supercili muscles are located above the eyes and near the base of

the eyebrows. These muscles control the lowering and raising of the eyebrows, and can index

perceptual attentional effort (Cohen et al., 1992). The facial EMG data were acquired by placing

a pair of 4-mm Ag/AgCl electrodes on the facial muscle sites on the left side of the face. The

data were sampled at 500 Hz.

Time Series Data of Independent and Dependent Variables

For each participant, time series were created for each physiological variable at the rate

of one observation per sec. Each series is composed of 360 data points recorded during viewing

of the 12 ads. First, a detrending procedure was carried out on the data to remove linear trends

using the general linear model procedure in SAS (PROC GLM). This removes the influence of

time on the data series which is not stimulus-specific and does not constitute the focus of the


study (e.g., habituation and fatigue). The detrended data were standardized for each variable

across all participants to facilitate interpretation and comparison of model parameters.

For independent variables, three time series were created using the medians of the CRM

ratings of positivity, negativity, and arousing content acquired in the pretest, again at the rate of

one observation per sec. Dummy coding was used to distinguish the presidential candidate

featured in each ad (OM: McCain = 0, and Obama = 1). Differences in the evaluation of the two

candidates were computed for each participant (Eval = evaluation of McCain - evaluation of

Obama; range = -8 to 8; M = -.60, SD = 4.73). Gender, race, and individual motivational traits

(ASA, DSA, and their interaction) of viewers were controlled in all analyses.

Analysis

Time-Series Cross-Sectional (TSCS) Analysis

TSCS analysis was conducted on the time series data of all participants using PROC

TSCSREG in SAS software. Advantages of TSCS include increased sample size, and

simultaneous estimates of the cross-time effects of message variables and cross-sectional

individual differences. In the following analyses, individual viewers were considered cross-

sectional units and 360 observations during ad viewing constituted the time series for each

section. The estimation method was the Fuller-Battese method (Fuller & Battese, 1974), which

includes the individual- and time-related random effects to the error disturbances. The error part

of the model also included an autoregressive lag 1 effect, which corrects the autocorrelation of

the errors.

Model Fitting and Comparison

To test the hypotheses on dynamic interactions between messages and viewers, three

groups of models were compared: (1) the message model, (2) the message and message-audience


interaction model, and (3) the message-audience interaction model. All models include the first

and second order system feedback terms (i.e., the lag 1 and lag 2 autoregressive terms of the

dependent variable), but vary on inclusion of general message or message-audience interaction

terms. The message models consist only of general message variables: Arousing Content (A),

Positivity (P), Negativity (N), their two-way interactions (A×P, A×N, P×N), and their quadratic

effects (A2, P2, N2). The message and message-audience interaction models consist of all the

variables in the message models, but also include interaction effects between message variables

and individuals’ evaluation difference of the two candidates (Eval × A, Eval × N, Eval × P) as

well as three-way interactions involving the dummy codes indicative of the featured candidate in

each ad (Eval × A × OM, Eval × N × OM, Eval × P × OM). Finally, the interaction models are

simpler than the second group of models, including the interaction terms of message variables

and candidate evaluation, but excluding general message effects.

Each of the four physiological responses was separately tested for the three groups of

competing models. Following Wang et al. (2011), to identify the best delay lags for message

inputs to reach the physiological system, 11 message input lagged models were estimated using

lags 0 to 10 (indicating no delay to 10 sec delay from the A, P, and N motivational message

inputs to the elicitation of physiological responses). The input lag model with the largest

regression R2 was selected for each competing model of each physiological variable. This step

reduced the 11 (message input delay) × 3 (competing models) models to three for each

physiological variable, as summarized in Tables 1-4. This message input delay is not the focus of

the current design and study. However, quantifying this delay in the models helps accurately

estimate the other model parameters of interest. To test Hypothesis 1 and select the best model

among the three message and audience models for each physiological response, Bayesian


Information Criterion (BIC) was employed because the three models for each physiological

variable are not nested and have a different numbers of parameters. BIC considers both goodness

of fit and complexity of models. Models with smaller BIC are preferred (Busemeyer &

Deiderich, 2010; Schwarz, 1978).

Results

The Interaction Model

Model parameters and model fit statistics for the four physiological responses are

summarized in Tables 1-4. As shown, the interaction model consistently achieved smaller BIC

compared to its competing models for all four physiological responses. Thus, the interaction

models are the preferred models, and Hypothesis 1 is supported. As indicated by regression R2,

the interaction model accounts for 38.27% of variance in the HR time series across all

participants, 55.67% in SCL, 33.74% in corrugator EMG, and 61.09% in zygomatic EMG. The

regression R2 is smaller than those found by Wang et al. (2011). This is expected as the current

model attempts to account for variance across time as well as across all individuals (c.f., models

in the Wang et al. study only account for time series variance).

[Insert Tables 1-4 about here.]

Message Motivational Inputs Interact with Candidate Evaluation

Message motivational inputs interact with viewers’ evaluation of the candidates to

influence physiological responses, and viewers holding opposing attitudes show divergent

responses (Hypothesis 2 supported). The effects are estimated by model parameters Eval × A,

Eval × P, Eval × N, and their three-way interactions with the dummy code OM (indicating the

featured candidate) (see Tables 1-4). The sign and size of the parameters indicate the direction

and size of the effects. For illustration, these effects were simulated in a MATLAB program


using the parameters of “the Interaction Model” in Tables 1-4. Interaction effects involving

arousing content are shown in Figure 1. Figures 2 and 3 illustrate interactions involving

negativity and positivity, respectively. It is worth emphasizing that these effects, estimated by the

model parameters, are per time unit (i.e., per second, in this study) and are disentangled from the

system’s dynamic feedback effects (discussed in more detail later).

