[ieee 2010 11th international symposium on computational intelligence and informatics (cinti) -...

Cognitive Infocommunications:

CogInfoCom

Peter Baranyi

3DICC Laboratory, the Consortium of

Budapest University of Technology and Economics (BME) -

Department of Telecommunication and Media Informatics (TMIT)

Computer and Automation Research Institute (MTA SZTAKI) -

Hungarian Academy of Sciences

Email: [email protected]

Adam Csapo

3DICC Laboratory, the Consortium of

Budapest University of Technology and Economics (BME) -

Department of Telecommunication and Media Informatics (TMIT)

Computer and Automation Research Institute (MTA SZTAKI) -

Hungarian Academy of Sciences

Email: [email protected]

Abstract—In recent years, considerable amount of researchhas been dedicated to the integration of artificial cognitivefunctionalities into informatics. With the immense growth involume of cognitive content handled by both artificial andnatural cognitive systems, the scientific treatment of new andefficient communication forms between such cognitive systems isinevitable. In this paper, we provide the first definition of cognitiveinfocommunications, a multidisciplinary field which aims toexpand the information space between communicating cognitivesystems (artificial or otherwise). Following this definition, wespecify the modes and types of communication which make upcognitive infocommunications. Through a number of examples,we describe what is expected from this new discipline in furtherdetail.

I. INTRODUCTION

The idea that the information systems we use need to

be accomodated with artificially cognitive functionalities has

culminated in the creation of cognitive informatics [1], [2].

With the strong support of cognitive science, results in cogni-

tive informatics are contributing to the creation of more and

more sophisticated artificially cognitive engineering systems.

Given this trend, it is rapidly becoming clear that the amount

of cognitive content handled by our engineering systems is

reaching a point where the communication forms necessary to

enable interaction with this content are becoming more and

more complex [3], [4], [5], [6], [7].

The inspiration to create engineering systems capable of

communicating with users in natural ways is not new. It is

one of the primary goals of affective computing to endow

information systems with emotion, and to enable them to

communicate these emotions in ways that resonate with the

human users [8], [9]. On the other hand, there are a whole host

of research fields which concentrate less on modeling (human)

psychological emotion, but aim to allow users to have a more

tractable and pleasurable interaction with machines (e.g., hu-

man computer interaction, human robot interaction, interactive

systems engineering) [10], [11]. Further fields specialize in the

communication of hidden parameters to remote environments

(e.g., sensory substitution and sensorimotor extension in engi-

neering applications, iSpace research, multimodal interaction)

[12], [13], [14], [15], [16], [17], [18].

In recent years, several applications have appeared in the

technical literature which combine various aspects of the

previously mentioned fields, but also extend them in significant

ways (e.g., [19], [20], [21], [22], [23], [24], [25], [18], [26],

[27], [13], [28]). However, in these works, the fact that a new

research field is emerging is only implicitly mentioned. The

goal of this paper is to provide a concise but clear definition

of cognitive infocommunications, and to further demonstrate

through examples what is expected from results within this

research field.

II. DEFINITION

Cognitive infocommunications (CogInfoCom)

investigates the links between the research areas of

infocommunications, informatics and cognitive sciences,

as well as the various fields which have emerged as

a combination of these sciences. The primary goal of

CogInfoCom is to provide a complete view of how brain

processes can be merged with infocommunications devices

so that the cognitive capabilities of the human brain may not

only be efficiently extended through these devices, irrespective

of geographical distance, but may also be efficiently matched

with the capabilities of any artificially cognitive system. This

merging and extending of cognitive capabilities is targeted

CINTI 2010 • 11th IEEE International Symposium on Computational Intelligence and Informatics • 18–20 November, 2010 • Budapest, Hungary

978-1-4244-9280-0/10/$26.00 ©2010 IEEE- 141 -

towards engineering applications in which any combination

of artificial and biological cognitive systems are required to

work together.

We define two important dimensions of cognitive infocom-

munications: the mode of communication, and the type of

communication.

