=Paper= {{Paper |id=Vol-1241/paper08 |storemode=property |title=High-Level Gaze Metrics From Map Viewing - Charting Ambient/Focal Visual Attention |pdfUrl=https://ceur-ws.org/Vol-1241/paper08.pdf |volume=Vol-1241 |dblpUrl=https://dblp.org/rec/conf/giscience/KrejtzDC14 }} ==High-Level Gaze Metrics From Map Viewing - Charting Ambient/Focal Visual Attention== https://ceur-ws.org/Vol-1241/paper08.pdf

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High-Level Gaze Metrics From Map Viewing
Charting Ambient/Focal Visual Attention

Krzysztof Krejtz1 , Andrew T. Duchowski2 , and Arzu Çöltekin3
1
National Information Processing Institute and
University of Social Sciences and Humanities, Warsaw, Poland
2
School of Computing, Clemson University, Clemson, SC, USA
3
Department of Geography, University of Zurich, Zurich, Switzerland

Abstract. Distinguishing ambient and focal attention, we demonstrate the use of
the K coefficient, which can serve as a cue for recommender systems in deciding
when to offer information to the user, e.g., when focally attending during search.

Keywords: maps, eye tracking, visual attention

1 Introduction & Background

Gaze-based recommender systems are designed to respond with information contingent
on the viewer’s gaze, e.g., in geographic contexts, when directed to a particular location
in physical or virtual space (such as on a map). These geographic gaze-based recom-
mender systems have been referred to as location-aware (mobile) eye tracking systems
[7]. For the system to provide an appropriate response, analysis of the viewer’s gaze is
required to infer the viewer’s desire for information. Generally, this is accomplished via
computation of an interest metric [11,10,4]. Recent approaches characterize interest or
boredom via Support Vector Machines [6] or Area Of Interest (AOI) revisitation [5].
In this paper we use the K coefficient to distinguish ambient and focal attention.
Ambient attention is characterized by relatively short fixations followed by high am-
plitude saccades. Conversely, focal attention is described by long fixations followed
by saccades of low amplitude [12]. The K coefficient captures the temporal relation be-
tween standardized fixation duration and subsequent saccade amplitude. K > 0 indicates
focal viewing while K < 0 suggests ambient viewing [9].
Attention becoming more focal over time, or oscillating between focal and ambient
modes, could indicate changing cognitive load corresponding to stimulus complexity.
The K coefficient can thus potentially act as a contextual cue which could be exploited
by recommender systems, e.g., do not interrupt the user when in ambient search mode,
or oscillating between ambient and focal search.
We demonstrate the utility of K with an experiment comparing visual search behav-
ior over different geographic representations of two cities: Amsterdam and Barcelona.

ET4S 2014, September 23, 2014, Vienna, Austria
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(a) Apparatus and setting. (b) Satellite rendering. (c) Map rendering.

Fig. 1. Apparatus and stimuli (Barcelona) using Google’s hybrid and roadmap rendering.

2 Methodology

The experiment consisted of localizing map objects (features), arguably the most fun-
damental map-reading task [1]. Localization, or visual search, i.e., finding an object of
interest among others, is a fundamental map use task, because regardless of the map
type or the final goal of the map reader, an object or area of interest must be found be-
fore it can be studied further. Geographic task taxonomies widely acknowledge this task
among the most basic and common [8,2]. The localization tasks were carried out when
viewing a city representation in either (cartographic) map or satellite view (Google’s
roadmap or hybrid rendering; see Fig. 1 and text below for technical details). Unlike
previous work on route learning [3], we hypothesized there would be an effect of map
type on visual search, with localization (search) taking longer in satellite view than in
map view.
Participants. Sixty-three (63) university students took part in the study, with 7 ex-
cluded due to technical and procedural problems (e.g., poor calibration). The final sam-
ple included 56 participants (19 M, 37 F, ages M = 24.62, SD = 4.01).
Apparatus. All stimuli were presented on a computer monitor (1680×1050 reso-
lution; 2200 LCD, 60 Hz refresh rate) connected to a standard PC computer. Eye move-
ments were recorded at 250 Hz with an SMI RED 250 eye tracking system.
Stimulus. Map images were created using Google Mapstm JavaScript API v3. Maps
were rendered (see Fig. 1) by disabling visual user interface controls for navigation,
scale, rotate, pan, and zoom, and limiting the number of Points Of Interest (POIs)
to two. Specifically, the Barcelona maps (using Google’s latitude/longitude coordi-
nates: 41.375384, 2.141004) displayed only the “park” and “sports complex” POIs
at zoom level 17 with the transit layer turned on. The Amsterdam maps (lat./lon.:
52.3614777, 4.8837351) were also displayed at zoom level 17 with two POIs (“at-
traction” and “business”) and transit layer turned on. All maps were rendered to
1280×1024 resolution, then screen-captured and cropped to the same dimensions.
Procedure. Participants were randomly assigned to either experimental condition,
map (N = 29) or satellite view (N = 27). Prior to viewing of the map, the eye tracking
system was calibrated to each individual. Following calibration, participants carried out
the localization task (after having located a start point).