Based upon the range of actual independent variable values in our experiment, the

message motivational inputs in the simulation were 0-1.5 (on the scale of 0-2), and candidate

evaluations were represented using the Eval scores of -6, -3, 0, 3 and 6 (on the scale of -8 to 8).

Negative scores indicate favoring Obama over McCain, positive scores indicate favoring McCain

over Obama, and 0 indicates a relatively neutral position in the sense that there is not clear

preference for one candidate or the other. The greater the absolute value of the Eval score, the

larger the difference in evaluation of the two candidates. In each graph panel, separate lines

depict evaluation difference scores. From top to bottom, the four rows illustrate HR, SCL,

corrugator EMG, and zygomatic EMG, respectively. The panels on the left are responses to

Obama ads (i.e., ads that feature Obama, regardless of who sponsored or paid for the production

and airing of the ad) and those on the right are responses to McCain ads (i.e., ads featuring

McCain, regardless of sponsor). The plotted psychophysiological changes are in standardized

scores.

[Insert Figures 1-3 about here.]

Arousing Content Interacts with Candidate Evaluation. HR, SCL, and corrugator EMG

were significantly affected by Eval × A and Eval × A × OM interactions, but zygomatic EMG

was not (see Tables 1-4 for parameters and Figure 1 for illustration of the effects). Across all

panels in Figure 1, we can see that when the Eval score is 0 (indicating a neutral position to the


two candidates), increasing arousing content does not change physiological responses to the

message. However, when evaluation is negative (indicating preference for Obama) or positive

(indicating preference for McCain), increasingly arousing content does impact responses. The

effects are in exactly opposite directions for supporters of the two candidates, and the larger the

evaluation difference, the greater the effects. When ads become more arousing, proponents

respond to their favored candidate’s ads with a decrease in HR and SCL, and an increase in

corrugator EMG. While viewing the opposing candidate’s ads, the reverse pattern occurs: HR

and SCL increase, and corrugator EMG decreases.

The predicted response changes are plotted in standardized scores. Comparing the panels

in Figure 1, we can infer the effect sizes in terms of the portion of one standard deviation of the

physiological data. The largest effect of arousing content is on HR during exposure to McCain

ads. When arousing content increases from 0 to 1.5, the HR difference between those strong

Obama vs. strong McCain proponents increases by more than 50% of one standard deviation of

the HR data. HR during Obama ads differs only by around 20% of its standard deviation for

strong opposing partisans. Corrugator EMG shows similar effect sizes during both candidates’

ads (around 35% of its standard deviation) as does SCL (around 20%).

Negativity Interacts with Candidate Evaluation. All four physiological responses are

affected by Eval × N and Eval × N × OM interactions, although a few were only marginally

significant at p < .1 (see Tables 1-4 and Figure 2). Similar to arousing content, changes in the

negativity of emotional message input has no effect on viewers with neutral evaluations, but does

influence those with a preference for one candidate over the other. Negativity drives

physiological reactions in opposite directions for those favoring different candidates, and again,

the greater the evaluation differences, the larger the effect. There are two exceptions to this


general pattern: SCL and zygomatic EMG during Obama ads show no effect of negativity

regardless of candidate evaluations.

Describing the effects of message negativity in more detail, when McCain ads become

more negative, McCain supporters show increased HR, SCL, and zygomatic EMG, but decreased

corrugator EMG. Obama supporters demonstrate the opposite pattern of physiological responses.

When Obama ads become more negative, Obama supporters show the same response patterns

that McCain supporters show while viewing McCain ads—except that SCL and zygomatic EMG

are not significantly affected. McCain supporters’ responses to Obama ads are similar to Obama

supporters’ responses to McCain ads—except that SCL and zygomatic EMG are unaffected. Of

the effects, corrugator EMG during Obama ads shows the largest effect size. Differences in

corrugator EMG between strong Obama compared to strong McCain supporters increased by

around 40% of its standard deviation when negativity increases from 0 to 1.5. The other changes

are around 20% of the physiological measures’ standard deviations.

Positivity Interacts with Candidate Evaluation. Interestingly, positivity shows very

similar effects as negativity (see Tables 1-4 and Figure 3). However, the Eval × P and Eval × P ×

OM effects on zygomatic EMG are not significant. Another difference is that compared to

negativity, positivity shows a larger divergent effect on HR (35% vs. 20% of standard deviation).

Physiological Feedback Effects

All four physiological systems have significant first and second order feedback effects

(Hypothesis 3 supported). This means that the changes and asymptote of the physiological

system’s reactions to motivational inputs depends not only on the nature of the effects of

message inputs (e.g., direction and size of the effects as reported in the previous section), but

also the system’s own feedback functions. The feedback effects determine how quickly the


motivational inputs affect the individual viewer’s physiological systems and how the message-

individual effects evolve, accumulate, and decay across time. This is examined in detail below.

