A

Models of TTENTICommunication: From P r
Creating computing and One of the main results of 20th century cognitive
communication systems psychology is that, despite the overall impressive abilities
that sense and reason of people to sense, remember, and reason about theworld, our cognitive By Eric Horvitz, Carl Kadie,
about human attention
abilities are extremely
Tim Paek, and David Hovel
by fusing together limited in well-characinformation from terized ways. In particular, psychologists have found thatmultiple streams. people grapple with scarce attentional resources and limited working memory. Such limitations become salient
when people are challenged with remembering more than a handful of new ideas oritems in the short term, recognizing important targets against a background patternof items [4], or interleaving multiple tasks [5].
These results indicate that we cannot help but
to inspect the world via a limited spotlight ofattention. As such, we often generate clues implicitly and explicitly about what we are selectively attending to and how deeply we arefocusing. Given constraints on attentional
resources, it is no surprise that communication among people relies deeply on
attentional signals. Psychologists and linguists studying communication have recognized that signaling and detecting
attentional states lies at the heart of the fast-paced
and fluid interactions that people have with one
another when collaborating or communicating[1, 6]. Attentional cues are central in decisions
about when to initiate or to make an effective
contribution to a conversation or project. Beyond
knowing when to speak or listen in a conversa-

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tion, attention is critical in detecting that a conversation is progressing. More generally, detecting
or inferring attention is an essential component of
the overall process of grounding—converging in a shared manner on a mutual
understanding of a communication [1].
The findings about our limited attentional resources—and about how we rely
on attentional signals in collaborating—have significant implications for how wedesign computational systems and interfaces. Over the last five years, our team at
Microsoft Research has explored, within the
Attentional User Interface project, opportunities
for enhancing computing and communication
systems by treating human attention as a central
construct and organizing principle. We consider
attention a rare commodity—and critical cur-

ON in Computing andP rinciples to Applications
rency—in reasoning about the information
awareness versus disruption of users [11]. We
have also pursued the use of attentional cues as animportant source of rich signals about goals,
intentions, and topics of interest [9]. We seek tobuild systems that sense, and share with users,
natural signals about attention to support conversations and other forms of fluid mixed-initiative
collaborations with computers. Moving to considerations of computational efficiency, an assessment of a user’s current and future attention canbe employed to triage computational resources.
Investigations in this realm include selective allocation of resources in rendering graphics via relying on models or on direct observations of visual
attention, and in guiding precomputation andprefetching [10] with forecasts of future attention. Finally, although there is a rich history ofprior work on attention from cognitive psychology, we have found there is much we do not yet
understand. Thus, beyond pooling results fromprior psychological studies, we need to continue
to perform user studies that adapt or extend prior
results on attention and memory from cognitive
psychology to real-world computing and communication applications [2, 3].
We shall first describe several principles andmethodologies at the heart of research on integrating models of attention into human-computer interaction and communications. Then, weshall review representative efforts illustrating how
we can harness these principles in attention-sensitive messaging and mixed-initiative interaction
applications.

Models of Attention and Decision
Making Under Uncertainty
How might we access and use information
about a user’s attention? To be sure, subtle clues
about attention are often available, and a number of these clues can be taken as direct signals
about the attentional status of users. For example, sensing patterns of simple gestures such as
the touching and lifting of a device in different
settings can relay important information about
attention that can be exploited in a number of
exciting ways [7]. Moving to higher-precision
sensing, several researchers have pursued the useof gaze-tracking systems, and have used signals
about the focus of visual attention in a variety of
applications. As gaze sensors grow in reliability
and decrease in cost, we are seeing the evolutionof devices that recognize when and how they areinterrogated by the spotlight of visual attention.
Nonetheless, we may often be uncertain about
a user’s attentional focus and workload in light ofobservations, and about the value of alternate
actions in different contexts. Thus, we turn tomodels that can be harnessed to reason about auser’s attention and about the ideal attentionsensitive actions to take under uncertainty.
Such models and reasoning can unleash new
functionalities and user experiences.
We have constructed by hand and learned
from data Bayesian models viewed as performing
the task of an automated “attentional Sherlock
Holmes,” working to reveal current or future
attention under uncertainty from an ongoingstream of clues. Bayesian attentional models take

