Nicolas Maisonneuve

Application of a simple visual attention model to the communication
overload problem


Received: 07-06-2007 / Accepted: 22-06-2007

Abstract
The network organization model described several years
ago by Lenge [2] has, to a large extent, become a reality.
Furthermore the low cost of communication and the
convergence/interoperability of devices and networks
increasing connections among people and providing a
sense of real-time awareness.. Not surprisingly, the time
dedicated to our communication has increased
considerably. The main goal of this paper is to present a
simple mechanism based on an attention model able to
select from a list of received items (emails,
blog/community’s feeds, IM) the most salient ones for a
limited attention user. This attention model is inspired by
visual attention model of J.M Wolfe [21] reflecting a
biological behavior where visual signals are changed in
communication stimuli for our concern. Like this visual
attention model our model has two forms of preattentive
guidance. Bottom-up guidance directs attention toward
signal whose features differ from their neighbors
(reputation, solicitation scarcity, audience focus, etc..).
Top-down guidance directs attention toward signals that
have target features (current user’s interests represented in
an intention profile).

Keywords: attention economy, attention aware system,
communication overload, visual attention model, social
network, computer mediated communication

1. Introduction

1.1. Context1

Internet increases people's social capital and increase
connections among people. Not surprisingly, the time

N. Maisonneuve
The Centre for Advanced Learning Technologies, INSEAD ,
Fontainebleau
Tel.: +33 (0) 1 6072 9168
E-mail : nicolas.maisonneuve@insead.edu
dedicated to our communication has increased
considerably. This is also true from the enterprise
perspective. The network organization model described
several years ago by Lenge [2] has, to a large extent,
become a reality. Increasingly, the knowledge workers of
today work in virtual and distributed teams. Furthermore
the low cost of communication and the
convergence/interoperability of devices and networks.
Offline devices (e.g. cell phones) are now connected to
online ones (e.g. IM, email, feed) providing a sense of
real-time awareness. It is an always-on, anywhere,
anytime, any place.
Due to this change a new behavior coined “Continual
partial attention” (CPA) described by L.Stone[17]
involves an artificial sense of constant crisis: people
attempt to stay partially but continuously aware about the
activity within their networks. “Continuous partial
attention involves constantly scanning for opportunities
and staying on top of contacts, events, and activities in an
effort to miss nothing.”

1.2. Problem

In fact being aware of the activity of virtual teams, virtual
communities and personal or professional social networks
is a difficult task and requires making choices. According
to the study [16] the total cost to the U.S. economy of
attention-management problems caused by e-mail and
other online tools amounts to about $588 billion a year.
In fact an information-rich environment is characterized
by a competition for the user’s attention (attention
economy [2]). A person has still a limited capacity to
manage his attention (and his social relationship [3]). A
typical situation for a user in a rich social environment is
to decide from a list of received items (emails, IM
messages, received articles from communities/newsgroup
by feed) which ones are the most salient for his limited
attention capacity (e.g. time to read), keeping him aware
to unexpected but important events and avoiding noisy
information according his current interests.

1.3. Proposed approach

In this early step of our research we introduce an attention
aware system based upon the application of a visual
attention model in a communication context. More
specifically we adapt the visual attention model from J.M
Wolfe called “guided search 2.0” [21] in replacing visual
stimuli by communication stimuli.
We use this analogy because of the similarity of the
problem. Humans have developed a versatile ability to
extract, filter and compress information from visual
stimuli. Basically the retina has approximately 130
million cells to capture the light but there is only 1 million
fibers leaving the eye. This high-level faculty to filter and
compress (roughly 1:100) the visual information overload
is notably achieved due to a complex set of attentional
processes enabling, and guiding the selection of incoming
perceptual information.

The remainder of this paper is organized as follows: in
Section 2 we present some attention models and
especially the “guided search 2.0” model. In Section 3 we
introduce our approach and its adaptation for the attention
management in a communication context. We conclude
with Section 4, which presents a few scenarios related to
our problem.

2. Related works

2.1. Attention models

Usually all attention models (not only the visual ones) try
to do the same thing: given a set of constraints (e.g.
limited resources) and some information regarding the
environment and the task, the models attempt to
determine/predict which option will be chosen in order to
achieve a given goal.
A lot of research has been about the design of attention
aware system addressing the issue of interruption (see
Roda[13] for a review). Recently, Huberman et al [8]
attempted to answer the same kind of problems we have
(selection of salient items in the context of a rich
information environment) in a general way. In their
approach they formulated this problem as a restless bandit
problem (MAB), a dynamic allocation problem. But this
approach does not reflect the psychological aspect of the
attention, or the social aspect.
Furthermore in human/robotics vision and psychology
fields, a lot of research has been done to develop visual
attention models (predicting the allocation of human
visual attention). These models fall into one of two
categories [6]:
• Models of visual sampling/monitoring behavior (how
do people scan/monitor a set of area). This
problematic is mainly studied in the aviation domain,
especially in supervisory control tasks. (cf. [6] for a
review)
• Models of visual search (how do people locate an
object in the visual environment).

