Towards Interactive Cognitive Agents with Culturally Restricted

Behaviors

Luis-Felipe Rodr

ıguez

, Luis A. Castro

and Omar Gonzalez-Padilla

Dept. of Computing and Design, Sonora Institute of Technology, Cd. Obreg

on, Sonora, Mexico

Catrina Labs., Guadalajara, Jalisco, Mexico

Keywords:

Culture, Interactive Cognitive Agent, Cognitive Architecture.

Abstract:

Culture is one of the most important factors that inﬂuence human interaction. Evidence shows that people’s

culture determines the type of actions they are able to perform in certain situations. Consequently, in the ﬁeld

of HCI, the study of culture has become essential for the design of interactive cognitive agents embedded in

social and realistic virtual environments. However, current attempts to enculturate such agents focus on mod-

eling speciﬁc aspects of culture, leaving aside the implementation of more integrative and biologically inspired

models. In this paper, we propose an integrative framework as a novel approach to the modeling of culture

in cognitive agents for HCI applications. This integrative framework is designed to serve as the underlying

architecture of interactive cognitive agents whose behavior is inﬂuenced by speciﬁc cultural backgrounds. The

architectural design and operations of such integrative framework is based theories and models formulated in

psychology and neuroscience.

1 INTRODUCTION

People belonging to different societies perceive and

respond to particular events in contrasting ways. The

cultural differences among societies and the increas-

ing facility of people to communicate and move

around the world makes cultural conﬂicts and mis-

understandings an everyday issue. Related literature

shows how diverse disciplines address many of these

types of conﬂicts from different perspectives, trying

to develop models and strategies to minimize the im-

pact of cultural differences on the interactions be-

tween humans or between humans and machines.

Conﬂicts caused by cultural differences highlight

the importance of understanding culture (Lee and

Nass, 1998). Certainly, this would enable the de-

sign of environments for human-human and human-

machine interactions that reduce the impacts of cul-

tural differences on intercultural virtual and physical

encounters. In this line, some observable causes can

be identiﬁed, including the ambiguity in understand-

ing verbal and non-verbal expressions of people from

different societies, the differences in their attitudes to-

ward same things, the variability in the intensity and

type of emotions they express when experimenting

determined events, and the diversity in the type of ac-

tions they perform as reactions to same situations.

Culture related issues can be addressed in vari-

ous ways. For example, interactive cognitive agents

whose behavior is restricted by a cultural background

may be useful in diverse multicultural scenarios.

Cultural Cognitive Agents (CCAs) are autonomous

agents whose underlying architecture includes a num-

ber of components embodying cognitive and affective

capabilities, which enable the emergence of culture-

driven behaviors. In the case of the problematic inter-

actions between students and tutors with different cul-

tural background, CCAs might play the role of a vir-

tual tutor that embodies certain culture traits accord-

ing to the culture of students, allowing thus a more

convenient and conducive environment for learning.

Research focused on the analysis and measure-

ment of how important is for an intelligent system to

recognize the user’s culture and behave accordingly

has shown that CCAs may signiﬁcantly improve the

performance of HCI systems. For example, CCAs

may be able to respond to humans as they expect,

given that the agent is aware of the traditions, beliefs,

and other aspects inherent to the user’s cultural back-

ground (Payr and Trappl, 2004). Furthermore, such

research has led to the creation of a number of models

for enculturating agents (Rehm et al., 2007), which

are usually focused on the simulation of speciﬁc as-

pects of culture and are supported by the results of

RodrÃ guez L., Castro L. and Gonzalez-Padilla O.

Towards Interactive Cognitive Agents with Culturally Restricted Behaviors.

DOI: 10.5220/0006583502060211

In Proceedings of the International Conference on Computer-Human Interaction Research and Applications (CHIRA 2017), pages 206-211

ISBN: 978-989-758-267-7

empirical observations and social-psychological the-

ories (Rehm et al., 2007). However, a general and

more integrative model of culture for cognitive agents

based on biological ﬁndings has not been addressed.

