Top-Down and Bottom-Up Processes in Web Search Navigation

Top-Down and Bottom-Up Processes in Web Search Navigation
Shu-Chieh Wu (shu-chieh.wu@nasa.gov)
NASA Ames Research Center and San Jose State University, MS 262-4
Moffett Field, CA 94035 USA
Abstract
In current theories of web navigation, link evaluation has
been treated primarily as a bottom-up process involving
assessing the semantic distance between a search target and a
given link in the information architecture. We investigated
whether there exists top-down influence from familiarity with
the search target and/or the information architecture. We
compared search performance on targets that varied in the
level of category ambiguity and the presence of category
names. We found that categorically unambiguous search
targets resulted in fewer categories being evaluated, fewer
fixations and shorter fixation durations, and overall fast initial
classification regardless of the presence of category names,
suggesting proactive use of top-down knowledge in guiding
search behavior. Our results have implications on designing
information architectures that support efficient top-down
strategies in searches for menu items and web links.
Keywords: Web navigation; web search; eye movements;
eye tracking; visual scanning; menu organization.
Introduction
The prevalence and improved accuracy of web search
technologies have transformed the process of how users
seek information. While keyword search has arguably
become the most dominant method for locating information
on the internet, search through navigation remains an
important element of web user experience. Navigation
offers the ability to locate unfamiliar targets in the absence
of proper keywords and to browse all available instances on
a particular theme. Outside of the web environment,
navigation through multiple levels of menus also remains
the primary method for locating information on personal
digital devices such as digital cameras, personal digital
assistants (PDAs) and cellular phones, where implementing
a search function is impractical given the limitation in
display areas and input methods. Different from keyword
search where results are collated and returned, search
through navigation requires users to click through a series of
links in order to navigate themselves toward search goals.
How users at any given moment search among available
links and choose one that they believe will lead them to their
goals is a question of both theoretical interest and practical
implications on the design of efficient information
architecture.
In this paper, we examine the process underlying link
evaluation, a critical element of navigation. The result of
link evaluation dictates how a user navigates, whether it is
to proceed with a link or to backtrack from a dead-end page.
Craig S. Miller (cmiller@cs.depaul.edu)
DePaul University, 243 S. Wabash Avenue
Chicago, IL 60657 USA
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Theories of web navigation have focused on predicting
among a group of links which one will more likely be
selected in actual user behavior but with little attention paid
to the evaluative process, per se, of a single link. This paper
aims to address possible top-down and bottom-up factors
that influence link evaluation.
Link Evaluation
Despite its central role in navigation, the process underlying
link evaluation is not always explicitly defined in current
theories of web navigation. Some models like MESA
(Method for Evaluating Site Architectures) have link
evaluation outcomes generated outside the model by
separate procedures and fed into the model (Miller &
Remington, 2004). Others like CoLiDeS (Comprehensionbased
Linked model of Deliberate Search) (Kitajima,
Blackmon, & Polson, 2000) and SNIF-ACT (Scent-based
Navigation and Information Foraging in the ACT
architecture) (Fu & Pirolli, 2007) model link evaluation as
measuring the semantic distance between an encountered
link and a search target. There is no question that link
evaluation involves some form of semantic comparison. The
question is where the comparison takes place; that is,
whether the comparison really is between the particular link
being evaluated and the search target, an assumption shared
by both models.
One reason to question such an assumption of link
evaluation processes is that users often possess a certain
amount of knowledge, not only of their search targets (i.e.,
queries) but also of the likely options from the information
architecture. For example, a shopper who wishes to
purchase an MP3 player from an online store would not
necessarily expect to find the word MP3 on the front page of
the store. Rather, a savvy shopper may expect to find links
like electronics or portable audios. In other words, to
facilitate search, users are likely to take into account what
opportunities are available based on their prior experience
and apply that knowledge to rephrase their search targets in
ways conforming to what they believe to be conventional
contents of the information architecture.
What is being suggested here is that link evaluation likely
involves not only a bottom-up process in which the meaning
of an encountered link is compared to that of the search
target but also a top-down process that precedes evaluation
in which a user rephrases the search target in languages
closer to those available from the information architecture.