We think that the study of both types of visual model can
help in our attention regulation problem, but, in this
paper, we focus only on visual search models, and
notably, due to its simplicity, the guided search v2.0
proposed by Wolfe.

2.2. The “guided search2.0” visual attention model

A person searching for visual targets among distractor
items, guide attention with a mix of top-down (over or
endogenous attention) and bottom up activations (covert
or exogenous attention). The bottom-up activation is
depends of the stimuli’s properties and largely
independent of the user’s knowledge. Treisman [18]
proposed that in a preattentive stage, only simple basic
visual features, such as intensity, color, orientation,
motion are computed in a parallel manner over the entire
visual scene resulting in feature maps. These feature maps
aim at detecting salient areas in the scene for each feature.
However, saliency cannot always capture attention in a
purely bottom-up fashion if attention is focused or
directed elsewhere in advance. Thus it is necessary to
recognize the importance of how attention is also
controlled by top-down information relevant to current
visual behaviors. The information that guided your
attention in this case can be labeled top-down—meaning
that it depended on the observer’s knowledge.


Fig. 1 – “guided search 2.0” visual attention model

In the guided search model, a visual scene is decomposed
in feature maps for each feature (intensity, color,
orientation, motion, etc..). All the feature maps are then
combined and modulated by the user’s task into a unique
scalar ``saliency map'' which encodes for the saliency of a
location in the scene (see Fig.1 ).

3. Our approach
3.1. Received items as a “visual” perception of the
network‘s activity

In our context the stimuli are not visual signals, but
communication signals. We are aware of the activity of
our network and our communities by the information we
receive (email, rss feed,IM). In our research we assume
that this perception can be represented in a visual way
where each signal is not visual but a communication
signal (incoming message). The signal’s properties are
not color, luminance, motion but the message’s author,
content, date, popularity etc.

3.2. Feature maps (bottom up)

As we said previously, features are attractable properties
guiding covert attention. In a communication context we
propose 7 salient preattentive features that could attract
the attention of the receiver without any knowledge (i.e.
without regard the user’s current interests). For each
feature, all the signals (e.g. incoming messages) are
computed according to the feature to produce a feature
map (i.e. a distribution of the saliency of the signals in
this feature)

Author’s influence: As suggested by [20] prioritizing
messages by contact’s importance will improve the email
system, due to the fact that less unsolicited attentional
demands come from important senders. Without knowing
the current context of the user, the definition of an
important sender can be based upon his reputation or local
affinity in the user’s social network. Guéguen [7] has
shown that the information on the reputation of the sender
has an influence of the reading and response of an email.
Hence the author’s reputation feature map has a value
range between 0 (low reputation) and 1 (high reputation).

Popularity influence: collective attention. The popularity
of an item influences clearly the selection of the message
[9]. In large-scale communities (digg.com, YouTube,
Del.icio.us, Slashdot) the popularity of an item (e.g.
number of votes, views, comments) plays an important
role due to the information overload.
This social filtering process is, in our context, perceived
in a different way. We call this behavior collective
attention alignment. The user is attracted by resources to
which a lot of people have paid attention to. In selecting
popular items the user aligns his attention toward the
attention of the whole community. A social attention
aware system should therefore detect attention alignment
disorder (e.g. a user unaware of messages that seem have
attracted the attention of his social network).
The value range of the feature map characterizing the
collective attention or popularity of the message is
normalized: 0 (low collective attention) to 1 (high
collective attention)

Temporal influence: Lifecycle of a signal: In general the
messages are ordered by time due to the fact that a new
message will attract more attention than an old one,
except if the message is a reminder (e.g. a call for a
conference in 1 month). In this case the reminder will
attract more and more the user’s attention until its
deadline. The value range of the lifecycle’s feature map
characterizing the temporal aspect of the message is
between 0 (obsolete) to 1 (active)

Issue/topic’s scarcity: Without knowing the user’s context
and so his current topic’s interest, a message about an
unusual topic attracts by nature more attention than a
common topic. The value range of the topic importance
map is between 0 (common topic/issue) and 1 (rare
topic/issue)

Medium influence. We have introduced the influence of
the medium in using the Media Richness Theory [2].This
theory suggests that media vary in certain characteristics
that affect an individual’s ability to communicate rich
information. The richer the media is in information, the
lesser effort is required from the user to get the
information. That is why a user will be, a priori, more
attracted to a multimedia content than a text. The value
range of the medium map characterizing the attractiveness
of the user for the media’s type is between 0 (poor
information medium e.g. text) and 1 (rich information
medium e.g. video)

Audience focus: A user is more attracted by messages
sent specially for him than messages sent for a large
community or for a public/anonymous audience [5]. We
can identify several types of audience: public, large,
middle, small audience and personal. The value range of

the audience map characterizing the attractiveness of the
user for the messages’ audience is between 0
(public/anonymous audience), to 1 (personal).