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For the city of Barcelona (see Fig. 1), participants were asked to find the intersection
of Carrer de Vilardell and Carrer d’Hostafrancs de Sió, indicating its localization by
dwelling on it for 3 seconds. For Amsterdam, they were told to find the Rijksmuseum.

3 Results & Discussion

Participants’ Familiarity with Presented Areas and Google Maps. Participants were
not very familiar with the areas of Barcelona or Amsterdam that were presented to them
during the experiment. We drew this conclusion after analyzing questionnaire answers.
The percentage of participants who had visited either city at least once in their lives was
39% for Amsterdam and 25% for Barcelona. Of these, they had visited the city at most
one time (32.1% for Amsterdam and 19.6% for Barcelona).
Google maps was fairly popular among participants, with only 7.1% claiming they
had never used the service. Table 1 gives the detailed distribution of answers to the
question “How often do you use Google Maps?”
Performance (time to completion) and process (visual attention) measures we report
from the experiment therefore speak to more or less typical use of Google Maps. The
importance of our findings lies in their potential indication of online map use when
encountering a new location (e.g., as one would do prior to travel to the destination) by
users already familiar with the service.
Localization Task Performance. All subjects successfully completed the tasks. To
gauge localization performance, we first considered basic eye movement metrics, using
a linear mixed model (LMM) analysis with task duration as the dependent variable and
experimental condition (map vs. satellite) as the between-subjects fixed factor and city
(Amsterdam vs. Barcelona) as the within-subject fixed factor.
As expected, analysis revealed a significant main effect of experimental condition,
F(2, 54) = 26.26, p < 0.01, indicating that task completion time was shorter for the map
view (M = 37.33 s, SE = 41.63) than for the satellite view (M = 47.36 s, SE = 55.27).
Unexpectedly, however, the city appeared to be a moderating factor resulting in a
significant interaction term, F(1, 54) = 9.15, p < 0.01. Investigation of contrasts related
to this interaction effect revealed that the difference between map and satellite views
was significant only for Barcelona. For Amsterdam, no significant difference between
completion times for the two view types was found. Analysis also revealed a main effect
of city, F(1, 54) = 31.68, p < 0.01, meaning that, overall, time to localization on either of

Answer Percent
Everyday 7.1%
2-3 times a week 32.1%
Once a week 12.5%
2-3 times a month 19.6%
Once a month 21.4%
Never 7.1%

Table 1. Answers to the questionnaire question: “How often do you use Google Maps?” (N = 56)

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0.2 0.2
Ambient−Focal K Coefficient

Ambient−Focal K Coefficient
0.0 0.0

−0.2 Exp. condition −0.2 Exp. condition
Roadmap Roadmap
Satellite Satellite

1 2 3 4 5 1 2 3 4 5
Time sequence Time sequence

(a) Amsterdam. (b) Barcelona.

Fig. 2. Dynamics of K coefficient over time between viewing conditions (whiskers ±1SE).

the Barcelona maps (map or satellite views) took significantly longer (M = 61.7 s, SE =
1.9) than on the Amsterdam maps (M = 25.44 s, SE = 1.5).
The studied sections of the two cities show strong differences in spatial organization,
that is, the way the buildings and other features are organized in relation to each other.
In the section from the Barcelona map there is more regularity (more rows of buildings
similar to each other), making the visual search potentially harder. This is possibly one
of the reasons why we found a strong difference between the cities and one that is also
supported by literature [13].
Temporal Dynamics of Visual Attention. To delve deeper into the visual search
process of viewers over each of the city maps, we performed a similar LMM analysis
but with K as the dependent variable, with the same independent variables of map view
and city as before. Because we believe plotting K over time depicts the dynamic dilation
and constriction of the viewer’s field of view during visual search, we used time as a
fixed within-subject factor, discretized into 5 equal periods (see Fig. 2).
Considering the city as a moderating factor, analysis of K showed significant inter-
action among city, map type, and time, F(4, 9910) = 2.44, p < 0.05. Temporal ambient/-
focal attention patterns are influenced by map type (satellite vs. cartographic) and city.
When working with the Barcelona satellite view, attention generally becomes in-
creasingly more focal over time, especially over the satellite view, as expected (see
Fig. 2(b)). Over the cartographic map view, the pattern is more sinusoidal, eventually
becoming increasingly focal over the last two time segments. When viewing the Ams-
terdam maps, however, the search pattern is less predictable (see Fig. 2(a)).

4 Conclusion

Analysis of K strongly suggests different cognitive strategies involved in visual search
depending on the stimulus and/or task difficulty. Faster search over less complex stimu-

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lus may afford ambient search sooner, longer, or with greater ambient/focal oscillation,
possibly indicating boredom. More complex stimulus may require increasingly focal
attention later on into the search, possibly indicating greater interest.

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