Dynamic Interplay between Motivational Inputs and Candidate Evaluation across Time

We have examined the interaction effects between the motivational inputs and candidate

evaluation and the dynamic system feedback effects. These interaction and feedback effects are

not only disentangled from one another, but also teased apart from time and estimated per

second. Next, we put all of these components together to examine how the system feedback

effects integrate the message-audience interaction effects to generate reactions across time. Often

in social and behavioral sciences, these integrated effects are the final observable behavior of the

dynamic system. Importantly, they can have different—sometimes even opposite—patterns

compared to the effects estimated per time unit (Busemeyer & Deiderich, 2010; Roe, Busemeyer,

& Townsend, 2001).

The estimated parameters for each physiological system are entered into the proposed

models. Following the common analytic strategy in time series analysis and the simulation

method by Wang et al. (2011), eight combinations of the three motivational inputs (A, P, N) are

selected to systematically demonstrate their effects on the physiological systems. They are: (1)

all three inputs are off (baseline); (2) only A is on; (3) only P is on; (4) only N is on; (5) A and P

are on, but N is off; (6) A and N are on, but P is off; (7) P and N are on, but A is off; and (8) all

three inputs are on. To facilitate interpretation, the magnitudes of all three motivational inputs

are kept at 1.2, which can be considered as moderate on the 0-2 scales. These motivational inputs

are controlled as a step input (i.e., turned on from zero to a fixed magnitude for a specified time

duration), which is commonly used to examine the accumulation and evolution of dynamic

effects (Luenberger, 1979). The step input duration is set to be 45 sec each, a little longer than


the actual 30-sec ads in our experiment but within the range of most ads (15-60 sec), so we can

have a clear observation of the evolution trajectories of the effects during a realistic political ad

exposure timeframe. After each step input, a 15-sec zero setting (i.e., no input) is used, which

allows the system to return to its natural baseline. Additionally, this zero input setting enables

observation of the decay of the previous input effect and avoids confounding the subsequent

input effect. Figure 4 provides a visual illustration of the eight input conditions: (1) baseline or 0,

(2) A, (3) P, (4) N, (5) AP, (6) AN, (7) PN, and (8) APN. By examining how a physiological

system reacts to the eight input conditions, we can systematically examine the motivational

effects as estimated by model parameters (see Figures 5-8). This facilitates understanding the

dynamic physiological systems with greater rigor and clarity than relying on the observed data

alone. In the latter situation, entangled exogenous and endogenous influences and can be quite

perplexing. A dynamic system is complex and with emergent features (Buzsáki, 2006), and

formal modeling and simulation becomes essential in understanding its behavior (Busemeyer &

Deiderich, 2010; Kelso, 1995; Ward, 2002; Wang et al., 2011).

The simulated effects are shown in Figures 5-8. In each figure, the eight input conditions

are represents as letters at the bottom. The corresponding step input durations are highlighted in

grey. The five panels on the left are dynamic physiological responses to ads featuring Obama,

whereas the five panels on the right show responses to McCain ads. From top to bottom,

candidate Eval scores range from strong support of Obama (Eval = -6) to strong support of

McCain (Eval = 6). A consistent pattern emerges across the four physiological measures.

[Insert Figures 4-8 about here]

First, the figures illustrate the dynamic nature of the physiological systems. Onset and

offset of a motivational input do not instantaneously bring the system to its equilibrium state.


Instead, upon exposure to motivational message input, it takes time for the physiological system

to reach its equilibrium state. Similarly, when external motivational message input is turned off,

it takes time for the system to decay back to its baseline. During this dynamic effect growth and

decay, a key role is played by system feedback. Feedback effects integrate the motivational input

effect to generate the dynamic trajectories depicted in Figures 5-8.

Second, it is interesting to note that when integrated across time by system feedbacks

(i.e., Figures 5-8), the influence of candidate evaluation on motivational inputs effects appears

different from its per time unit effect alone (i.e., Figures 1-3). As shown in each of Figures 5-8,

the five panels on the left (i.e., responses to ads featuring Obama) demonstrate similar response

directions to the eight input conditions. However, from the top to the bottom panel (i.e.,

increasingly favorable attitudes toward McCain), the response magnitude monotonically

diminishes. A similar phenomenon is observed among the five panels on the right (i.e., responses

to ads featuring McCain). Here, the response magnitude attenuates from the bottom to the top

panel (i.e., increasingly supportive of Obama). The two panels in the middle (i.e., neutral

candidate evaluation) show similar response patterns. These results indicate that people respond

intensely to ads featuring their favored candidate, while generally less responsive to ads featuring

the opponent. The powerful integration and moderation effect of system feedbacks is clearly

demonstrate by comparing this integrated effect pattern to the motivational inputs and candidate

evaluation interaction effects per second described earlier (Figures 1-3). First, a small system

input effect can accumulate through system feedback effects, growing into a much larger effect

across time. As shown in the top left panel in Figure 1, when arousing content increases from 0

to 1.2, HR of Obama supporters (blue solid line) drops about .036 of one standard deviation.

However, when arousing input effect is accumulated by feedback effects across time, it grows


many times larger, producing a HR deceleration of nearly .25 of one standard deviation

(Condition “A” in the top left panel of Figure 5). Second, the exogenous input effect, aggregated

and moderated by system feedback effects, may manifest in a direction different from the per

unit time estimation of the input effect. This, too, is the case in our data. In general, system

feedback effects attenuate the opposing trend of motivational input effects among people with

opposing candidate evaluations. System feedback effects energize responses to ads featuring the

favored candidate and attenuate, instead of reverse, responses to ads featuring the opponent. This

is even more evident in a supplementary simulation, in which the exact same model and model

parameters used to generate Figures 5-8 were implemented, but system feedback effects were

turned off (see the first author’s website for the supplementary simulation). When feedback

effects were turned off, motivational input effects across time were opposite among people with

opposing candidate evaluations. This is exactly consistent with the per time unit effects

illustrated in Figures 1-3.