They differ in the level at which a user injects his/her prior
knowledge of both the search goal (i.e., query) and the
available links into the search process, and consequently the
nature of the evaluative process. The difference between our
stipulation of the top-down processes (rephrasing a search
target to match available links) and assessing semantic
distance between a given link and words related to the
search target (as in information foraging) is that the former
could lead to cases where no evaluative process takes place
because the user simply knows what he/she is looking for
and where it is. In top-down link evaluation, the nature of
the evaluative process is simplified from comparison to
recognition. Note that Hornof (2004) and Fleetwood and
Byrne (2006) study processes where the user is searching
for a specific string. This is akin to what happens in a topdown
strategy.
Figure 1 proposes an idealized process that uses two
distinct strategies for searching a menu item depending on
the ability to first recall a category phrase that might appear
in the desired selection. After acquiring the search target,
the first condition box indicates that a user tries to retrieve a
relevant phrase that likely appears in the menu. If the user is
able to retrieve a relevant phrase, it employs a top-down
strategy. That is, it prepares a search for the physical
properties of that phrase. If a relevant phrase cannot be
retrieved, it employs a bottom-up strategy, which requires
an item-by-item calculation of category membership. In this
way, the bottom-up strategy incurs a greater time cost when
searching through menu items, not only because it likely
requires more iterations of link visitation and evaluation
before a final selection can be made but also because the
evaluative process itself takes longer than that in the topdown
strategy. Note that for the top-down strategy, this
schematic indicates that a user still confirms category
membership when finding a physical match, but it is
possible that a confident user may forgo this step if the
initially retrieved phrase is almost certainly part of the
desired category.
Figure 1: A schematic diagram of how search proceeds under top-down and bottom-up processes
1849

The possibility of top-down strategies has at least two
important implications for how users perform at web and
menu navigation. First, a top-down strategy would
presumably be more efficient when the user can look for the
physical features of a specific target by way of a faster scan
of the menu items. Second, a top-down strategy may cause a
user to overlook highly relevant items if their labels do not
physically match the character sequence that is anticipated
in the target label. Given its implications for human
performance, understanding when top-down strategies occur
could be helpful for designing information architectures and
diagnosing navigation problems when they occur.
Present Research
In the present research, we investigate whether there is
evidence for top-down processing in individual link
evaluation. We hypothesize that the distinction between topdown
and bottom-up processes is most likely revealed in the
comparison between search for categorically ambiguous and
categorically unambiguous goals. When the search goal
clearly indicates its own category, users are more likely to
apply their prior knowledge of the information architecture
to rephrase the search target and transform the process of
link evaluation to recognition. Conversely, when the search
goal is categorically ambiguous, users are more likely to
depend on bottom-up processes and compare the search
target against each encountered link. Certainly categorically
ambiguous targets will lead to a longer search process
wherein more links are evaluated. The critical prediction
here however is on individual link evaluation, that it should
take less time with categorically unambiguous than
categorically ambiguous goals. Alternatively, if search is
carried out exclusively through bottom-up processes,
individual link evaluation time should be equivalent in
searches for both categorically ambiguous and unambiguous
targets because the nature of the evaluative process remains
the same.
Naturally, categorically unambiguous goals are more
likely to contain category names as part of their description.
As a result, faster link evaluation could be equally
attributable to close semantic distance. To control for this
potential confound, we independently manipulated category
ambiguity and the presence of category names. If link
evaluation is subject to top-down influence, we would
expect to see fast link evaluation in category-unambiguous
search goals despite the absence of category names.
Experiment
Participants
Ten students recruited from local colleges participated.
They had no experience with the expert database and were
naïve to the purpose of the study.
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Apparatus
The study was carried out on a Pentium 4 PC running
Firefox. Eye movements were monitored using a headmounted
high-speed eye tracker (Applied Sciences
Laboratory, Model 501) with eye-head integration function,
sampling at 120Hz. Gaze positions were then synchronized
with recorded scenes using GazeTracker software (Eye
Response Technology), which records video at 640x480
pixel resolution and samples at 40 frames per second.
