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Goal Control of Attention to Advertising:
The Yarbus Implication
RIK PIETERS AND MICHEL WEDEL *
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* Correspondence can be addressed to Rik Pieters, Professor of Marketing, Tilburg University,
Department of Marketing, PO BOX 90153, The Netherlands, Telephone: (+31) 13 466 3022,
Email: pieters@uvt.nl or Michel Wedel, Dwight F. Benton Professor of Marketing, Office
D7202, Michigan Business School, 701 Tappan Street, MI 48109-1234 Ann Arbor, USA,
Telephone: (+1) 734 936 3228, Email: wedel@umich.edu. We thank Verify International for the
eye-tracking data.
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An eye tracking experiment with four processing goals and a free viewing condition reveals goal
control of attention even during a few seconds of self-paced ad exposure. An ad-memorization
goal enhanced attention to the body text, pictorial and brand. A brand-learning goal enhanced
attention to the body text but simultaneously inhibited attention to the pictorial. This supports the
thesis that ad informativeness is goal-contingent. Differences in pupillary diameter between ad
objects but not between processing goals reflect the pupil’s role in maintaining optimal vision.
Implications of the findings for advertising theory and avenues for future research are indicated.
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What do consumers look at in advertisements, and do the goals that they have in mind
systematically influence what they attend to? Is attention predominantly a “dumb process”
guided by the perceptual salience of the ad stimuli, after which the goals of consumers control
the higher-order cognitive processes that work on the attended information? Or, do consumers’
processing goals already have systematic effects on their patterns of visual attention to ads?
In view of the potential implications of these questions for advertising theory and the
importance of advertising to brand and firm performance, it is surprising that almost no research
has directly addressed them. Not only in advertising, but also in attention research at large, most
research effort thus far has been devoted to understanding reflexive control by stimulus factors
(Treisman and Gelade 1980; Itti 2005), in advertising mainly the size of ads and the objects
contained therein (Janiszewski 1998; Lohse 1997; Pieters and Wedel 2004). As a result, the role
of goals in attention to ads (and other complex visual stimuli) is still largely unknown, which has
been identified as an important gap in our knowledge (Henderson and Hollingworth 1998;
Kingstone, Smilek, Ristic, Friesen, and Eastwood 2003; Pashler, Johnston, and Ruthruff 2001).
The pioneering work of Alfred Yarbus (1967) offers an optimistic initial answer. He
recorded the eye movements of a single participant who looked at the painting “The Unexpected
Visitor” of Ilya Repin, under different task sets. Each recording lasted three minutes, and the
participant was instructed to freely examine the painting, estimate the material circumstances of
the family in the picture, give the ages of the people in the picture, or determine how long the
unexpected visitor had been away from the family. Inspection of the raw eye recordings during
each of these specific conditions led to the conclusion that “depending on the task in which a
person is engaged, that is, depending on the character of the information which he must obtain,
the distribution of the points of fixation on an object will vary correspondingly, because different
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items of information are usually located in different parts of an object” (Yarbus 1967, 19). In
other words, Yarbus’ thesis is that the informativeness of objects in a scene is contingent on the
specific processing goal that is activated, and that eye movements reflect the extent to which
these objects are informative. Our research pursues the implications for advertising. It
investigates the influence of consumers’ processing goals on visual attention to ads and four key
objects--design elements--that they are comprised of, to assess the extent to which advertising
informativeness is contingent on consumers’ processing goals.
However, it is not at all evident that there should be systematic effects of processing
goals on attention to advertising under natural exposure conditions. This is because the topdown, voluntary control of attention by goals involves slower serial processes than the fast
parallel processes underlying bottom-up, reflexive control by stimulus factors (Treisman and
Gelade 1980; Wolfe 2005). Therefore processing goal effects may have a lower likelihood of
surfacing during the few seconds that consumers typically spend on ads during self-paced
exposure, than when viewing the “Unexpected Visitor” for a forced exposure of three minutes.
Moreover, consumers are usually exposed to multiple ads surrounded by editorial material rather
than to a single isolated scene, and the ensuing competitive clutter may favor reflexive control
and hinder systematic goal control (Itti 2005). Heterogeneity across consumers (Rosbergen,
Pieters, and Wedel 1997) further impedes the expression of goals in identifiable attention
patterns. In addition, processing goals during ad exposure are typically more abstract than
Yarbus’ concrete instructions to search for specific cues, such as the ages of the people in the
“Unexpected Visitor.” Finally, in an influential review, Viviani (1990) questioned more
generally whether any higher-order mental process would be systematically reflected in eyemovements. It is thus not apparent that reliable goal effects on attention to advertising exist at all.
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Therefore, we first propose a conceptual framework that describes how visual attention to
objects in ads may be contingent on consumers’ processing goals. We focus on four general,
design objects each with a specific function in advertising, that is, the brand, pictorial, headline,
and body text, rather than on idiosyncratic, natural objects, which are present in some but not in
other ads and are difficult to define uniquely. This enables predictions about the informativeness
of these design objects under particular processing goals, which we test in an experiment with a
baseline condition of free viewing and four processing goals, with 220 participants and 17 ads,
using eye movements and pupillary diameter as process measures of attention.
GOAL CONTROL OF ATTENTION TO ADVERTISING
The sheer volume of visual stimuli that simultaneously call for attention surpasses the
processing capacities of any consumer. Attention therefore selects specific stimuli and the
objects they contain, and further engages in these, which occurs covertly in the visual brain. In
most real world situations, covert attention and overt attention, which is expressed in observable
eye movements, are tightly connected, such that covert attention can be inferred from patterns of
eye movements (Findlay 2005), as indicated in figure 1.