Third, arousing content, positivity, and negativity influence different physiological

systems in different ways. As shown in Figure 5, the HR pattern predicted by the dynamic model

coincides with previous research measuring HR in response to emotional media messages.

Arousing content (Condition “A”) elicits large HR deceleration. Non-arousing (i.e., calm)

positive and negative messages (Conditions “P,” “N,” and “PN”) elicit HR acceleration, and

negativity elicits slower HR than positivity (P. Lang et al., 1993). However, if arousing content is

added to these conditions (Conditions “AP,” “AN,” and “APN”), HR becomes slower. Previous

analyses have seldom closely examined coactive messages (Conditions “PN” and “APN,” both

of which contain positivity and negativity simultaneously). This dynamic model clearly shows


HR acceleration when it is calm (Condition “PN”), but HR deceleration when arousing content is

added (Condition “APN”). This replicates findings by Wang et al. (2011).

The SCL dynamic model simulation is shown in Figure 6. Similar to previous findings

(e.g., Wang et al., 2011), the SCL model is most responsive to arousing content. Surprisingly,

however, the presence of arousing content (Conditions “A,” “AP,” “AN,” and “APN”) causes a

decrease in SCL. This is contrary to many previous findings using static methods, which

generally find that arousing content leads to an increase in SCL. The findings here are also

different from Wang et al.’s results, in which the only response of SCL to the eight input

conditions was a SCL increase in response to arousing content (Condition “A”). Some

speculation of these aberrant SCL findings seems warranted. The current model parameter

estimations are based upon observations from participants viewing relatively short, highly

persuasive stimuli (30-sec political ads). In contrast, Wang et al.’s parameter estimation was

based upon data from self-controlled 30-min entertainment television viewing. Additionally,

when the political ad experiment was conducted, the political campaign had been going on for

several months, with Hillary Clinton’s concession of the Democratic Party nomination to Obama

in early June marking the beginning of the general election race (Franz & Ridout, 2010). It is

possible that viewers had habituated to the arousing content of campaign ads, perhaps even

showing emotional responses similar to annoyance or boredom (Hastings, Stead, & Webb,

2004). It is also interesting to note that SCL is more complicated during the viewing of ads

featuring McCain compared to those featuring Obama. During the former, an input of positivity

and/or negativity (Conditions “P,” “N,” and “PN”) caused an increase in SCL among McCain

supporters and a decrease among Obama supporters.


Comparing the dynamic corrugator and zygomatic EMG side by side (Figures 7 and 8),

the two measures react to the eight input conditions in a generally opposite way, which is

consistent with psychophysiological theories and extant empirical evidence. Arousing content

(Condition “A”) increases corrugator but decreases zygomatic EMG. Positivity (Condition “P”)

decreases corrugator but increases zygomatic EMG. Negativity (Condition “N”) also decreases

corrugator but barely affects zygomatic EMG. It is interesting to note that coactive messages

(Conditions “PN” and “APN”) have a clear impact on the two facial EMG measures. When the

message is calm (Condition “PN”), corrugator activity decreases but zygomatic activity

increases, but when the message is arousing (Condition “APN”), the increasing or decreasing

activity is largely reduced.

Discussion

Consistent with a wealth of research on motivated attention (M. Bradley, 2009; A. Lang,

2006) and previous tests of DMA (Wang et al., 2011; Wang & Busemeyer, 2007), this study

supports the central role of motivational activation in the allocation of attentional resources,

which dynamically changes as a message unfolds. Furthermore, this study extends previous

DMA work by theorizing dynamic motivational influences of both message components and

viewers’ attitudes. It provides evidence of disparate attention among supporters of the two

opposing political candidates. This confirms previous work theorizing mediated cognition as

interactions between messages and individuals (e.g., A. Lang, 2006; Southwell, 2005), but is the

first attempt to test the interactions using dynamic models of physiological data.

The dynamic analysis extends theoretical understanding of the message-audience

interactions in two ways. First, it disentangles the message-audience interaction effects from the

information processing system feedback effects and also from time. The message-audience


interaction effects per second suggest that opposite candidate evaluations lead to opposite

attentional responses to the same message. Second, it examines how individuals’ attitudes to

candidates interact with message motivational content to affect attention across time. The

processing system feedback effects accumulate the motivational input effects, creating a larger

effect among supporting viewers while attenuating the effect among viewers supportive of the

opposing candidate.

The Cardiac-Somatic Coupling of Attention to Emotional Messages Featuring the Favored or

the Opposed Candidate

Increasing political polarization has been a growing concern for scholars and the public

(Mutz & Martin, 2001). The present study suggests that in addition to selective exposure,

selective attention to political information may contribute to this polarization. Despite reduced

exposure selectivity in a high-intensity election campaign (Cho, 2008), attentional selectivity can

be a critical component in determining how information processing influences campaign

message reception and effects. As revealed by the integrated, dynamic effects figures (Figures 5-

8), viewers with neutral candidate evaluations respond in the same way to both candidate’s ads.