The mode of communication refers to the actors at the two

endpoints of communication:

• Intra-cognitive communication: The mode of com-

munication is intra-cognitive when information transfer

occurs between two cognitive beings with level cognitive

capabilities (e.g., between two humans).

• Inter-cognitive communication: The mode of communi-

cation is inter-cognitive when information transfer occurs

between a two cognitive beings with different cognitive

capabilities (e.g., between a human and an artificially

cognitive system).

The type of communication refers to the type of informa-

tion that is conveyed between the two actors, and the way in

which this is done:

• Sensor-sharing communication: The type of commu-

nication is sensor-sharing when the sensory information

obtained or experienced by each of the actors is merely

transferred to the other end of the infocommunications

line, and therefore the same sensory modality is used on

both ends to perceive the information.

• Sensor-bridging communication: The type of commu-

nication is sensor-bridging when the sensory information

obtained or experienced by each of the actors is not

only transferred to the other end of the line, but also

reallocated and transformed to an appropriate sensory

modality on the receiver end. A sensor-bridging appli-

cation uses the plasticity of biological cognitive systems

to create an effective matching between the properties of

the remotely obtained information (e.g., the number of

its dimensions, its density and its communication speed,

etc.) to the properties of the receiving sensory modality

(e.g., the number of perceptible dimensions using the

modality, the resolution with which the modality can be

used to perceive each dimension, and the speed at which

the modality can process the information, etc.).

Sensor-bridging communication also includes scenarios

in which the two communicating actors are in a master-

slave relationship. In such cases, the master can be

a human user, and the slave can be a simple sensor

that performs remote sensing and/or preprocessing tasks

(therefore, the slave does not implement an autonomous

cognitive system at all). Such communication could serve

to allow the human operator to directly sense information

which is not normally directly perceptible through the

sensors of his/her cognitive system, and therefore, an

efficient mapping may be created between the highly

sophisticated organization of cognitive content in the

environment and the possibilities afforded by the human

nervous system for the sensing and representation of this

cognitive content.

Remarks

1) Dominant parts of a number of basic ideas behind

CogInfoCom are not new in the sense that different

aspects of many key points have appeared, and are

being actively researched, in several existing areas of IT

(e.g. affective computing, human computer interaction,

human robot interaction, sensory substitution, sensori-

motor extension, iSpace research, interactive systems

engineering, ergonomics, etc.)

2) CogInfoCom should not be confused with computa-

tional neuroscience or computational cognitive model-

ing, which can mainly be considered as a very impor-

tant set of modeling tools for cognitive sciences (thus

indirectly for CogInfoCom), but have no intention of

directly serving engineering systems.

3) A sensor-sharing application of CogInfoCom is novel in

the sense that it extends traditional infocommunications

by conveying any kind of signal normally perceptible

through the actor’s senses to the other end of the commu-

nication line. The transferred information may describe

not only the actor involved in the communication, but

also the environment in which the actor is located.

The key determinant of sensor-sharing communication

is that the same sensory modality is used to perceive

the sensory information on both ends of the infocom-

munications line.

4) Sensor bridging can be taken to mean not only the way

in which the information is conveyed (i.e., by changing

sensory modality), but also the kind of information that

is conveyed. Whenever the transferred information type

is imperceptible to the receiving actor (e.g., because its

cognitive system is incompatible with the information

type) the communication of information will necessarily

occur through sensor bridging.

III. DISCUSSION

In this section, we examine the research areas treated within

CogInfoCom from two different points of view: the research

historical view and the cognitive informatics view.