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Insert Figure 1 about here
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Attention Selection and Engagement
Eye movements on ads involve fixations--moments that the eye is relatively stable--and
saccades--ballistic movements between fixation locations to redirect the gaze. During a fixation
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an object or location in the ad is projected via the line of sight onto the fovea, the small area of
the retina with the highest acuity, for detailed visual processing (Rayner 1998). Attention
selection involves bringing an ad object into the focus of attention, and is reflected in at least one
eye movement to fixate it. Attention engagement is the process of sustaining attention to an
already selected object, and has duration and intensity components. Duration of attention is
reflected in longer gaze durations on the object, which is achieved by re-fixations (Russo and
Leclerc 1994). Intensity of attention has been linked to changes in pupillary diameter. The pupil
is the eye’s aperture through which light passes, and changes in its diameter ensure optimal
vision under varying luminance conditions, distance to the stimulus, and required visual angle.
Because the pupillary diameter has been found to enlarge with rising processing intensity, it is
deemed a reporter variable for the latter, although there is no direct causal relationship and the
underlying cortical mechanism is still unknown (Beatty and Lucero-Wagoner 2000).
Attention selection and engagement are determined bottom-up by the salience of ad
objects and top-down by their informativeness to consumer goals. A large psychological
literature addresses the assessment of salience and informativeness, but disagreement still exists
on their definition and operationalization. We review these two concepts and place them within
our conceptual framework.
Ad Objects and Salience
The brand, pictorial, headline and body text are design objects with specific functions in
ads (Figure 1, left) (Book and Schick 1997). The brand contains all textual and pictorial signs
that uniquely identify the brand, such as its name, logo and trademark. The pictorial conveys one
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or more aspects of the message in (photo)-graphic form and often shows the product in its usage
context. The headline contains the leading text, usually in large font size, expressing the key
message of the ad and anchoring the meaning of the pictorial. The body text is in smaller font
size, provides support for the message claim and details about features and benefits.
Ad objects capture attention reflexively and immediately when they are salient (Figure 1,
left). The salience of an ad object depends on its local contrast with other objects on basic
perceptual features, such as surface size, form, color, and luminance (Itti 2005; Wolfe 2005). The
surface size--space--of ad objects is a perceptual feature with “a privileged status” in visual
attention (Yantis 2000, 80). First, a larger surface size of an object facilitates figure-ground
segmentation and increases its salience, so that larger objects tend to “pop-out” (Treisman and
Gelade 1980). Second, because the eyes move in space, objects that occupy more of the ad’s
space have a higher likelihood of being fixated, even if eye fixations would be randomly
distributed. In past research on visual attention to advertising, surface size effects on attention
have been found to be pervasive (Pieters and Wedel 2004; Wedel and Pieters 2000). It is
therefore important to control for the surface size of ad objects, to make accurate and unbiased
inferences about their informativeness, based on eye movements (Rayner et al. 2001).
Processing Goals and Informativeness
Consumers may freely view and explore ads that they are exposed to without a particular
task in mind (Janiszewski 1998; Kahneman 1973). But they may also pursue a specific goal,
such as learning new information about the advertised brand or determining the attractiveness of
the ad (Lichtenstein and Srull 1987). Processing goals focus consumers’ attention on the stimuli
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that they are exposed to and on the information contained in them. Such goals are of intermediate
abstractness between low-level concrete goals, such as gauging the age of people (Yarbus 1967)
or searching for a specific target in a scene (Wolfe 2005), and high-level abstract goals such as
the pursuit of freedom or health (Kruglanski et al. 2002; Vancouver and Austin 1996).
Processing goals vary in target and content. The goal target can be the ad as a whole or
the brand that is advertised (Keller 1987). With respect to goal content, we distinguish evaluation
goals (Hilton and Darley 1991; Wyer and Srull 1989), which involve judging the value of the
target on a specific dimension (such as its usefulness or attractiveness), from learning goals
(Dweck and Leggett 1988; Midgley, Kaplan, and Middleton 2001), which involve acquiring
knowledge about the target and storing it in memory for later use. These target and content
dimensions define four classes of processing goals with specific instances, only a few of which
have been used in prior attention research (Henderson and Hollingworth 1998), and mostly
without explicit comparisons between them. We examine the influence of four processing goals,
as defined by the goal target and content dimensions, spanning a wide range of possible
intermediate processing goals, and compare these to a baseline condition of free viewing.
Objects in ads receive attention voluntarily when they are more informative (figure 1).
That is, processing goals activate representations of objects in long-term visual memory that are
perceived to be relevant to the goal (Friedman 1979). These representations are held in working
memory and compared with the objects actually appearing in the ad, which happens faster and
more accurately to the extent that they match (Biederman 1987). Activating a processing goal
may enhance or inhibit the association with certain objects. For example, when the participant in
the study of Yarbus (1967) tried to determine the material circumstances of the family in the
painting of the “Unexpected Visitor,” attention to the furniture in the room was enhanced, but
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attention to the faces and clothing of the people was inhibited. The final attention priorities of ad
objects are the integration of their salience and informativeness, which have similar effects on
attention (Spratling and Johnson 2004). Attention selects and engages in the prioritized ad
objects through enhancement and inhibition, leading to eye movements to foveate them.
Information extracted during eye fixations on ad objects updates the attention priorities until the
goals are sufficiently attained and attention moves on.