However, for those who already prefer one candidate or the other, ads featuring a favored

candidate elicit intense responses, whereas ads featuring the opponent result in decreased

responsiveness. Furthermore, the larger the candidate evaluation difference, the more divergent

an individual’s responses are to a favored and opposed candidate’s message. This divergent

pattern is even more dramatic when examining the message-individual interaction alone (Figures

1-3).

From these figures, we can see a clear pattern of attentional selectivity based upon the

sensory information intake-rejection interpretation (e.g., Lacey, 1959, 1967) and the cardiac-


somatic coupling theory of attention (Obrist, Webb, Sutterer, & Howard, 1970; Cohen et al.,

2002). On the one hand, the cardiac-somatic coupling response pattern of attention suggests that

increasing external attention or sensory intake is associated with more dominant PNS activation,

leading to slower HR (A. Lang, 2006). Simultaneously, corrugator muscles tighten and

zygomatic muscles relax to facilitate perception by minimizing communication distraction and

noise (Bartoshuk, 1956; Haagh & Brunia, 1984). On the other hand, sensory rejection is

associated with more dominant SNS activity, leading to faster HR, decreased corrugator activity,

and increased zygomatic activity. The information rejection mode helps prevent external

disruption and facilitates internal focus and mental activities (e.g., imagination, decision making,

counter-argument) or facilitates preparation for behavioral responses (e.g., fight or flight,

communication, and signaling). This cardiac-somatic coupling of attention is consistently

revealed in the figures illustrating HR and facial EMG responses. Again, response patterns to the

same motivational message inputs vary largely by the viewers’ attitudes to the candidates.

Interestingly, depending on the viewers’ attitudes, the cardiac-somatic responses are

elicited by different dimensions of emotional content of the ads (see Figures 1-3). When ads

featuring the favored candidate become more arousing, or when ads featuring the opposed

candidate become more negative or positive, an information-intake mode is activated. Viewers

show decreased HR and zygomatic activity, and increased corrugator activity. When ads

featuring the favored candidate become more negative or positive, or when ads featuring the

opposed candidate become more arousing, viewers show increased HR and zygomatic activity,

and decreased corrugator activity. This information-rejection response pattern suggests that

viewers may be starting to engage in internal mental activities or action preparation. One

possible explanation is that with the Election Day on the immediate horizon, viewers were


already quite familiar with their favored candidate. Viewers might have been mostly intaking the

excitement delivered in ads with arousing content, whereas valenced information might have

elicited mental activities or behavioral tendencies, such as counter-arguing criticisms or

elaborating on supportive messages about their favored candidate. When viewing ads featuring

an opposed candidate, participants may have attempted to encode valenced information in an

effort to counter-argue against or criticize the opponent’s viewpoints. When the ad is arousing,

execution of mental activities or behavioral tendencies (e.g., arguments or counterarguments)

may occur, which interrupted the encoding process. More interestingly, however, the dynamic

system feedback effects intensify responses to ads featuring the favored candidate, but attenuate

responses to the ads featuring the opponent (Figures 5-8). At the integrated effect level, viewers

are responsive to ads featuring their favored candidate but rather irresponsive to ads featuring the

opponent. Also, it is interesting to note that response patterns of both candidates’ supporters to

their favored and opposed candidate are not perfect mirror images of each other. Perhaps this

indicates mutual influences of ideological differences and physiological reactivity (Oxley et al.,

2008).

These findings on divergent attention during political information processing suggest a

potential source for gaps in public opinion and political perceptions, such as the hostile media

effect (Huge & Glynn, 2010; Gunther & Liebhart, 2006), and the increasingly intensified

negativity of Democrats’ and Republicans’ evaluations of an opposing party president

(Abramowitz, 2010; Jacobson, 2006). People not only selectively expose themselves to

information that reinforces rather than challenges their beliefs (Iyengar & Hahn, 2009), but also

attend to the political information environment in a highly selective manner. Our data suggest

that individuals are more responsive to information about their favored candidate. This finding


does not support the common tactic of using negative ads attacking an opponent to mobilize

supporters or leaning voters. Furthermore, it seems that individuals are drawn by the excitement

about their favored candidate, and quickly engage in arguments and action tendencies if the

information is valenced. Further replications are needed—particularly connecting the findings to

post-exposure, long-term political perceptions. However, it seems likely that in addition to

selective exposure, selective attention to the same political information may contribute to

increased bias in political perceptions.

By accounting for the influence of political predispositions, interactions between political

attitudes and message content, and the dynamic nature of message processing, some inconsistent

results in the extant political ads literature might be resolved. An important and lingering

question addressed by current research is whether negative or positive ads have an advantage in

capturing or retaining audience attention during message exposure. Research generally relies on

various memory measures as indicators of attention or learning. Yet, empirical research has

revealed inconsistent findings. For instance, Shapiro and Rieger (1992) found that arguments in

negative ads were better remembered than those in positive ads. However, Geer and Geer (2003)

showed that recall did not differ between negative and positive political ads, and Basil, Schooler,

and Reeves (1991) found that positive ads were more likely to be remembered than negative ads.