Domain for the Information Architecture
The website used in the study was generated based on an
“expert database” maintained by the Media Relation
Department of DePaul University, which contains
descriptions of 970 university faculty members and their
respective areas of expertise as a resource for journalists in
need for a subject-matter expert. The database was
organized in levels of categories and sub-categories and
implemented in a web-based application. For details of the
categories and the process by which they were derived, see
Miller et al. (2007). In the present research, the expert
database was reduced and restructured to contain one level
of top categories, one level of various numbers of subcategories,
and at the bottom one level of content items (i.e.,
expert descriptions). The resulting database had the
following 9 top-level categories: Arts and Literature,
Business and Economics, Education, Law and Legal, Health
and Medicine, Politics and Public Policy, Religion, Society
and Culture, and Science and Technology.
Task and Design
Participants were given descriptions of experts and asked
to locate them in the web application. The descriptions
chosen as search targets varied according to two factors:
category ambiguity and the presence of category names. We
hand-selected the descriptions that represented clear cases
for each of the four factorial conditions. The level of
category ambiguity was determined in a previous study
(Miller et al., 2007) based on how users assigned the
descriptions to the 9 top-level categories. We used the
following calculation to measure the distribution of category
choices for a description:
ambiguity = 1 −
c
∑
i=
1
Here, c equals the number of categories, Si is the number of
choices for a particular category and n is the total number of
choices. For a fully unambiguous description, where the
description was consistently assigned to only one category,
the ambiguity measure is 0. For the present study, we used
the ambiguity measure as a guide for selecting categorically
ambiguous and unambiguous descriptions while also
controlling for their length. In the end, all descriptions
classified as categorically unambiguous had an ambiguity
⎛
⎜
⎝
si
n
⎞
⎟
⎠
2

measure less than .25 and all descriptions classified as
categorically ambiguous had a measure greater than .45.
The presence of category names was determined by
calculating the amount of co-occurrence of words between a
particular expert description and the category to which most
users assigned the description. Descriptions with no cooccurrence
were assigned to one condition. For the opposite
condition, a description was considered having category
names if it contained words from the category to which
most users assigned the description.
To illustrate, here are examples of the four types of
descriptions from Science and Technology category:
“Use of technology including computers,
telecommunications and multimedia”
(categorically unambiguous with category names)
“Fiber-optic communications, chaos and optical
systems, lasers”
(categorically unambiguous without category names)
“Ethics of new technology, employee privacy and
technology”
(categorically ambiguous with category names)
“The harmful effects environmental contamination has
on living organisms and systems”
(categorically ambiguous without category names)
Note that in the case of descriptions that were categorically
ambiguous and with category names, the particular category
name contained in the descriptions always corresponded to
the category to which the descriptions belong. In other
words, there were no misleading category names in the
descriptions chosen.
Four descriptions were selected from each category
(except Religion) to cover the four factorial conditions,
resulting in a total of 32 search task trials. Their length was
between 46 to 89 characters (~66 characters on average).
The tasks were selected so that average length was
comparable across all four conditions of descriptions,
average ambiguity was comparable within the two
ambiguity classes (with and without category names), and
average word overlap was comparable within the two
overlap classes (categorically ambiguous and categorically
unambiguous).
Procedure
In the beginning of each trial, participants were given the
search target alone on a separate page. They were instructed
to read the description through before clicking on a
“continue” link which, when pressed, displayed the top level
categories and started the timer. Each task scenario was
terminated upon either finding the target expert or after two
minutes had elapsed. Then the next trial was presented.
The web application recorded and time-stamped every
selection performed by the participant. The categories and
content items were consistently arranged in the same order
on the page throughout the tasks. The order of the tasks was
1851
randomized for each participant. Eye movements were
recorded along with displayed web contents.
Eye Movement Data Processing
The analysis of eye movements focused on identifying
fixations on category links. On the top-level menu we
defined 10 non-overlapping areas of interest (AOIs) which
included one AOI covering each of the 9 category links and
one covering the top area where the current task description
was displayed. Fixations were defined as 4 or more
consecutive sampled gaze points falling within an area of 60
pixels and with a total duration of at least 100 ms. We then
identified fixations within each of the 10 AOIs. When
calculating the evaluation time of a given category,
successive fixations within the same AOI for that category
were combined, along with intervening saccade intervals.