Surprisingly, previous quantitative research (reviewed in Henderson and Hollingworth
1998) has typically compared attention to different objects in the same scene, or to the same
object in different scenes, to determine the informativeness of these objects. This assumes that
informativeness is a property of objects per se or of their relationship with the rest of the scene,
which is misguided. Instead, and in line with the Yarbus thesis, we propose that the
informativeness of an ad object resides in its linkage to a processing goal, which thus can only be
revealed by comparing attention to that ad object between different processing goals. That is,
because their salience is invariant across goals, the informativeness of ad objects can be
separated from their salience by comparing attention to them between different processing goals.
This proposal is summarized in figure 1 and formalized in appendix 1.
PREDICTIONS
Because we focus on three attention indicators, four ad objects and their surface size, and
four processing goals and a free viewing baseline, the number of possible effects is too large to
offer specific predictions for each. Moreover, the current embryonic stage of knowledge about
goal control of attention to advertising and other complex scenes (Kingstone et al. 2003; Pashler
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et al. 2001) permits a number of broad predictions only, which we present here.
Attention Selection and Engagement. We predict that processing goals have larger effects
on the duration of attention engagement than on attention selection and intensity of attention
engagement, based on the following. The little that is known about goal control of attention
predominantly comes from target search tasks (Pashler et al. 2001), where opposite effects have
been found. There, participants need to locate a single target object among a large set of
perceptually similar distractor objects, and targets and distractors are often defined on a single
perceptual feature only. Then, goals influence attention selection most. The situation in
advertising is very different, because ads contain four perceptually distinct objects that vary on
multiple perceptual features, which makes selecting them easy, and the activated goals do not
call for the selection of a single object, for example, the pictorial, at the expense of all others.
Therefore, goal control of attention selection is expected to be minimal here.
Goal control of the intensity of attention engagement should be small as well, but goal
control of the duration of attention engagement should be larger. That is, once an ad object is
selected, attention is engaged to pursue the activated goal, which can be done through increased
attention duration or intensity, with an inherent tradeoff between these. Attention engagement is
required to extract, integrate and store the information from the various ad objects. When
exposure to print ads is self-paced, gaze durations are typically a few seconds only and easily
expandable (Pieters and Wedel 2004). Under such conditions, we expect it to be more efficient to
adapt the required attention resources for a particular goal by adjusting the duration rather than
the intensity of attention per unit of time, that is, “by attending longer instead of harder.”
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H1:
Processing goals influence duration of attention engagement most, and attention
selection and intensity of attention engagement least.
Advertisement Objects. We predict that compared to the brand, pictorial and body text,
attention to the headline will be least prone to processing goal control, because of the headline’s
perceptual salience and coordinating role in advertising. That is, the headline stands out due to its
large font type and central position in the ad, and it summarizes the ad’s main message and
anchors the meanings in the pictorial (Book and Schick 1997), so that it is highly salient and
informative across all processing goals (Rayner et al. 2001). In line with this, attention
engagement with the headline diminishes little across repeated ad exposures (Pieters, Rosbergen,
and Hartog 1996).
H2:
Compared to the brand, pictorial, and body text, attention to the headline is least
prone to processing goal control.
Processing goals. We predict that overall attention to ad objects is highest for learning
goals, intermediate for evaluation goals, and lowest for free viewing, based on the following
reasoning. During free viewing, object salience primarily determines attention, which happens
quickly (Friedman 1979; Janiszewski 1998). Conversely, when a specific processing goal is
activated, information from the ads needs to be processed, evaluated, integrated and stored,
which requires attention. Thus, we expect that less attention will be deployed during free viewing
than under each of the processing goals. Further, we predict higher levels of visual attention
under learning goals than under evaluation goals. This is because focused attention is required
during learning tasks to determine the novelty and quality of the information in the ad, to
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integrate this with existing knowledge and to store the results in memory for future use (Bettman,
Johnson, and Payne 1990), which takes attention time. On the other hand, evaluation is generally
a faster and more automatic process, in particular if it concerns feelings of appreciation (Pham,
Cohen, Pracejus, and Hughes 2001). In fact, because evaluation is an (almost) immediate process
that proceeds in a few seconds or less (Ferguson and Bargh 2003), the divergence in attention to
ad objects between learning goals versus evaluation goals and free viewing will be large.
H3:
Attention to ad objects is largest for learning goals, intermediate for evaluation
goals and smallest for free viewing.
Processing Goal Influences on Specific Ad Objects. First, one would clearly expect attention to
the brand object to be higher under brand-directed than under ad-directed goals and free viewing
(Keller 1987). Under a brand-directed goal, consumers need to retrieve brand-relevant
information from memory and match this with the information about the brand in the ad, which
takes time. Under an ad-directed goal and free viewing, on the other hand, the brand is not as
informative and attention to it should be less and mostly determined by perceptual salience.
Second, we predict attention to the pictorial to be highest under an ad memorization goal,
when the aim is to learn the perceptual features of the ad in detail. Memory for pictures is vast
and efficient (Childers and Houston 1984), which should stimulate consumers to prioritize
picture processing when learning ads for future recollection. This is in line with Costley and
Brucks’ (1992) speculation that compared to text, pictorials are not only remembered better, but
also relied upon more under ad learning goals.