Perhaps these seemingly disparate findings could be reconciled by acknowledging the dynamic

nature of both ads and information processing. The context of the information (e.g., the nature of

its preceding information, the length of the ad), might significantly affect whether negative or

positive information is advantageous for ad memory. Future research expanding on the current

study might include memory or thought listing measures to examine whether arguments or

counterarguments actually occur during the information rejection mode, and also further specify


the nature of mental activities or behavioral tendencies during ad viewing. For instance, Meirick

(2002) found that comparison ads prompted more counterarguments than negative ads. Post-

exposure thought listing and real time psychophysiological measures might illuminate whether

the ad content that prompts counter-arguing by audience members occurs during the information

rejection mode as indicated by the cardiac-somatic responses patterns.

Effects of coactivation of both motivational systems present another interesting finding.

Scant empirical data exists on how coactivation of both motivational systems affects

physiological responses and cognitive processes. However, these contexts may have important

implications for political ads research, because the widely used comparison ads are likely

coactive messages. In addition to HR findings that are consistent with those found by Wang et al.

(2011), this study finds somatic facial EMG data that couples with HR data. The data can be

interpreted from the cardiac-somatic coupling patterns of attention (Obrist et al., 1970; Lacey,

1967). When the coactive message is calm (Condition “PN”), HR accelerates, corrugator activity

decreases, and zygomatic activity increases, indicating a sensory rejection mode. When the

coactive message is arousing (Condition “APN”), the increasing or decreasing activity is

attenuated, which suggests that the PNS may become more active, competing with the SNS to

quiet down the body and starting to facilitate sensory intake. These speculations should be

further tested using experiments specifically designed to test coactive motivational activation,

such as during watching comparison ads (Shah et al., 2007).

Feedback Effects of the Physiological Systems

Consistent with the findings by Wang et al. (2011), this study provides evidence of the

first and second order feedback effects of the four PNS physiological measures. The system

feedback effects determine the dynamic nature of message processing and motivational effects.


First, they determine the speed of message effect activation and decay. As shown in the model

simulation figures (Figures 5-8), changes in motivational inputs do not elicit instantaneous

changes in physiological responses. Rather, because of feedback terms, it can take anywhere

from fractions of a second to a number of seconds before a physiological response system

reaches its equilibrium state. Second, the feedback effects determine the final level of growth and

the cumulative effect produced by a message motivational input. As illustrated by a simulation

example of a first order system provided by Wang et al. (2011), if message inputs and their

effects parameters are kept the same, dramatically different maximum and cumulative effects can

be produced by slightly varying the first order feedback parameter. Although the system

dynamics are much more complicated when the system has an additional second order feedback,

which produces an oscillation, the powerful influence of these feedback effects is demonstrated

in that simple example.

In the current study, it is interesting to compare the estimated system input effects

(Figures 1-3) to the integrated system outputs (Figures 5-8). As discussed earlier, the system

feedback effects accumulate the motivational inputs, producing larger effects, but they may also

alter effect directions by attenuating the opposing patterns among people with opposite attitudes.

It is a common feature of dynamic complex systems that unexpected effects at the integrated

system level can emerge from interactions between the interconnected system components

(Busemeyer & Deiderich, 2010; Ward, 2002; Wang et al., 2011). For example, a well-known

decision making phenomenon, preference reversal, is explained by a computational theory called

decision field theory from a dynamic system approach (Roe, Busemeyer, & Townsend, 2001).

The authors show that in a dynamic decision system formulated by their theory, a system input

with options A and B, where greater utility assigned to A than B, can generate an unexpected


system output in which B is preferred over A because of dynamic interactions between the

attention and evaluation components in the system. This is similar to our case, where

motivational input effects interact with system feedback effects, generating aggregated effects at

the system level that appear in a different direction from the motivational inputs effects alone.

The evidence of system feedback effects suggests three important implications for

message design and message effects research. First, consideration of message variables alone

may provide only a partial picture of message effects. Observed message effects usually are an

integration of both message effects and the processing system’s feedback effects. Message

variables controlled by media production practitioners, such as emotional content and production

features, produce their effects through human processing systems. It is important to carefully

study how these controllable message production features can achieve certain outcomes through

the moderation of dynamic processing systems. To accomplish this, researchers need to keep in

mind that message manipulations are probably not, in many cases, linearly related to outcomes.

As shown in the current study and the study by Wang et al. (2011), the processing system

feedback effects contribute to the nonlinear growth and decay of the message effects.

Second, consideration of time duration is critical to understanding message effects. The

dynamic nature of the message effects observed in the present study indicates that effect sizes

(and sometimes even directions) depend crucially on the status of message processing system on

the effect evolution trajectory when research observations are made. Cumulative message effects

depend on how long the system has been activated, whether it has reached its equilibrium state,

and if so, for how long. Empirical studies with the same experimental design and stimulus

manipulation but different stimulus durations and/or different measurement timings may find

divergent or even conflicting results based upon static analysis such as ANOVA. On the other


hand, dynamic analytical tools allow time to take central stage in examining message effects. By

estimating message variable effects per time unit, this procedure allows researchers to

disentangle message variable effects from the confounding effects of message duration.

Furthermore, dynamic models separate the effects caused by message variables from the

moderating and cumulating effects of the information processing system feedback effects.