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Perceptual
Sensors

Device Interaction

CalendarDay, Time
Location, tn+1

Location, tn

Attentional
Focus & Workload

Calendar

Visual pose, tn+1
CalendarDay, TimeAppt
Status, tn

Computer's
Actions

Day, Time

Attentional
Focus, tn

Acoustical
Signal, tn

Appt
Status, tn+1

Inspection
Interval, tn

Expected
Utility

App Usage
Pattern, tn(a)Figure 1. (a) High-level decision model considering a user’s
attentional focus and workload as a random variable, influenced
by the observed states of several sensors. (b) A temporal
Bayesian attentional model, highlighting key dependencies(dashed arcs) between variables in adjacent time slices.

as inputs sensors that provide streams of evidence
about attention and provide a means for computing
probability distributions over a user’s attention andintentions.
Perceptual sensors include microphones listening
for ambient acoustical information or utterances,
cameras supporting visual analysis of a user’s gaze or
pose, accelerometers that detect patterns of motionof devices, and location sensing via GPS and analysis
of wireless signals. However, more traditional sourcesof events can also offer valuable clues. These sources
include a user’s online calendar and considerations of
the day of week and time of day. Another rich streamof evidence can be harvested by monitoring a user’s
interactions with software and devices. Finally, background information about the history of a user’s
interests and prior patterns of activities and attention
can provide valuable sources of information about
attention.
To build probabilistic attentional models able tofuse evidence from multiple sensors, we leverage theresults of accelerated research over the last 15 years
on representations for reasoning and decision making under uncertainty. Such work has led to inferential methods and representations including Bayesian
networks and influence diagrams—graphical models
that extend probabilistic inference to considerationsof actions under uncertainty. Algorithms have beendeveloped that enable us to compute probability distributions over outcomes and expected utilities ofactions from these graphical representations.
Figure 1a displays a high-level influence diagram
representing sensor fusion and decision making inMarch 2003/Vol. 46, No. 3 COMMUNICATIONS OF THE ACM

Acoustical
Signal, tn+1
App inFocus, tn+1

Inspection
Interval, tn+1

User, Device
Activity, tn

Time n54

Attentional
Focus, tn+1

App inFocus, tn

Cost of
Actions

Value of
Actions

Visual pose, tn

App Usage
Pattern, tn+1

User, Device
Activity, tn+1

Time n+1(b)

the context of a user’s attention under uncertainty. As
portrayed in the figure, a set of variables (oval nodes)
representing sensed evidence influence a randomvariable representing a user’s attentional status which,in turn, influences the expected value of alternate
actions or configurations. We introduce intermediate
cost and benefit variables in the pedagogical model as
it can be useful to deliberate about the value andcosts associated with different outcomes. Decisions(rectangular node) about ideal computer actions take
into consideration the costs and benefits, given
uncertainty about a user’s attention. In the end, theexpected utility (diamond-shaped node) is influenced by the action and the costs and benefits.
We extend such a high-level, pedagogical view byconstructing richer models that contain additional
intermediate variables and key interdependencies
among the variables. Also, as both devices and people
are immersed in time, we move beyond pointwise
considerations of the states of variables, to build
higher-fidelity temporal attentional models that represent changing observations and beliefs with theflow of time. We have employed dynamic Bayesian
networks and Hidden Markov Models for representing and reasoning about states of attention and location over time.
Figure 1b displays two adjacent time slices of atemporal attentional model. Such a model provides aprobability distribution over a user’s workload andtask developed for an application that provides selective filtering of messages and communications tousers. In this case, the status of attention includes
approximately 15 discrete states.

Economic Models of Attention andInformation
As we can all attest from personal experiences, computers and communication systems today have little

Sensors
Preferences
Sources
Email
Incoming
messages

IM
Voice

Notificaton
preferences

News
Financial
Attenton-sensitive
message
Decision model

Attention model

File Scout

Notification
Manager

Background
Query• Best device & time?
• Modality & intensity?