Third, we predict attention to the body text to be highest for brand learning. Text
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processing is an effortful and slow process (Rayner 1998), on which usually limited time is spent
during ad exposure (Wedel and Pieters 2000), often less than a second. However, the body text
in print ads typically describes product features and similarly detailed information, which are
relevant to learn about the brand. Moreover, there appears to be a cultural predisposition to
anticipate such detailed, precise information to be in the body text rather than the pictorial of ads
(Kenney and Scott 2003). Therefore, one would expect enhancement of attention to the body text
of print ads under brand learning, even if it would not contain the desired or any relevant
information. In support of this, Rayner et al. (2001) found that consumers who needed to make a
decision about an advertised brand fixated the text more frequently than did other consumers.
H4:
Attention is highest for the brand under brand-directed goals, for the pictorial
under ad learning goals, and for the body text under brand learning goals.
Support for hypotheses 1 and 2 would demonstrate that rather than indiscriminately
raising all attention indicators for all ad objects, goal control is confined to the duration of
attention (hypothesis 1), for the brand, pictorial and body text in ads (hypothesis 2). It would also
reveal how even attention to ad objects of the same modality--the headline and the body text both
being textual--is under differential goal control, because of their functions in ads and relevance
to the goals. Supports for hypotheses 3 and 4 would provide first evidence of how specific
means-end linkages between processing goals and ad objects systematically influence visual
attention. Taken together, this would point out that far from being a mechanic, dumb process
driven reflexively by the salience of ad stimuli and the objects contained therein, visual attention
to advertising adapts rapidly to the requirements of specific processing goals.
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EXPERIMENT
Method
Two hundred and twenty regular consumers were recruited by a market research agency
for the experiment (52% females; 27% between 16-25 years, 15% between 26-35 years, 39%
between 36-45 years, and 19% between 46-55 years). All had normal or corrected-to-normal
vision, and none had participated in eye-tracking research before. They received 15$ for
participation. Participants were randomly assigned to one of five conditions of a one-factor
between-subjects design, that is, the baseline free viewing condition, and respectively: (1) ad
memorization, (2) ad appreciation, (3) brand learning, and (4) brand evaluation. All material in
the five conditions was identical, except for the processing goal instruction described later.
All 17 full-page ads from the most recent issue of “Allerhande” magazine were selected.
This is a free, popular magazine of the largest retailer in the Netherlands, containing articles and
ads about food and cooking, and none of the participants had seen this issue before (although
some may have been familiar with some ads). The ads were for frozen vegetables (brand:
Bonduelle), food wraps (Casa Fiesta), canned fruit (Del Monte), pasta sauce (Heinz), ice cream
(Hertog), sausage (Huls), frozen pizza (Iglo), cookies (Liga), cake (Maitre Paul), bread (Meggle),
cheese (Milner), spices (Pataks), healthy snacks (Quaker), frying oil (Remia), softdrink (Sisi),
fruit juice (Tropicana), and dairy drink (Yakult). All ads contained a brand, pictorial, headline
and body text. The surface sizes of the four ad objects in each of the seventeen target ads were
determined with specialized software, by drawing the appropriate boxes and polygons around
them. In case the brand name appeared in the headline, it was counted to the brand object.
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Data Collection
Data collection was in cooperation with Verify International, a company specialized in
eye-tracking research. All instructions and stimuli were presented on NEC 21-inch LCD
monitors in full-color bitmaps with a 1,280 x 1,024 pixel resolution. Throughout the experiment,
participants continued to a next page by touching the lower right corner of the (touch-sensitive)
screen, as when paging. Infrared corneal reflection methodology was used for eye tracking
(Duchowski 2003). The specific equipment (Verify International) leaves participants free to
move their heads in a virtual box of about 30 centimeters. Cameras track the position of the eye
and head, allowing continuous correction of position shifts. Measurement precision of the eyetracking equipment is better than 0.5 degree of visual angle.
Upon entering the facilities of the market research company, participants were seated
behind the eye-trackers. After a brief warm up eye-tracking task, they read the following
instructions on the computer screen: “Now a set of advertisements follows,” with the subsequent
text for each of the five conditions. Baseline free viewing: “Explore these freely as you would at
home or in a waiting room.” Ad learning (memorization): “Try to memorize the advertisements.
Try to remember each advertisement as good as possible.” Ad evaluation (appreciation):
“Determine how attractive or unattractive each advertisement is to you. Try to take account of
the beautiful and ugly aspects of each advertisement in your judgment.” Brand learning: “Use
the advertisements to collect information about the brand products. Try to learn something new
about each brand product.” Brand evaluation: “Use the advertisements to determine how good or
bad the brand products are. Try to form an evaluation of each brand product using the
advertisement.” In the remainder we will use the terms “ad memorization” and “ad appreciation”
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goals, to denote our specific operationalization, and to distinguish them from the “brand
learning” and “brand evaluation” goals. The 17 target ads were shown each with the
accompanying editorial page from the magazine, preceded by the front cover and trailed by the
back cover of the magazine. Since previous research has shown serial-position effects on
attention to print ads (Wedel and Pieters 2000), the order of ads was randomized across
individuals. Nine ads were on the right page and eight were on the left page.
Visual Attention Indicators
Attention was indicated by (1) selection, (2) gaze duration, and (3) pupillary diameter.
Selection denotes whether the particular ad object is fixated (1) or not (0) by a particular
participant. Gaze duration is the total time (seconds) that a participant who selected a particular
ad-object attends it. Pupillary diameter is the average diameter of the participant’s pupil (mm)
during attention engagement with a particular ad object. Hence, gaze duration and pupillary
diameter are conditional on whether or not a participant selected a particular object, and our
statistical analysis described later accounts for this data structure. Since the distance between a
participant’s eye and the ad may influence pupillary diameter and possibly other eye-tracking
measures (Beatty and Lucero-Wagoner 2000), we use it as a control variable in the analyses.