Lastly, the demonstrated dynamic nature of message processing emphasizes the

importance of contextual effects in the field of communication. As specified by the feedback

terms, preceding stimuli or contextual factors can affect media processing, further illustrating

that media processing depends on prior history and experience. This is consistent with classic

media effects theories, such as excitation transfer theory (Zillmann, 1971). The formal dynamic

models tested here can help researchers further understand how communication messages create

contexts for one another, such as how the processing of ads is affected by program context (e.g.,

Potter et al., 2006) and how a political conversation can be influenced by group dynamics and

school/family environment (e.g., Hively & Eveland, 2009).

Limitations and Future Directions

Several limitations of this study warrant consideration. First, with limited data points, we

have restricted our models to include only the key interaction terms between the linear main

effect of motivational inputs and candidate evaluation. Future studies should obtain larger data

samples to test more complicated interactions, such as those involving quadratic motivational

terms and more sophisticated political attitude constructs, such as partisanship, political

ideology, or attitudes toward specific public policy issues. Second, contrary to much previous

literature, SCL found in this study showed a decreasing response to motivational inputs (see

Figure 6). Wang et al. (2011) also found that SCL was rather irresponsive based upon dynamic


analysis. One possibility is that uncontrolled structural features of the messages, such as music

and camera changes, might have influenced skin conductance (e.g., Potter & Choi, 2006). Future

studies should consider controlling these factors. In addition, skin conductance changes were

more responsive to positive and negative motivational inputs in ads featuring McCain, but this

change was not observed during the viewing of Obama ads. It is unclear why this pattern

emerges. Future research might explore the underlying reasons producing these results. Third,

participants’ evaluations of the two candidates were measured following message exposure. This

procedure was done in an attempt to avoid possible contextual effects of candidate evaluation

measures on the sensitive physiological measures. The impact of the stimulus ads on candidate

evaluation was expected to be minimal because the stimuli included a mix of randomly ordered

ads both for and against each candidate. However, the study did not include an experimental

check of the effects of the ads on post-exposure evaluations. Future studies might measure

candidate evaluations (or other political attitude constructs) before exposure to the stimuli. This

might be accomplished by including a pre-screening session.

Finally, this study focuses on the cardiac-somatic coupling pattern of attention during

political ads processing. Future research should examine other key components of information

processing, such as comprehension, memory, and attitude change, which would provide a more

comprehensive understanding of the dynamic interactions between messages and attitudes (Lee,

Roskos-Ewoldsen, & Roskos-Ewoldsen, 2008). In addition, different types of political messages

and communication channels, such as debates (Holbert, LaMarre, & Landreville, 2009),

conversations (Hayes, 2007), and online forums (Knobloch-Westerwick & Meng, 2009),

introduce different degrees of interactivity and information dynamics. Other types of political

messages could be tested and compared with political ads processing using the DMA framework.


More importantly, motivational content can affect selective exposure in a dynamic way (David,

Song, Hayes, & Fredin, 2007; Wang et al., 2011; Wang & Tchernev, in press); and means of

information acquisition (e.g., active selectivity or passive exposure), in turn, can affect cognitive

processing (Wise & Kim, 2008). Therefore, a natural next step is to integrate dynamic selective

exposure and selective attention to examine their mutual influences (Slater, 2007). This would

provide further insight into how citizens dynamically interact with political information streams

to construct a personalized political information environment.


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Table 1. Model Evaluation and Estimated Parameters for Competing HR Models

The Message Model

The Message and Interaction

Model

The Interaction

Model M (SE) M (SE) M (SE) Intercept -.45(.38) -.48(.40) -.42(.40) Feedback (lag 1) .62(.01)* .62(.01)* .62(.01)* Feedback (lag 2) -.03(.01)* -.04(.01)* -.04(.01)* A -.01(.12) -.02(.12) P -.01(.09) .001(.09) N .004(.11) -.01(.11) A×P .29(.20) .29(.20) A×N .20(.22) .18(.22) P×N -.14(.20) -.11(.20)

A2 -.14(.17) -.13(.17)

P2 -.04(.10) -.05(.10)

N2 .003(.14) .02(.14)

OM .02(.02) Eval .001(.02) Eval × OM .15(.20) Eval × A -.03(.01)* -.03(.01)* Eval × P .02(.01)* .02(.01)* Eval × N .01(.007)† .01(.007)† Eval × A × OM .04(.01)* .04(.01)* Eval × P × OM -.04(.01)* -.04(.01)* Eval × N × OM -.02(.01)* -.02(.01)* ASA .12(.07) .12(.08) .12(.08) DSA -.08(.07) -.08(.07) -.08(.07) ASA × DSA -.10(.05)* -.10(.05)* -.10(.05)* Gender .38(.13)* .39(.14)* .39(.14)* Race -.03(.07) -.03(.08) -.02(.08)

Model fit

R2 .3882 .3893 .3827

BIC 163.35 239.62 136.49 * p < .05, † p < .10


Table 2. Model Evaluation and Estimated Parameters for Competing SCL Models

The Message

Model


Model

The Interaction Model

M (SE) M(SE) M(SE) Intercept .07(.15) .07(.15) .07(.15) Feedback (lag 1) .48(.01)* .47(.01)* .48(.01)* Feedback (lag 2) .23(.01)* .23(.01)* .23(.01)* A .07(.04)† .07(.04)† P -.08(.03)* -.09(.03)* N -.06(.03)† -.06(.03) A×P -.02(.06) -.03(.06) A×N -.02(.07) -.01(.07) P×N .11(.06)† .10(.06)

A2 -.03(.06) -.03(.05)