Results
The analyses focused on the processes leading up to the
first top-level category selection on each task, where we
hypothesized the effect of top-down processing was most
likely to appear. Of particular interests was the time spent
on evaluating individual categories. We use fixation
duration as the indicator of evaluation time and hypothesize
that the durations of fixations on individual category links
would be shorter in the search for categorically
unambiguous targets than for categorically ambiguous
targets. In addition, because the categories were fixed and
displayed at constant locations throughout the session, it is
possible that a participant with a category in mind may look
directly toward the desired category without evaluating any
other link. In that case, top-down processing may be
evidenced as selecting the first fixated link. Analysis to
follow also examined this possibility.
Validation of Category Ambiguity Manipulation
As a first step, we sought validation for the manipulation
of category ambiguity by examining the percentages of
correct category selections. For the unambiguous targets,
the category leading to the target was initially selected on
88.8% of the trials for those with category names and 86.2%
for those without. For the ambiguous targets, the category
leading to the target was initially selected on 70.0% of the
trials for those with category names and 55.0% for those
without.
Additional validation for the manipulation of category
ambiguity could be seen in the total numbers of different
categories examined. For the unambiguous targets,
participants examined on average 3.8 different categories
for those with category names and 3.9 categories for those
without. For the ambiguous targets, participants examined
on average 4.5 different categories for those with category
names and 5.8 for those without. Results from a repeated
Analysis of Variance (ANOVA) with factors of category
ambiguity and presence of category names showed
significantly more categories were fixated for categorically
ambiguous targets, F(1, 9) = 22.59, p < .001.

In summary, for categorically unambiguous targets
participants evaluated fewer categories and their final
selections converged greatly on the category that would lead
to the target.
Category Link Evaluative Processes
Next, we examined the evaluative processes in terms of
total time taken, numbers of fixations generated prior to
category selection, and the duration of fixations. For the
time taken to make the first category selection, we extracted
from video recordings the elapsed time from when the toplevel
categories were first visible to when they were last
seen before the page transitioned to show the sub-categories
of a selected category. Since trials were blocked by
participant, we performed mixed model analyses where the
participants were modeled as a random effect (Singer,
1998). We then tested the fixed effects of trial number,
category ambiguity, the presence of category names, and the
interaction between category ambiguity and the presence of
category names. The mixed model revealed a significant
effect for the trial number, F(1, 306) = 19.94, p < .0001. The
effect of category ambiguity was significant, F(1, 306) =
11.51, p < .0001. While the effect of the presence of
category names was not significant, F(1, 306) = 2.25, p =
.1350, the interaction of category ambiguity and the
presence of trigger was significant, F(1, 306) = 4.46, p =
.0354. The means for the four conditions of targets are
presented in Table 1 (the mixed model provided a standard
error of .57 for all four means). The interaction of ambiguity
and presence of category names can be seen in the
difference in selection times. For the ambiguous targets, the
presence of category names resulted in selection times that
were more than 1 second faster on average. However, the
presence of category names did not yield faster selection
times for the unambiguous targets.
Category
Ambiguity
Presence
of
category
names
Mean
Selection
Time
Standard
Error
Ambiguous None 5.8 .57
Ambiguous Present 4.5 .57
Unambiguous None 3.1 .57
Unambiguous Present 3.3 .57
Table 1. Model estimated mean selection time (in seconds)
and standard error of first link selection times.
Although time to first category selection provides a
general measure of search efficiency, faster selection times
observed for categorically unambiguous targets than
categorically ambiguous targets do not necessarily suggest
different processes, especially in light of the finding that
fewer categories were examined in the former case. An
examination of the fixation data showed that efficient search
of categorically unambiguous targets was marked by both
fewer fixations and shorter fixation durations, providing
some indication of differences in processing. Table 2 shows
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in each condition average numbers of fixations made prior
to the first category selection and their average durations. 1
On average, fixation durations (i.e., link evaluation times)
were approximately 20 ms shorter for categorically
unambiguous targets. Although this decrease in link
evaluation time was consistent with the use of a top-down
strategy, results from a repeated ANOVA on fixation
durations with factors of category ambiguity and presence
of triggers failed to find it to be statistically reliable, F(1, 9)
= 2.91, p > 0.12.