In this paper, we discuss theoretical and computa-

tional models of culture in order to identify key as-

pects to be included in the underlying architecture of

interactive cognitive agents whose behavior is cul-

turally restricted. We present an integrative cogni-

tive framework designed to include components that

model the various identiﬁed aspects of culture as well

as other cognitive and affective mechanisms associ-

ated with the culture construct.

2 RELATED WORK

We review some of the most inﬂuential theories in the

development of computational models of culture.

2.1 Theoretical Models

The development of computational models of culture

has been mainly inspired by theoretical ﬁndings origi-

nated in areas such as social sciences and psychology.

One of the most inﬂuential theories is the dimensional

model of Hofstede (Hofstede, 1991). This is a so-

cial model developed on the basis of data from a sur-

vey conducted among employees at IBM and students

from 23 countries. The main hypothesis of Hofstede’s

theory is that people’s culture can be represented as a

point in a ﬁve-dimensional space:

1. Power distance: level of people’s acceptance in

the distribution of power among members of or-

ganizations, whether equitable or inequitable.

2. Individualism-collectivism: degree to which peo-

ple act as members of a group or as individuals

seeking personal achievement.

3. Masculinity-femininity: emphasizes on gender eq-

uity, and women’s and men’s values.

4. Uncertainty avoidance index: level of tolerance of

people to face uncertainty and thus change or not

their way of living.

5. Long-term Orientation versus short-term Orien-

tation: degree to which people are interested in

the future, in the present and the past.

Other computational models of culture have also

been developed on the basis of the cultural schema

theory (Nishida, 1999). This is a cognitive-based ap-

proach that tries to explain how culture is learned and

represented in the human’s brain. This theory charac-

terizes culture as an organized and structured network

of knowledge or schemas, which are supposed to be

retrieved in cultural situations in order to provide a

person with the appropriate knowledge to understand

the current scene, create expectations, and respond ac-

cordingly (Nishida, 1999).

2.2 Computational Models

A variety of computational models have been pro-

posed to develop Autonomous Agents (AAs) whose

behavior reﬂects certain cultural traits. For example,

Jan et al. (Jan et al., 2007) propose a model for devel-

oping conversational AAs that incorporate a speciﬁc

cultural background. This proposal focuses on the

modeling of cultural differences in proxemics, gaze,

and overlap in turn taking in North American, Mexi-

can, and Arabic cultures. Jan et al. use data derived

from empirical observations reported in related litera-

ture to assign proper values to each these three aspects

in conversational agents embodying a determined cul-

ture. In the underlying architecture of these agents,

proxemics is represented by the beliefs of the agent’s

relationship with other agents, gaze is modeled using

a probabilistic schema that allows moving between

ﬁve predeﬁned gaze states, and overlaps in turn tak-

ing is implemented by using a gaussian distribution in

which mean and variation reﬂect cultural traits.

Rehm et al. (Rehm et al., 2007) propose a compu-

tational model of culture based on Hofstede dimen-

sions. In this model the recognition of user’s cultural

background is used to adjust the behavior of AAs.

Agents interact with users by embodying their same

cultural background, which is matched to one of the

following prototypes: Arab, Chinese, Germanic, Is-

raeli, Japanese, Swedish, Thai, and North American.

Five steps are implemented to achieve such proce-

dure. Behavior observation: a Wii remote controller

is used to collect user’s non-verbal information by

sensing their movements in a 3D space. Appraisal:

the data collected from users (spatial extent, speed

and power factors) are analyzed to match one of the

eight culture prototypes. Mode: the user’s culture is

linked to the agent’s culture; in this case, the agent

adopts the same culture of the user. Simulation: the

agent’s non-verbal behaviors such as sound, spatial

extension, speed, and power are calculated according

to the designated culture. Behavior display: the agent

displays accurate culture-driven behaviors. In a more

recent publication, Rehm et al. (Rehm et al., 2009)

describe a similar model of culture in which the be-

haviors developed by AAs imitate the actions of Ger-

man and Japanese individuals identiﬁed in video clips

recorded by the authors. Three scenarios were estab-

lished for such videos: meeting someone for the ﬁrst

time, negotiations, and interactions with people with

a higher social status.