Table 1 gives summary information of the surface sizes and attention indicators.
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Insert Table 1 about here
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Analysis Approach
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There are three dependent variables and a control (selection, gaze duration, pupillary
diameter, eye-ad distance), measured on four ad objects (brand, pictorial, headline, body text),
for seventeen ads, and two hundred and twenty participants assigned to one of five goal
conditions (free viewing, ad memorization, ad appreciation, brand learning, brand evaluation).
The attention measures are not normally distributed, but correlated. That is, attention selection is
binary (yes-no), and gaze duration, pupillary diameter and eye-ad distance are positive, but
observed only if an ad object is selected by attention. Surface sizes of ad objects strongly affect
attention and these effects may differ across goals (Rosbergen, Pieters, Wedel 1997), so they
need to be accounted for, although we do not have specific predictions on the magnitude or
direction of the effects. Goals vary between participants, but participants are heterogeneous so
that individual differences need to be accommodated (Hutchinson, Kamakura and Lynch 2000).
These characteristics of our data make the application of standard ANOVA techniques
problematic, if not impossible. We have thus developed a statistical model that adequately
represents the data-generating mechanism and formalizes the conceptual model in figure 1. The
model (see appendix) is formulated and estimated in the Bayesian framework, which has the
operational advantages that it facilitates dealing with mixed outcome variables, heterogeneity
across individuals, correlated dependent measures, and missing data (Rossi, Allenby and
McCulloch 2005). In the model, processing goals influence attention parameters hierarchically,
consistent with recent views that top-down influences--including goal effects--in visual
perception reflect a hierarchical Bayes structure (Lee and Mumford 2003).
Rather than providing the parameter estimates of the model, we compute from them
quantities that are directly interpretable as means of the attention measures for each of the five
goal conditions, and their standard deviations. We do this as an integral part of the Bayesian
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estimation procedure, which on the one hand enables appropriate statistical testing based on
accurate assumptions about the data generating mechanism, yet on the other hand provides
results that can be interpreted analogously to those of standard ANOVA.
RESULTS
Attention selection ranges between 97% for the pictorial and 67% for the body text (table
1). Average gaze duration is between 1.52 sec for the pictorial and 0.83 sec for the body text, and
average pupillary diameter is between 3.40 mm for the pictorial and 2.30 mm for the body text.
The average time that participants attended to each of the ads under the current self-paced
exposure conditions is 4.1 seconds only. The goal control effects under these short exposure
durations are summarized in table 2 (hypotheses 1 and 3) and table 3 (hypotheses 2 and 4).
Overall Differences between Processing Goals
In support of hypothesis 1, there are no differences between processing goals in attention
selection and the intensity of attention engagement, but large differences in the duration of
attention engagement. Table 2 shows that attention selection of the average ad object is 92.1%
for free viewing (transforming 0.888 in table 2 by the Normal cumulative distribution function,
as dictated by the model specification), 94.7% for ad memorization, 88.8% for ad appreciation,
90.4% for brand learning, and 93.0% for brand evaluation, with no significant differences among
them. Also, the intensity of attention engagement in the average ad object, as reflected in
pupillary diameter, does not differ significantly between processing goal conditions, varying
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from 3.43 to 3.55 mm. Yet, and as predicted, processing goals had a systematic influence on the
duration of attention engagement. Gains and losses in duration of attention under the four
processing goals relative to free viewing are plotted in the left part of figure 2.
In support of hypothesis 3, the duration of attention to the average ad object was much
higher under the two learning goals, ad memorization (1.238 sec) and brand learning (1.047 sec),
than under free viewing (0.888 sec), an increase of respectively 39% and 18%. Unexpectedly and
counter to hypothesis 3, the duration of attention under the ad appreciation goal (0.847 sec) was
as low as under free viewing, rather than being intermediate. Attention engagement under brand
evaluation (1.056 sec) was as high as under brand learning. The average duration of attention to
each ad as a whole under the various processing goals can be easily derived from table 2 (four ad
objects-times-the average gaze duration per ad object). The average duration of attention to ads
under ad appreciation and free viewing are respectively 3.388 and 3.552 sec only, which
indicates the rapidity with which ad appreciation goals were implemented. Although the surface
sizes of the ad objects had a significant effect on each of the three attention measures, there were
no differences between processing goals in the effects of the surface sizes on attention (table 2).
This underlines the goal control of attention to the ad objects per se.
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Insert Table 2 and Figure 2 about here
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Differences in Attention to Specific Ad Objects
Because, as hypothesized, processing goals influenced the duration of attention
engagement with the ad objects, independent of their surface size, and did not influence attention
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selection or intensity of attention engagement, we only present the former effects in table 3 (none
of the other effects are significant). The predicted influence of processing goals on attention
engagement to specific ad objects is prominent. To facilitate comparisons of these processing
goal effects on each of the ad objects, we plot the gains and losses relative to the baseline free
viewing condition in the right part of figure 2.
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Insert Table 3 about here
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In support of hypothesis 2, there are no significant differences between processing goals
in duration of attention to the headline, but large differences for the other three ad objects.
Although average duration of attention to the headline is lowest under an ad appreciation goal
(1.085 sec) and highest under a brand evaluation goal (1.379 sec), none of the differences
between goals is significant. Note that across the 17 ads, the average number of words in the
headline (4.8) is much smaller than the average number of words in the body text (61.1). Still,
the duration of attention to the headline across the five goal conditions is 2.2 times longer (1.285
sec) than to the body text (0.595 sec), and sufficient to fully absorb its meaning (Rayner 1998).