Explicit attention demand: A user is more attracted by an
urgent message than a normal message. The priority
property of an email allows the sender to explicitly solicit
the receiver’s attention. Any form of explicit attention
solicitation is a factor of attractiveness. The value range is
between 0 (no explicit solicitation) to 1 (urgent
solicitation)

3.3 Intention profile (Top-down)

For the user-driven or top-down activation, we have
created a simple intention profile. A user’s intention
profile, noted P is the set of concepts C (e.g. a task, an
issue, a person) interesting the user in a given context


(e.g. user’s current projects, current social environment).
At each concept c C is associated a weight 
representing its level of interest. The set of  is noted W.

P  C,W

The user has also a limited attention capacity. Because
the user can’t want to pay attention to everything if he
has, for instance, only 5 minutes we force him or her to
choose (behavior regulator) in adding an attention
capacity noted H. The user’s attention capacity is a
function depending on the interval of time (The longer the
interval is, the higher the user’s capacity is) and the user’s
effort, as described by Kahneman [9], that we attribute
to a context k (e.g. at work more attention is required than
at home). So P depends also of H by adding the
following constraint on W.

∑ 
 (∆t,k)  > 

with the minimum possible attention level. Due to
this limit the user has to choose his or her priorities
(paying attention to only a subset of his social network or
a limited set of topics according to his context). This
intention profile2 can be explicitly completed by the user

2
We can also take this profile to describe the user’s attention
and characterize it in studying its distribution: dispersed user =
small attention level for a lot of concepts) or concentrated user =
high level for very few concepts) and overloaded user =
distribution’s air > H.

or implicitly found in tracking the user’s activity ([11]
[14]).

Intention Map: We assume that for each signal  a
function can evaluate if the concept c is present or not in
the item (e.g. if the message’s sender belongs to the
current intention profile). We note ,  the result of this
evaluation (e.g. 0 or 1). Then we build an intention map
 , computing, for each item  its intention level 
such as   ∑ ,

3.4. Top down and bottom up influence

The saliency map, the output of the attention system,
represents the final saliency levels of the items. The set of
feature maps and the intention map are simply combined
and normalized (different features contribute with
different strengths to perceptual salience [12]) in a global
saliency map:
 
∑ ! " #
∑ ! " #


with F the set of the feature maps , the  coefficients
representing the importance of each feature in the bottom
up activation, and # the influence ratio between top-down
and bottom-up maps (#=∑ ! for an equal influence).
This ratio can be adjusted according to the user’s
preference.

Fig. 2 – An overview of the attention aware system
4. Scenarios

At work: managing the user’s solicitation

A common scenario for a knowledge worker is when he
has to do a collaborative task. He wants to focus mainly
his attention on messages sent by certain colleagues
(or/and certain topic related to his task). He configures an
intention profile representing this degree of social focus
to limit solicitations out of the context (e.g.
Pk=(Colleague1: 1, (Colleague2, 0,5) (colleague3, 0,2) ).
To not disturb the user, a certain perception threshold pS
is set : only message with a saliency higher than pS will
be notified directly to the user. So the user will be aware
of targeted messages or exceptionally important but
unexpected solicitations. Futhermore if he wants check
his or her messages , the attention aware system can rank
them by saliency and recommend only the top-n messages
to decrease the user’s effort.

At home: managing rss feeds
Having subscribed to several general and active
communities or blogs a user doesn’t want to be
overloaded by all sorts of article. At home he prefers to be
aware of new articles about politics and business, his
favorite topics. But he still wants to be aware of the others
topics but only popular ones. So his draws his preferences
in an intention profile as following:


Fig. 3 - intention profile of the user when he’s at Home
So due to the influence of both the popularity as a salient
feature and the intention profile, the attention system
allows to filter not so popular articles about business (top
down influence) but also only popular articles about
technology (bottom up influence).

Sensitive to the context
According to the context (at work, at home or switch from
one task to another one) the user can apply a specific
intention profile to have a customized perception of his or
her network’s activity without being totally closed to
unexpected/important events.


5. Conclusion

Our system attempts solve a common problem in a rich
environment where the user is commonly interrupted by
incoming messages or/and have to manage a lot of
messages (email, rss feed, IM chat). Our original
approach is to use existing visual attention models in this
communication context due to the similarity of the
problem, in adapting signal from a visual perspective to a
communication perspective. Due to a mix of a bottom-up
and top-down influence, this attention aware system:
• Is sensitive to the user’s context (intention profile)
• Is able to filter unwanted messages according to the
user’s interest but also able to accept important but
unexpected messages.
• The ranking model doesn’t take only factors from the
message’s content (topic scarcity), but also social
ones (popularity, author’s reputation or trust, explicit
attention demand, audience’s focus), temporal or
medium-related ones.
This basic model of an attention aware system has to be
evaluated and adjusted, notably the importance of each
feature in the user’s judgment to find what is attractive. In
parallel we are going to extend and consolidate it in
studying more attention models like the ART[1] and the
SEEV model[6].

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