Assistance(a)the time until a user will review
unread messages. The systemcomputes an expected cost of
delayed review for each incoming
message. This cost is considered,along with a cost of interruption
based on activity sensing and calendar information, in automateddecisions about if and how to alert(b)
and transmit information to a user
about email, tasks, and appointment reminders in mobile and desktop settings.
The Notification Platform uses a decision-analytic
model for cross-device alerting and relay of information from multiple sources. The analyses consider auser’s attention and location under uncertainty, as wellas the fidelity and relevance of potential communication channels. We developed a distributed architecture that executes over multiple devices. Figure 2b
displays a schematized view of the architecture of the
Notification Platform. Standard interfaces and metadata schemas allow users to subscribe different sourcesof information and devices to a Notification Manager.
At the heart of the Notification Manager is a Bayesianattention model and decision analysis that accesses
clues about attention and location from sensors via a
module we refer to as a Context Server.
The Context Server accesses several states andstreams of evidence, including a user’s appointmentsfrom Microsoft Outlook, events about device presence and activity, an analysis of ambient acoustics inthe room, and a visual analysis of pose using a
Bayesian head-tracking system. Key abstractions fromthe evidence, such as “voice trace detected,” “taskcompletion occurred within five seconds,” “single
application focus,” “head-tracked—looking awayfrom display,” and “meeting away from office—ending in 10 minutes,” are posted to a volatile store called
the Context Whiteboard, which is continually updated
by incoming evidence. The Context Whiteboard iscontacted for updated information every few seconds
by the Bayesian attentional model in the Notification
Manager.
The Notification Manager’s decision analysis
weighs the expected costs and benefits of alerting auser about messages coming into the system’s Universal Inbox. In computing the costs of disruption, thedecision model considers the probability distribution
over a user’s attentional state and location in several
places in its analysis, including the cost of disruption
associated with different alerts for each device, theavailability of different devices, and the likelihood thatthe information will reach the user when alerted in a
specific manner on a device.

Device interaction
Ambient acoustics
Visual analyses
Sensing
Accelerometers
CalendarProximity detectors
Location(802.11/GPS)
Context ServerContext
Whiteboard

Error
Messages

Notification
schemaFigure 2. Conceptual overview of the Notification Platform, across-device messaging system that balances the costs of
disruption with the value of information from multiple message
sources. The system employs a probabilistic model of attention
and executes ongoing decision analyses about ideal alerting,fidelity, and routing. (b) Constellation of components ofNotification Platform, depicting the subscription architecture.
Subscribed sources and devices communicate with the Notification
Manager via a set of standard interfaces. Sensor findings frommultiple devices are considered in deliberations about information
value, attention, and the best channel and alerting modality.

awareness of the value and costs of relaying messages,
alerts, and calls to users. Research on the Notification
Platform project has centered on formulating economic principles of attention-sensitive notification—and on implementing a cross-device-alerting
system based on these principles. A descendant of
the Notification Platform named Bestcom applies
similar principles to interpersonal communications[12]. We focus here on the Notification Platform.
The Notification Platform system modulates theflow of messages from multiple sources to devices byperforming ongoing decision analyses. These analyses
balance the expected value of information with theattention-sensitive costs of disruption. As highlighted
in Figure 2a, the system serves as an attention-savvy
layer between incoming messages and a user, taking as
inputs sensors that provide information about a user’s
attention, location, and overall situation.
The design of the Notification Platform wasinformed by several earlier prototypes exploiting context-sensing for identifying a user’s workload, including the Priorities system [11, 12]. Priorities employs
classifiers that predict the urgency of incoming email.
The classifiers are trained with sample messages,
either obtained via explicit training or by automatically drafting data sets by observing a user’s interaction with an email browser. Studies havedemonstrated the system performs remarkably well atclassifying the urgency of messages (see, for example,
the receiver-operator characteristic curve described in[11]). Beyond classifying the urgency of messages,
Priorities also observes a user’s patterns of presence ata desktop computer based on time of day, and infers

Endpoints
DesktopofficeDesktop
home
PDA

Cell
phone

VoicemailDeviceschemaContent
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Device i, Modality jTransmitted
value