This underscores the importance of the headline in advertising, and its high informativeness
irrespective of the activated goal. It also reveals the predicted differential impact of processing
goals on attention to two ad objects of the same modality, the headline and body text.
In support of hypothesis 4, duration of attention to the pictorial is highest under ad
memorization (1.459 sec), and much higher than under free viewing (1.133 sec; the average gain
under ad memorization being 326 ms across the 17 target ads). In further support of hypothesis 4,
duration of attention to the body text is highest under brand learning (0.967 sec) and much higher
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than under free viewing (0.251 sec, a gain of 285%). Unexpectedly however, attention to the
brand is not higher under brand-directed than under ad-directed goals. On the contrary, attention
to the brand is highest under an ad memorization goal (1.342 sec) and equal to or less than that
amount under the other three processing goals and free viewing (1.000 sec on average). The
attention advantage of over 300 ms for ad memorization is large in view of the overall small gaze
durations. We will return to this unexpected result in the final section.
Figure 2 gives the complete attention pattern for each processing goal relative to the free
viewing situation. It demonstrates how ad memorization clearly raises attention to the brand,
pictorial and body text. The attention pattern under ad appreciation is remarkably similar to that
under free viewing, which points to the possibility that consumers partially adopted an ad
appreciation goal during free viewing, which we will discuss later.
Recall that in support of hypothesis 4, duration of attention to the body text was largest
under brand learning. Figure 2 shows that this attention gain for the body text comes with a
simultaneous attention loss for the pictorial under this goal. Duration of attention to the pictorial
was 1.133 sec during free viewing, but dropped to 0.871 sec during brand learning (a 23% loss).
The simultaneous enhancement and inhibition of attention is remarkable, and first evidence to
our knowledge of such effects during rapid exposure to complex scenes--beyond demonstrations
for neurons in the visual brain (Spratling and Johnson 2004).
Finally, the body text gains attention under the brand evaluation goal (0.723 sec) but less
so than under the brand learning goal (0.967 sec), and this gain is not accompanied by a
simultaneous loss of attention to the pictorial. As a consequence, the overall duration of attention
to the ads under the brand evaluation and brand learning goals is the same, although their
attention patterns for specific ad objects differs substantially.
23
DISCUSSION
The findings of this study demonstrate the rapid and systematic influence that processing
goals have on visual attention to advertising, and show that the informativeness of ads is
contingent on the goals that consumers pursue during exposure to them. Although consumers
examined each of the ads for an average of a little over four seconds only, processing goals
strongly affected the duration of attention engagement with the brand, pictorial and body text.
The four processing goals promoted unique patterns of visual attention, thereby reflecting the
distinctive informativeness of the ad objects under each of them. Specifically, and compared to
free viewing, (1) informativeness of the ad objects was similar under an ad appreciation goal, (2)
the body text was more informative under a brand evaluation goal, (3) the body text was more
but the pictorial was simultaneously less informative under an brand learning goal, and (4) body
text, pictorial and brand were all more informative under an ad memorization goal. These effects
of processing goals on attention emerged while controlling for the surface size devoted to the ad
objects, the effects of which were not found to be goal contingent, which is remarkable given the
consistent effects of surface sizes found in this and previous research.
Goal control was strong for the duration of attention to the brand, pictorial and body text,
but not for attention to the headline, as hypothesized. In fact, differences between goals in
duration of attention were largest for the body text and smallest for the headline. This highlights
the goal-contingency of informativeness, because whereas the headline was equally informative
for all goals, the body text was least informative under free viewing and most under the brandlearning goal, even though both ad objects are textual in nature. It accentuates the specificity of
24
goal control of attention, and for instance that brand-learning goals do not promote processing of
textual information per se, but selectively render body text more informative.
Unexpectedly, the duration of attention to the brand was higher under an ad-directed
(memorization) goal than under the predicted brand-directed goals. Although unanticipated, this
finding is consistent with the qualitative results of Yarbus (1967), who found that a
memorization goal caused his participant to closely examine all scene objects rather than
singling out some for close inspection. It is also consistent with results obtained by Keller
(1987). Counter to his initial predictions, consumers recalled less ad claims under a branddirected (evaluation) goal than under an ad-directed (memorization) goal. He conjectured that
brand-directed processing might promote increased attention to the headline and body text, but
decreased attention to the pictorials in ads; and that if the pictorial contains ad claims or helps to
memorize them, attention to it aids recall. Yet “without more detailed processing data, however,
these explanations cannot be further tested” (Keller 1987, 326). The present study provides these
processing data, and shows that, counter to his speculation, an ad memorization goal enhanced
attention to the body text, brand and pictorial, whereas a brand evaluation goal only enhanced
attention to the body text without inhibiting attention to the pictorial.
The finding that the duration of attention to the pictorial was highest under ad
memorization goals is significant in view of the reported picture-superiority effect in memory
(Childers and Houston 1984; Costley and Brucks 1992). Explanations of the picture-superiority
effect have emphasized the better organization and retrieval of pictorial relative to textual
information, but our results show that ad memorization goals already prioritize pictorials during
the encoding stage. Thus, our findings emphasize the importance of unraveling the influence of
processing goals on storage and retrieval processes in memory and persuasion, which has been
25
called for but seldom attempted (Wyer and Srull 1989).