P2 .06(.03)† .07(.03)*

N2 .04(.04) .03(.04) OM -.004(.005) Eval -.001(.002) Eval × OM .02(.04) Eval × A -.01(.004)* -.01(.005)* Eval × P .01(.003)† .01(.003)* Eval × N .01(.003)† .01(.003)* Eval × A × OM .01(.01)† .02(.01)* Eval × P × OM -.01(.004)† -.01(.004)* Eval × N × OM -.01(.004)† -.01(.004)* ASA .02(.03) .02(.03) .02(.03) DSA -.001(.03) -.001(.03) -.001(.03) ASA × DSA .11(.02)* .11(.02)* .11(.02)* Gender -.20(.05)* -.21(.05)* -.21(.05)* Race .05(.03)† .05(.03)† .05(.03)

Model fit

R2 .5626 .5631 .5567 BIC 142.40 219.74 116.61

* p < .05, † p < .10


Table 3. Model Evaluation and Estimated Parameters for Competing Corrugator EMG Models

The Message

Model


Model

The Interaction

Model M (SE) M(SE) M(SE) Intercept -.46(.35) -.47(.37) -.46(.37) Feedback (lag 1) .44(.01)* .44(.01)* .44(.01)* Feedback (lag 2) .17(.01)* .17(.01)* .17(.01)* A -.01(.09) -.01(.09) P -.08(.07) -.09(.07) N .12(.08) .13(.08) A×P -.001(.16) .001(.16) A×N .15(.17) .14(.17) P×N -.18(.15) -.19(.16)

A2 -.08(.13) -.08(.13)

P2 .12(.08) .11(.08)

N2 -.13(.10) -.13(.11) OM -.002(.01) Eval .001(.005) Eval × OM .02(.05) Eval × A .02(.01)† .02(.01)* Eval × P -.02(.01)* -.02(.01)* Eval × N -.01(.007)† -.01(.01) Eval × A × OM -.02(.01) -.04(.01)* Eval × P × OM .01(.01) .03(.01)* Eval × N × OM .02(.01) .03(.01)* ASA .03(.07) .03(.07) .03(.07) DSA -.001(.11) .18(.06)* .17(.06)* ASA × DSA .12(.08) -.10(.05)* -.10(.05)* Gender .21(.21) .26(.12)* .25(.12)* Race .05(.12) .03(.07) .03(.07)

Model fit

R2 .3417 .3481 .3374 BIC 146.10 223.45 120.32

* p < .05, † p < .10


Table 4. Model Evaluation and Estimated Parameters for Competing Zygomatic EMG Models

The Message

Model


Model

The Interaction

Model M (SE) M(SE) M(SE) Intercept -.15(.15) -.15(.15) -.15(.15) Feedback (lag 1) .72(.01)* .72(.01)* .72(.01)* Feedback (lag 2) .04(.01)* .04(.01)* .04(.01)* A -.09(.04)* -.05(.04) P .04(.03) .02(.03) N .09(.03)* .09(.03)* A×P -.05(.06) -.03(.07) A×N -.09(.07) -.07(.07) P×N -.04(.06) -.04(.06)

A2 .11(.05)† .07(.06)

P2 -.01(.03) -.001(.03)

N2 -.01(.04) -.02(.04) OM .001(.004) Eval .001(.005) Eval × OM -.02(.04) Eval × A -.01(.005) -.004(.004) Eval × P .01(.003)† .004(.003) Eval × N .01(.003)* .01(.003)* Eval × A × OM .01(.005)† .01(.01) Eval × P × OM -.01(.004) -.01(.004) Eval × N × OM -.01(.004)† -.01(.004)† ASA -.16(.03)* -.17(.03)* -.16(.03)* DSA .02(.03) .02(.03) .02(.03) ASA × DSA .04(.02)† .03(.02)† .03(.02)† Gender -.13(.05)* -.14(.05)* -.13(.05)* Race .08(.03)* .09(.03)* .09(.03)*

Model fit

R2 .6166 .6168 .6109 BIC 142.37 219.72 120.33

* p < .05, † p < .10


Figure 1. Psychophysiological Responses to Obama (on the Left) and McCain (on the Right) Ads

as a Function of Arousing Content of the Ads and Viewers’ Candidate Evaluation.

Ads Featuring Obama Ads Featuring McCain



as a Function of Negativity of the Ads and Viewers’ Candidate Evaluation.




as a Function of Positivity of the Ads and Viewers’ Candidate Evaluation.



Figure 4. Eight Conditions of Motivational Inputs with Arousing Content, Positivity, and Negativity Being On and Off During

Different Time Periods (Input Magnitude = 1.2)

AN APN PN 0 A P N AP


Figure 5. HR Dynamic Responses to Motivational Inputs in Obama Ads (Left Panels) and McCain Ads (Right Panels)

0 A P N AP AN APNPN 0 A P N AP AN APNPN



Figure 6. SCL Dynamic Responses to Motivational Inputs in Obama Ads (Left Panels) and McCain Ads (Right Panels)

Ads Featuring McCain Ads Featuring Obama



Figure 7. Corrugator EMG Dynamic Responses to Motivational Inputs in Obama Ads (Left Panels) and McCain Ads (Right Panels)




Figure 8. Zygomatic EMG Dynamic Responses to Motivational Inputs in Obama Ads (Left Panels) and McCain Ads (Right Panels)


Ads Featuring McCain Ads Featuring Obama

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