Fidelity
Now

Later

Attentional
cost

Transmission
reliability

Disruption

Information value

Display and alerting
configuration

Inferred
attention

Net value

Figure 3. (a) Graphical depiction of
the Notification Manager’s analyses.
Attention-sensitive costs of disruption andthe value of information are considered,along with losses based in decreased
fidelity (narrowing funnel) and transmission reliability (spinning slotted disk)
associated with the use of each alerting
modality of all subscribed devices.
(b) View of a portion of the Notification
Platform’s real-time reasoning. Informationfrom multiple sensors is posted to theContext Whiteboard and fused to infer theuser’s attentional status and location.
Multiple notifications are sorted by net
expected value and the best channel
and alerting modality with the highest
expected utility is selected.

(a)

(b)

The ongoing expected-utility analysis is performedin accordance with a user’s preferences, stored in a
profile. These include assertions about the cost of disruption for each alert modality, conditioned on theuser being in different attentional states. As an example, for the case of a desktop computer, the system
makes available a set of display alternatives as the
product of different visual displays of the alert (forexample, thumbnail, full-display alert) and several
auditory cues (for example, no auditory clue, soft
chime, louder herald). The placement of the alertwith regard to the current focus of visual attention orinteraction is also considered.
Figure 3a captures in a graphical manner the deliberation of the Notification Platform about incoming
messages. The system computes the expected value of
receiving an alert as the difference between the valueof alerting the user now and the value that will beobtained when the information is viewed later. Givenprobability distributions over a user’s attention andlocation inferred from its sensors, Notification Platform iterates over all alerting and display modalities
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for each device with an expected-utility analysis todecide if, when, and how to alert a user. As represented with the metaphor of a narrowing funnel in
Figure 3a, the system considers, for each device andmodality, the loss in fidelity of information transmitted. In addition, the system considers the likelihoodthat an alert will be received, given inferred probability distributions over the attention and location of theuser. This reliability of transmission is represented
metaphorically in the figure as the chance that a message will make it through a slot in a spinning disk. In
the end, the attention-sensitive costs of disruption are
subtracted from estimates of the value of alerting,yielding a net value of alerting a user for each channel
and alerting modality. The channel and modality
with the highest expected value is selected.
Figure 3b displays several aspects of the behindthe-scenes functioning of the Notification Platform.
A context palette displays current findings drawn fromsensor sources. Several views onto components of thedecision analysis are displayed, including inference
about the time-varying attention of the user. At thecurrent time, the user is inferred to be most likely ina state named “high-focus solo activity,” which hascompeted recently with “low-focus solo activity,”“conversation in office,” and other less likely states.
The Universal Inbox displays messages from several
sources, including email, instant messaging, breaking
news, and stock prices. Messages have also been
received from DocWatch, an agent subscribed to by
the user that identifies documents of interest for theuser. Each message is annotated with the best device
and alerting policy, and the associated net expected
dollar value of relaying the messages with that channel and mode is indicated. As portrayed in the inbox,
it is worthwhile passing two instant messages on to
the user. Other alerts are “in the red,” as the cost of

Figure 4. Sensing PDA, outfitted with multiple perceptual
sensors, including proximity, motion, and touch sensors. In the
background, accelerometer signals are displayed showing the
motion fingerprint of a user walking while looking at the device.

disruption dominates the net value of information. In
this case, the ideal alerting mode and channel for an
instant message is determined to be a visual notification in a large format coupled with an audio herald at
the user’s desktop system.
Ongoing research on the Notification Platformproject includes the refinement of preference assessment tools to ease the task of encoding preferences.
Currently, users can adjust sliders to change a set ofpredefined defaults on costs of interruptions. Anotherkey area of work centers on using machine learningfor building probabilistic models of attention, location, and cost of disruption from data. Results fromongoing machine-learning efforts by our team havebeen applied to refine the Notification Platform [12].
As highlighted in Figure 4, we have also beenworking to make small devices aware of the attentional status and location of users [7]—and eithertransmitting local sensor information to inform acentral Notification Manager, performing entirely
local notification management and related services
based on the observations, or doing a combination ofcentral and local deliberation about notification. In
the latter case, the central Notification Manager
makes general decisions about routing, and relies on
the endpoint device to perform precision targeting of
the timing and alerting modality, based on local sensing and reasoning. As an example, with the use of amethod we refer to as “bounded deferral,” a local
device commits to relaying a message that it hasreceived before a message-specific deadline is reached;
the device does its best to find a good time for interruption within the allotted period. Research on smart
endpoints includes the challenges of embedding andleveraging multiple perceptual sensors on small