A. Historical View

Traditionally, the research fields of informatics, media,

and communications were very different areas, treated by

researchers from significantly different backgrounds. As a

synthesis between pairs of these 3 disciplines, the fields of

infocommunications, media informatics and media communi-

cations emerged in the latter half of the 20th century (figure

1). The past evolution of these disciplines points towards their

convergence in the near future, given that modern network

services aim to provide a more holistic user experience, which

presupposes achievements from these different fields [29],

[30]. In place of these research areas, with the enormous

growth in scope and generality of cognitive sciences in the

P. Baranyi and Á. Csapó • Cognitive Infocommunications: CogInfoCom

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Fig. 1. The three fields of media, informatics and communications originallycreated separate theories, but are gradually morphing into one field today.From a research historical point of view, CogInfoCom is situated in the regionbetween cognitive informatics and cognitive communications.

past few decades, the new fields of cognitive media [31],

[32], cognitive informatics and cognitive communications [33],

[34] are gradually emerging. In a way analogous to the evo-

lution of infocommunications, media informatics and media

communications, we are seeing more and more examples of

research achievements which can be categorized as results

in cognitive infocommunications, cognitive media informatics

and cognitive media communications, even if – as of yet –

these fields are not always clearly defined [35], [36], [19],

[20], [24], [25], [37], [13], [38], [31].

The primary goal of CogInfoCom is to use information

theoretical methods to synthesize research results in some

of these areas, while aiming primarily to make use of these

synthesized results in the design of engineering systems. It

is novel in the sense that it views both the medium used

for communication and the media which is communicated as

entities which are interpreted by a cognitive system.

B. Cognitive Informatics View

Cognitive informatics (CI) is a research field which was

created in the early 21st century, and which pioneered the

adoption of research results in cognitive sciences within in-

formation technology [2], [1]. The main purpose of CI is

to investigate the internal information storing and processing

mechanisms in natural intelligent systems such as the human

brain. Much like CogInfoCom, CI also aims primarily to create

numerically tractable models which are well grounded from

an information theoretical point of view, and are amenable

to engineering systems. The key difference between CI and

CogInfoCom is that while the results of CI largely converge

towards and support the creation of artificial cognitive sys-

tems, the goal of CogInfoCom is to enable these systems to

communicate with each other and their users efficiently.

Thus, CogInfoCom builds on a large part of results in

CI, since it deals with the communication space between the

(a) Intra-cognitive infocommunications

(b) Inter-cognitive infocommunications

Fig. 2. Cognitive informatics view of CogInfoCom. The figure on the topshows a case of intra-cognitive infocommunication, and demonstrates thatwhile traditional tele-com deals with the distance-bridging transfer of rawdata (not interpreted by any cognitive system), cognitive infocommunicationsdeals with the endpoint-to-endpoint communication of information. The figureon the bottom shows a case of inter-cognitive infocommunication, when twocognitive systems with different cognitive capabilities are communicating witheach other. In this case, autonomous cognitive systems as well as remotesensors (sensorimotor extensions, as described in the definition of the sensor-bridging communication type) require the use of a communication adapter,while biological cognitive systems use traditional telecommunications devices.

human cognitive system and other natural or artifical cognitive

systems. A conceptual view of how CogInfoCom builds on CI

and traditional telecommunications can be seen in figure 2.

IV. EXAMPLES

We provide basic examples of the four specific combinations

of individual modes and types of communication, and one

more complex example which uses a combination of commu-

nication modes and types.

A. Intra-cognitive sensor-sharing communication

An example of intra-cognitive sensor-sharing communica-

tion is when two humans communicate through Skype or

some other telecommunication system, and a large variety of

information types (e.g. metalinguistic information and back-

ground noises through sound, gesture-based metacommunica-

tion through video, etc.) are communicated to both ends of the


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line. In more futuristic applications, information from other

sensory modalities (e.g. smells through the use of electronic

noses and scent generators, tastes using equipment not yet

available today) may also be communicated. Because the

communicating actors are both human, the communication

mode is intra-cognitive, and because the communicated in-

formation is shared using the same cognitive subsystems (i.e.,

the same sensory modalities) on both ends of the line, the

type of communication is sensor-sharing. The communication

of such information is significant not only because the users

can feel physically closer to each other, but also because the

sensory information obtained at each end of the line (i.e.,

information which describes not the actor, but the environment

of the actor) is shared with the other end (in such cases,

the communication is intra-cognitive, despite the fact that the

transferred information describes the environment of the actor,

because the environment is treated as a factor which has an

implicit, but direct effect on the actor).