Category
Ambiguity
Presence
of
category
names
Number
of fixations
Duration
of fixations
(ms)
Ambiguous None 17.1 283
Ambiguous Present 11.7 279
Unambiguous None 8.7 267
Unambiguous Present 8.8 256
Table 2. Average number of fixations made during the
first visit to the top-level category menu and their average
durations
The calculation of link evaluation time requires there be
at least one qualified fixation falling with the AOI of a
category link during the first visit to the top-level category
menu. As mentioned previously, top-down strategy may
lead participants to saccade directly to a desired link without
evaluating any other link in the process. In such cases, there
would be no qualified fixations for analysis. To capture this
aspect of top-down processing, we examined the number of
trials where the target category was visited on the very first
fixation, out of the eight total trials in each condition. For
categorically unambiguous targets, participants visited the
target category on their first fixation on 2.8 out of 8 trials
for those with triggers and 2.8 out of 8 for those without
triggers. For categorically ambiguous targets, participants
visited the target category on the first fixation on 1.8 out of
8 trials for those with triggers and 1.9 out of 8 for those
without triggers. Clearly participants were able to quickly
narrow their search down to one category immediately at
least on some trials regardless whether the targets were
considered categorically ambiguous or not. However, they
were more likely to do so for categorically unambiguous
targets.
General Discussion
The present findings suggest that link evaluation possibly
involves more than assessing the semantic distance between
the search target and each of the encountered links. When
the search target clearly indicated its own category,
participants evaluated fewer categories and spent less time
on each. They were also more likely to fixate directly the
1 Calculation of average fixation durations did not include the
last fixation made during the first visit to the top-level category
menu as it is inflated by the time to make manual selections.

target category, bypassing link evaluation altogether. These
findings are consistent with the possibility that participants
generated their own category names through top-down
processing prior to carrying out the search process.
Given the equivalent and overall fast selections in the two
category-unambiguous conditions, it is reasonable to believe
that participants generated their own category names even in
the absence of them based on their knowledge of the
descriptions and the information architecture. In contrast,
use of a bottom-up strategy seems implausible for the faster
selection times (i.e. < 3 seconds). After accounting for page
refresh, mouse movement and link selection, little time
remains for an item-by-item evaluation of the category
labels, which must take at least 230 ms per link to account
for the time needed for each eye movement (Card, Moran,
& Newell, 1983).
The present findings also suggest that the presence of
category names facilitates category selection at least when
the search target does not have a clear category selection. In
these cases, the participants may still use the more efficient
top-down strategy, relying on the category name in the
description to perform the visual search for its physical
properties. Category ambiguity still incurs a greater time
cost, possibly to confirm category membership or consider
alternate categories before making the selection.
Not surprisingly, the trial number was a significant factor
for the amount of time used to select a link. In particular, the
selection time decreased with the number of trials. This
result is consistent with increased use of top-down strategies
as users become more familiar with the categories and their
location on the page.
The presence of efficient top-down strategies has
implications for the effective design of web pages and menu
systems. In applied web design usability research, the
superordinate category names are sometimes referred to as
“trigger words” to signify in a post-hoc manner their being a
user’s internally formulated search goal and triggering a
selection response from the user (Spool, Perfetti, & Brittan,
2004). To the extent that users form key “trigger words” and
then scan for their physical properties, care must be taken to
include these words in the selection labels. Otherwise, users
may miss the targeted selection even if its label is highly
relevant to the user’s information goal. This consideration is
consistent with Spool’s advice of identifying common
trigger words for users and including them in link labels.
Certainly any postulation of dichotomy is prone to
oversimplification. The question is whether making such a
distinction is useful in the understanding of different
mixture of processes that may go on in link-based search.
Although computational modeling in web search has seen
great improvements, empirical investigation of the issue in
our opinion is still lacking. The present research represents a
step in finding factors that elicit top-down processing and
measures that indicate when top-down processing occurs.
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Acknowledgments
This work was funded by grant NNA06CA99A from
NASA’s Human Research Program. We are indebted to Jim
Lofton who developed the "UI Nav Test" web application,
which we used to administer our navigation studies. We also
thank Paul Roth, Mike Niebling, Ben Burton, Nils Hanson,
Devin Carter and Anna Eskra for testing and commenting
on preliminary versions of the web application, Jaymie
Massoletti for assistance with data analysis, Usha
Viswanathan for participants recruit, and Joel Lachter for
equipment setup.
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