devices, including GPS, 802.11 signal strength,
accelerometers, infrared proximity detectors, andtouch sensors. Part of this work has explored opportunities for developing devices, such as cell phonesthat behave with more insight about their disruptiveness by considering the situation at hand, including
states derived from coarse models of attention [8].
Additionally, we are continuing to pursue psychological studies of disruption. Formal studies of thecosts of disruption began with the early work of
Ovsiankina and Zeigarnik nearly 75 years ago. The
rich body of work in this realm includes studies on
memory, problem solving, and overall task efficiency
in the face of disruptions. More recent work includes
efforts by our team [2, 3] and other groups to probe
the influence of notifications of various types andsaliencies on the efficiency and satisfaction with performing a variety of computer tasks. The psychological studies and results complement the mathematicalmodels; the economic models provide a principled,
flexible foundation that can integrate findings about
the costs uncovered by user studies of disruption via
the setting of parameters considered in expected-utility decision making.

Attention, Initiative, and Interaction
In another area of investigation within the Attentional User Interface project, we have studied the use
models of attention to enhance the robustness andfluidity of human-computer collaboration. Some ofthis work focuses on the recognition of attentionalcues as coordinative signals in mixed-initiative interaction with computing devices. In mixed-initiativeinteraction, both users and computers take turns incontributing to a project or an understanding [9].
The turn taking of conversational dialogue is a prototypical example of mixed-initiative interaction.
Psychologists have found that people engaged inconversations rely on attentional cues to signal whena contribution is going to be offered or has been
accepted [1]. We have sought to endow computerswith an analogous ability to recognize and emit signals to guide the nature and timing of contributions
and clarifications in support of mixed-initiativeinteraction.
DeepListener and Quartet represent efforts inmixed-initiative interaction to incorporate attention
in spoken language systems. Both systems tackle
what we have referred to as the “speech-target problem:” When a computer with an open microphoneand speech recognizer detects an utterance, how is it
to recognize that it is being addressed when there areother people or listening devices in a room? DeepListener and Quartet explicitly address this challenge
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Figure 5. (a) DeepListener’sdeliberation about the target ofspeech and ideal clarification
dialog. The system first makes
an expected utility decision toshare in a subtle manner its
thoughts about the possibility
that it is the target of anutterance. Given additionalrecognitions, it goes ahead toseek clarification, and finally
executes an action for the user.
(b) Quartet in action. Quartet’s
partial recognition is displayed
at the top of the display. The
system’s belief about theattentional status of the user,
with regards to initiating,
maintaining, or breaking outof conversational dialogue, isrepresented as a dynamicallychanging probability
distribution.

(a)

(b)