B. Intra-cognitive sensor-bridging communication

An example of intra-cognitive sensor-bridging communi-

cation is when two humans communicate through Skype or

some other telecommunication system, and each actor’s pulse

is transferred to the other actor using a visual representation

comprised of a blinking red dot, or the breath rate of each actor

is transferred to the other actor using a visual representation

which consists of a discoloration of the screen. The frequency

of the discoloration could symbolize the rate of breathing, and

the extent of discoloration might symbolize the amount of air

inhaled each time. (Similar ideas are investigated in, e.g. [26],

[18]). Because the communcating actors are both human, the

communication mode is intra-cognitive. Because the sensory

modality used to perceive the information (visual system)

is different from the modality used to normally perceive

the information (it is questionable if such a modality even

exists, because we don’t usually feel the pulse or breath rate

of other people during normal conversation), we say that

the communication is sensor-bridging. The communication of

such parameters is significant in that they help further describe

the psychological state of the actors. Due to the fact that

such parameters are directly imperceptible even in face-to-face

communication, the only possibility is to convey them through

sensor bridging. In general, the transferred information is

considered cognitive because the psychological state of the

actors does not depend on this information in a definitve way,

but when interpreted by a cognitive system such as a human

actor, the information and its context together can help create a

deeper understanding of the psychological state of the remote

user.

C. Inter-cognitive sensor-sharing communication

An example of inter-cognitive sensor-sharing communica-

tion might include the transfer of the operating sound of

a robot actor, as well as a variety of background scents

(using electronic noses and scent generators) to a human actor

controlling the robot from a remote teleoperation room. The

operating sound of a robot actor can help the teleoperator gain

a good sense of the amount of load the robot is dealing with,

how much resistance it is encountering during its operation,

etc. Further, the ability to perceive smells from the robot’s

surroundings can augment the teleoperator’s perception of

possible hazards in the robot’s environment. A further example

of inter-cognitive sensor sharing would be the transfer of direct

force feedback through e.g. a joystick. The communication in

these examples is inter-cognitive because the robot’s cognitive

system is significantly different from the human teleopera-

tor’s cognitive system. Because the transferred information is

conveyed directly to the same sensory modality, the commu-

nication is also sensor-sharing. Similar to the case of intra-

cognitive sensor-sharing, the transfer of such information is

significant because it helps further describe the environment

in which the remote cognitive system is operating, which has

an implicit effect on the remote cognitive system.

D. Inter-cognitive sensor-bridging communication

As the information systems, artificial cognitive systems

and the virtual manifestations of these systems (which are

gaining wide acceptance in today’s engineering systems, e.g.

as in iSpace [13]) are becoming more and more sophisticated,

the operation of these systems and the way in which they

organize complex information are, by their nature, essentially

inaccessible in many cases to the human perceptual system and

the information representation it uses. For this reason, inter-

cognitive sensor bridging is perhaps the most complex area of

CogInfoCom, because it relies on a sophisticated combination

of a number of fields from information engineering and

infocommunications to the various cognitive sciences.

A rudimentary example of inter-cognitive sensor-bridging

communication that is already in wide use today is the

collision-detection system available in many cars which plays

a frequency modulated signal, the frequency of which depends

on the distance between the car and the (otherwise completely

visible) car behind it. In this case, auditory signals are used to

convey spatial (visual) information. A further example could

be the use of the vibrotactile system to provide force feedback

through axial vibrations (this is a commonly adopted approach

in various applications, from remote vehicle guidance to

telesurgery, e.g. [39], [40], [41], [42], [43]). Force feedback

through axial vibration is also very widespread in gaming,

because with practice, the players can easily adapt to the

signals and will really interpret them as if they corresponded

to a real collision with an object or someone else’s body [44],

[45]. It is important to note, however, that the use of vibrations

is no longer limited to the transfer of information on collisions

or other simple events, but is also used to communicate more

complex information, such as warning signals to alert the

user’s attention to events whose occurrence is deduced from a

combination of events with a more complex structure (e.g.,

vibrations of smart phones to alert the user of suspicious

account activity, etc.). Such event-detection sytems can be

powerful when combined with iSpace [23].