We did not observe goal effects on pupillary diameter, which presumably reflects
intensity of attention engagement. One explanation may be that our intensity measure--the
average pupillary diameter across fixations on a specific ad object--was insufficiently sensitive to
detect goal effects. This, however, seems unlikely in view of previous research with similar
measures, and we did find differences in pupillary diameter for the ad objects, the pupil being
wider for the pictorial (3.479 mm), and headline (3.487 mm), than for the brand (3.463 mm), and
body text (3.459 mm). A more likely explanation is that goal effects on intensity of attention
were absent because consumers managed their attention resources by “looking longer instead of
harder,” as revealed in systematically longer gaze durations. But, our findings may also raise
some doubts about the utility of pupillary diameter as a measure of attention intensity during
self-paced exposure to print ads and similar scenes. Note that prior research using pupillary
diameter as an intensity measure has typically used externally paced, effortful and non-visual
tasks, such as mental arithmetic or memorization of series of digits. Then, a dilated pupil
possibly indicates more and a constricted pupil less attention intensity, although the relationship
is only indirect and not causal (Beatty and Lucero-Wagoner 2000). The primary function of the
pupil, however, is to maintain optimal vision through regulation of the amount of light, visual
angle, and depth of focus, which is needed during exposure to ads and other complex scenes,
with heterogeneous textual and pictorial objects, each demanding different pupillary settings.
During ad processing, the vision function may prevail and override other potential determinants
of pupillary diameter. Our finding that the pupillary diameter for the pictorial, which demands
less attention resources but a more wide-angle view, was reliably larger than for the headline and
body text, and the systematic effects of surface sizes of the ad objects on pupillary diameter
26
(largest for the pictorial: 0.096 mm/cm2) are in line with this. Future research that systematically
varies the mental load of consumers, such as by time pressure or the introduction of secondary
tasks, might follow up on these speculations.
Interestingly, we know much about advertising processing and persuasion under brand
evaluation goals (reviewed by Meyers-Levy and Malaviya 1999), but much less about the
influence of other goals such as ad appreciation or of free viewing, which might be more
prevalent under natural exposure conditions. We find it of interest that the attention patterns for
free viewing and the ad appreciation goal were brief and remarkably similar. This not only
reveals the predicted, fast implementation of the ad appreciation goal, but also hints at the
possibility that consumers may have adopted an ad appreciation goal during free viewing. It may
hold true that ads themselves prime processing goals, for example, when objects in a particular
ad are prominent means to an end and activate it. Stimulus-based goal activation has recently
been examined in other domains (Shah and Kruglanski 2002) and appears relevant for
advertising as well. Based on our results we speculate, for example, that text-dominant ads
activate a brand-learning goal and pictorial-dominant ads an ad-appreciation goal. Future
research may test these speculations to expand the theory of goal control of advertising.
In sum, the present findings provide systematic evidence for the goal control of attention
to advertising, across four processing goals and a free-viewing situation, and large samples of
regular consumers and ads. They support Yarbus’ thesis that the informativeness of objects in
scenes is goal-contingent and that “Eye movements reflect the human thought processes; so the
observer’s thought may be followed to some extent from records of eye movements” (1967,
190), even during the brief moments that consumers choose to attend to ads.
27
APPENDIX 1
MODEL FORMALIZATION AND ESTIMATION
The model is a mixed-outcome multivariate hierarchical Bayes regression model, with a
Probit part for attention selection (0/1), and a Tobit part for attention engagement (> 0). There
are k = 1,...,K processing goal conditions, namely free viewing (k = 1), ad memorization (k = 2),
ad appreciation (k = 3), brand learning (k = 4) and brand evaluation (k = 5); l = 1,..., L attention
indicators, namely attention selection ( l = 1 ), gaze duration ( l = 2 ) and pupillary diameter
( l = 3 ) (and a control variable, eye-stimulus distance, l = 4 ); assessed for m = 1,…,M ad
objects, namely the brand (m = 1), pictorial (m = 2), headline (m = 3), body text (m = 4); for i =
1,…,I participants and j = 1,…,J ads. The (J × LM ) matrix Yi = ( yi , j ,l,m ) contains the attention
measures for participant i, the (J × M ) matrix X = (x j ,m ) contains the log(surface size) of each ad
object as deviations from their means; and finally the (I × K ) design matrix Z = (z i ,k ) indicates
the K goal condition for every participant, that is, zi ,k = 1 for participant i with goal k.
The duration and intensity of attention are only observed if an ad object is selected, and
else are unobserved. We let yi , j ,1,m denote the observed attention selection indicator. Then:
yi , j , l, m = 0 iff yi*, j ,1, m ≤ 0,
= 1, l = 1
=y
*
i , j ,l,m
,

*
 iff yi , j ,1, m > 0.
; l ≠1 
(1)
describes that selection of ad objects occurs for positive attention priorities, with probabilities:
P ( yi , j ,1,m = 1) = P ( yi*, j ,1,m > 0) . In (A1) yi*, j ,1,m reflects the covert attention priorities leading to
attention selection (Figure 1). Duration of covert attention engagement, yi*, j , 2,m , is reflected in the
duration of overt gaze, yi , j , 2,m , and its intensity, yi*, j ,3,m , in the pupil diameter, yi , j ,3,m , only if
28
yi , j ,1,m = 1 . Similarly the control variable, eye-stimulus distance, yi , j , 4,m , is only observed if an ad
object is selected for attention. We then specify:
Yi * = Ω i + X Σ i + Ε i .