with probabilistic models that infer the likelihoodthat they are the target of speech.
DeepListener reasons about a user’s attention andintentions to guide clarification dialogue in a spoken
command and control setting. The system considers
its uncertainty about whether it is the target ofspeech, what it has heard, and the likelihood of different intentions. DeepListener continues to makeexpected-utility decisions about taking actions in theworld, or about how it should approach users, if necessary, to clarify their intentions before taking suchworld actions. These decisions take into consideration
the utilities of alternate dialogue actions and thestakes of the world actions.
DeepListener shares its attention and availability
by gracefully changing the colors and intensities of an
attentional lens that glows on its control panel, or via
gestures and rendered “thoughts” of an animatedagent. These affordances provide cues that assist withconversational turn taking.
Figure 5a displays a situation where DeepListenerhas detected an utterance first directed elsewhere in a
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noisy environment. After
analyzing a new utterance
a bit later, the systemengages the user in a clarification dialogue, and theninvokes a desired action.
Quartet operates with acontinuous speech recognition system, and incorporates a richer model ofattention under uncertainty. It examines keyboard events, an analysis of
the content and the coherence of natural language parsing, and a visual pose
analysis to ascertain the attentional status of the userand system with regards to the establishment, maintenance, and disruption of attention between the userand system. Since Quartet couples speech recognition
to a natural language parser, the system can also use
the grammatical parse to reason about whether anutterance was misrecognized, or properly recognized
but intended for someone else. Figure 5b shows
Quartet listening to a user talking about the systemrather than speaking to the system. In this case, Quartet is being used as an assistant to control, via voicecommands, the navigation of slides displayed in a presentation. Requests directed to Quartet about navigation among slides arise intermittently during themore dominant stream of ongoing utterances associated with the presentation. In this example, the user
is talking about the computer, and, based on a fusionof the user’s language and visual pose, Quartet infers
the user is likely speaking to someone else.
Our ongoing research on mixed-initiative and spoken-language systems is focusing on several chal-lenges, including the use of sensed or inferred attention to provide clues about a user’s intentions, thecontent and context at hand, and the nature and ideal
timing of the appropriate contributions. This work
includes using sensed or inferred attention to informspeech recognition systems about the specific microcontexts being addressed with utterances. Such narrowing of the spotlight of analysis can be useful forenhancing recognition as it can enable spoken dialogue systems to swap in the appropriate language
models and semantics, and adjust the scope of possible actions. Also, robust solutions to the speech-target
problem promise to significantly influence the overallsociology of human-computer interaction, by allowing users to interact with multiple devices and people
in their proximity with speech and gestures in a manner similar to the way people interact with one
another.
In another realm of innovation, computers with anability to track and to understand attentional patterns
among people engaged in conversations can provide
new kinds of services and facilities. For example,
methods for identifying visual attention among participants in a conversation can be used to automate
the control of cinematography, and to capture, organize, and understand a group meeting or videoconference. Thus, beyond enhancing human-computerinteraction, sensing and reasoning about attention
promises to enhance the way we communicate andcollaborate with one another.

Conclusion
We have described efforts to endow computing andcommunication systems with the ability to sense andreason about human attention. After reviewing somebackground on the nature and importance of attention in cognition and discourse, we discussed methods for inferring attention from multiple streams of
information, and for leveraging these inferences indecision making under uncertainty. Then, we presented illustrative applications of the use of attentional models in cross-device, multichannel
messaging and in mixed-initiative interaction.
Research on the use of models of attention in computing and communication is still in its youth. We
expect that continuing refinement of methods forrecognizing, reasoning, and communicating about
attention will change in a qualitative manner theway we perceive and work with computing systems
and devices. c
CONTRIBUTORS ON THE CONSTELLATION OF EFFORTS ON THE ATTENTIONALUSER INTERFACE PROJECT INCLUDE JOHNSON APACIBLE, ED CUTRELL, MARYCZERWINSKI, SUSAN DUMAIS, KEN HINCKLEY, DAVID HOVEL, ANDY JACOBS,
CARL KADIE, PAUL KOCH, JOHN KRUMM, NURIA OLIVER, TIM PAEK, JOHNPLATT, DANIEL ROBBINS, CHAITANYA SAREEN, JOE TULLIO, MAARTEN VAN
DANTZICH, AND ANDY WILSON.

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THE COMPLETE SET OF REFERENCES FOR THIS ARTICLE CAN BE ACCESSED AT
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Eric Horvitz (horvitz@microsoft.com) is a senior researcher andgroup manager of the Adaptive Systems & Interaction Group at
Microsoft Research, Redmond, WA.
Carl Kadie (carlk@microsoft.com) is a research software design
engineer at Microsoft Research, Redmond, WA.
Tim Paek (timpaek@microsoft.com) is a researcher in the Adaptive
Systems + Interaction Group at Microsoft Research, Redmond, WA.
David Hovel (davidhov@exmsft.com) is a software developer at
Microsoft Research, Redmond, WA.
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