Finally, more complex examples of sensor bridging in


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Fig. 3. Scenario for the complex example, in which two remote telesurgeonsare communicating with each other and the telesurgical devices they are usingto operate a patient.

inter-cognitive communication might include the the use of

electrotactile arrays placed on the tongue to convey visual

information received from a camera placed on the forehead

(as in [16]), or the transfer of a robot actor’s tactile percepts

(as detected by e.g. a laser profilometer) using abstract sounds

on the other end of the communication line. In [46], relatively

short audio signals (i.e., 2-3 seconds long) are used to convey

abstract tactile dimensions such as the softness, roughness,

stickiness and temperature of surfaces. The necessity of haptic

feedback in virtual environments cannot be underrated [47].

The type of information conveyed through sensor bridging,

the extent to which this information is abstract and the sensory

modality to which it is conveyed is open to research. As re-

searchers obtain closer estimates to the number of dimensions

each sensory modality is sensitive to, and the resolution and

speed of each modality’s information processing capabilities,

research and development in sensory substitution will surely

provide tremendous improvements to today’s engineering sys-

tems.

E. Complex example

Let us consider a scenario where a telesurgeon in location A

is communicating with a telesurgical robot in remote location

B, and another telesurgeon in remote location C. At the same

time, let us imagine that the other telesurgeon (in location C)

is communicating with a different telesurgical robot, also in

remote location B (in much the same way as a surgical assis-

tant would perform a different task on the same patient), and

the first telesurgeon in location A (figure 3). In this case, both

teleoperators are involved in one channel of inter-cognitive and

one channel of intra-cognitive communication. Within these

two modes, examples of sensor sharing and sensor bridging

might occur at the same time. Each telesurgeon may see a

camera view of the robot they are controlling, feel the limits

of their motions through direct force feedback, and hear the

soft, but whining sound of the operating robot through direct

sound transfer. These are all examples of sensor-sharing inter-

cognitive communication. The transmission of the operated

patient’s blood pressure and heart rate are also examples of

sensor-sharing inter-cognitive communication (they are inter-

cognitive, because the transmission of information is effected

through the communication links with the robot, and they

are sensor-sharing, because they are presented in the same

graphical form in which blood pressure and heart rhythm

information are normally displayed). At the same time, infor-

mation from various sensors on the telesurgical robot might be

transmitted to a different sensory modality of the teleoperator

(e.g., information from moisture sensors using pressure applied

to the arm, etc.), which would serve to augment the telesur-

geon’s cognitive awareness of the remote environment, and

can be considered as sensor-bridging communication resulting

in an augmented form of telepresence. Through the intra-

cognitive mode of communication, the two teleoperators can

obtain information on each other’s psychological state and

environment. Here we can also imagine both distance and

sensor-bridging types of communication, all of which can

directly or indirectly help raise each telesurgeon’s attention

to possible problems or abnormalities the other telesurgeon is

experiencing.

V. ACKNOWLEDGEMENT

The research was supported by the HUNOROB project

(HU0045), a grant from Iceland, Liechtenstein and Norway

through the EEA Financial Mechanism and the Hungarian Na-

tional Development Agency, as well as the ETOCOM project

(TAMOP-4.2.2-08/1/KMR-2008-0007), coordinated by BME

TMIT and MTA SZTAKI through the Hungarian National

Development Agency in the framework of the Social Renewal

Operative Programme supported by the EU and co-financed

by the European Social Fund. We would also like to thank

Prof. Gyula Sallai for his invaluable scientific advice.

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