(2)
Here Ωi is a (1 × LM ) and Σi a (M × LM ) matrix of person-specific parameters, the latter
containing the ‘own’-effects of the surface sizes of each object on the attention measures for it,
and consisting of L (M × M ) concatenated diagonal matrices, Σi ,l = diag (σ i ,l, m ) . We assume that
Ei ~ N (0, Ψ ) , where the full (LM × LM ) covariance matrix Ψ accommodates the dependencies
between the L attention indicators for the M ad objects.
We allow goals to influence attention hierarchically (figure 1):
Ωi = Z i Λ Ω + Fi
Σ*i = Z i Λ Σ + Gi
,
(3)
′
Here Σ*i = vec (diag (Σi , l ), l = 1,..., L ) , Λ Ω is a (K × LM ) and Λ Σ is a (K × LM ) matrix of
parameters, allowing for separate--but correlated--goal control of attention due to the ad object
itself ( Λ Ω ) and due to its surface size in the ad ( Λ Σ ). We assume Fi ~ N (0, Φ Ω ) , Gi ~ N (0, Φ Σ ) ,
and cov(Fi , Gi ) = Φ Ω ,Σ , so that the model accounts for individual differences through a normal
distribution of coefficients and allows all effects of ad objects on attention to be correlated.
We specify standard proper conjugate diffuse prior distributions for all parameters
( N (0,10 4 ) for regression parameters and IW ( P + 1, I P ) for a generic (P × P) covariance matrix).
All model parameters are estimated simultaneously by standard MCMC procedures, employing a
data augmentation step to draw the latent attention variables Yi* , and post-processing to obtain
identifiable parameters of the multivariate Probit (Rossi, Allenby, and McCulloch 2005). We use
25,000 draws, with a burn-in of 10,000, thinning every 3d draw and monitor convergence through
29
plots of the draws of the hyperparameters. The chains converge well before 10,000 draws.
The formulated hypotheses require specific contrasts in the goal control parameters in
Λ Ω and Λ Σ to be tested, that is, pair-wise differences between (a) the K goal conditions for each
of the L attention indicators, and (b) the K goal conditions for each of the M ad objects and L
K
attention indicators separately. We specify a matrix R containing all Q = L(M + 1)  contrasts,
2
WΩ = R ⋅ vec (Λ Ω ) and WΣ = R ⋅ vec (Λ Σ ) . Thus, R is a Q × KLM matrix. We compute WΩ and WΣ
at every sweep of the MCMC chain, which enables us to test the significance of all required goal
contrasts by computing their posterior means and standard deviations.
30
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TABLE 1
DATA DESCRIPTION
Advertisement objects
Attention indicator:
Selection: (proportion)
Engagement: Gaze duration (sec)
Engagement: Pupillary diameter (mm)
Controls:
Surface size of ad object (dm2)
Distance to ad object (dm)
Brand
Pictorial
Headline
Body text
.86 (.35)
.97 (.16)
.82 (.39)
.67 (.47)
.84 (.84)
1.52 (1.29)
.91 (.95)
.83 (1.55)
2.96 (1.34)
3.40 (.86)
2.83 (1.46)
2.30 (1.70)
.74 (.55)
3.28 (.80)
.66 (.30)
.89 (.44)
4.70 (1.94)
5.36 (.97)
4.61 (2.22)
3.54 (2.52)
Note-Means with SDs between parentheses. Surface size is across ads (n = 17), and visual attention
measures across ads (n = 17) and participants (n = 220).
36
TABLE 2
INFLUENCE OF PROCESSING GOALS ON ATTENTION ACROSS AD OBJECTS
Processing goals
Attention
Advertisement
Indicator
Source
(Baseline)
Free
viewing
Selection
Object
1.412
1.616
Surface
size
.149
Object
Engagement:
Gaze duration
Engagement:
Pupillary
diameter
Memorization
Brand
Appreciation
Learning
Evaluation
1.217
1.302
1.474
.095
.143
.145
.131
.888 a
1.238 c
.847 a
1.047 b
1.056 b
Surface
size
.094
.136
.112
.099
.096
Object
3.551
3.431
3.498
3.453
3.427
Surface
size
.032
.049
.046
.049
.039
Note-Coefficients with boldface font have a 95% posterior credible interval that does not cover zero.
Coefficients with different superscript row-wise have posterior credible intervals that overlap less than 5%.
37
TABLE 3
INFLUENCE OF PROCESSING GOALS ON ATTENTION DURATION
Processing goals
Attention
indicator
Ad object
Advertisement
Brand
Memorization Appreciation
Learning
Evaluation
.923 a
1.342 b
1.000 a
1.030 a
1.047 a
Pictorial
1.133 b
1.459 c
1.029 b
.871 a
1.074 b
Headline
1.247
1.395
1.085
Brand
Engagement:
Gaze duration
(Baseline)
Free
viewing
Body text
.251 a
.759 b
.274 a
1.319
.967 c
1.379
.723 b
Note-Coefficients with boldface font have a 95% posterior credible interval that does not cover zero.
Coefficients with different superscript row-wise have posterior credible intervals that overlap less than 5%.
38
FIGURE 1
GOAL CONTROL OF ATTENTION TO ADVERTISING
Processing goals:
ad & brand,
learning &
evaluation
Long term memory
Updating
Advertising
stimulus:
brand, pictorial,
headline, body text
Salience
of
objects
Informativeness
of
objects
Covert attention
priorities
Working Memory
Updating
Overt attention
patterns:
selection, duration,
intensity
39
FIGURE 2
GOAL CONTROL OF ATTENTION:
GAINS AND LOSSES RELATIVE TO FREE VIEWING