A computational model of culture based on the

schema theory and the appraisal theory of emotions

(which explains the elicitation and differentiation of

emotions on the basis of the relationship between

individuals and their environment) is proposed by

Taylor and Sims (Taylor and Sims, 2009), which

aims to develop interactive 3D cultural characters for

cross-cultural training. The authors propose a gen-

eral framework called the Cultural Cogntive Archi-

tecture, which makes use of particular schemas of

knowledge to elicit culture-driven behaviors. In this

model, cultural schemas contain data that is used for

the establishment of adequate sequences of behaviors

that cultural characteres must develop or expect in de-

termined situations. Moreover, appraisal processes

provide mechanisms for assessing cultural events un-

der certain appraisal dimensions, such as goal con-

duciveness and compatibility with internal and exter-

nal standards. This process is restricted by cultural

schemas leading to emotional data useful for generat-

ing coherent agent’s behaviors.

In summary, most of the current attempts to model

culture traits in AAs focus on the generation of certain

cultural behaviors, leaving aside the creation of more

general models which allow AAs to implement any

behavior on the basis of their cultural background.

For example, the model proposed by Jan et al. (Jan

et al., 2007) focuses on modeling proxemics, gaze,

and overlap in turn taking, neglecting aspects such as

the utility of goals an emotions. Furthermore, such

proposals are not concerned with developing models

of culture that allow the integration of new cultural

behaviors or cultural traits in AAs.

3 CHARACTERIZING CULTURE

The word culture may refer to very different fea-

tures depending on the context, ranging from super-

ﬁcial manifestations such as clothes and language,

to deeper mental models inﬂuencing human aspects

such as socialization and needs (Hofstede, 1991).

However, what do all these features have in common?,

which are the main features of culture? This section

discusses the common features of culture.

Culture is acquired. People learn cultural values

by interacting with other individuals and the environ-

ment. Tylor (Tylor, 1874) states that culture is a com-

plex whole including capabilities and habits acquired

by an individual as a member of a society. Keesing

(Keesing, 1974), Vatrapu and Suthers (Vatrapu and

Suthers, 2007) and ScHall (Schall, 2010) prioritize

the inﬂuence of the context in culture. Hofstede (Hof-

stede, 1991) deﬁnes culture as a collective program-

ming of the mind that distinguishes the members of

one group from another.

Culture is collective. Individuals belonging to the

same groups share similar cultural values. Hofst-

ede (Hofstede, 1991) afﬁrms that each culture is con-

stituted by groups such as those formed in family,

school, religion, and work, which are considered as

subcultures that inﬂuence the behavior of individu-

als in different ways. Vatrapu and Suthers (Vatrapu

and Suthers, 2007) state that culture is shared knowl-

edge, created by a group in order to rule the way in

which people perceive, interpret, and express the re-

ality around them. Straub et al. (Straub et al., 2002)

state that culture is shared by individuals which share

the same space and engage in similar activities.

Culture is subjective. The perception of an indi-

vidual about the context depends on his own cultural

values. Researchers studying culture issues assert that

the ideas and conceptions of people are true only so

far as their civilization goes (Kitayama and Cohen,

2010). Sumner (Sumner, 2013) developed the term

ethnocentrism, which states that the individual’s own

culture is particularly important, and that other cul-

tures are measured in relation to the one’s own. Her-

skovits (Herskovits, 1972) afﬁrms that judgments are

based on experience, which is strongly inﬂuenced by

the culture of individuals.

Culture is dynamic. Culture evolves over time in

order to adapt to external inﬂuences. According to

Keesing (Keesing, 1974), the technological develop-

ment and economic, politic, and social changes play

a main role in culture evolution. Hofstede (Hofst-

ede, 1991) and ScHall (Schall, 2010) afﬁrm that when

members from different cultures meet and interact,

they are mutually inﬂuenced, producing changes in

their respective cultures. In addition, Scherer and

Brosch (Scherer and Brosch, 2009) consider that own

beliefs and thoughts change over time, producing an

evolution in culture. Hofstede (Hofstede, 1991) states

that culture is changeable as members of groups are

exposed to external inﬂuences.

Culture inﬂuences behaviors, emotions, and cog-

nition. Many studies about culture are based on ob-

servable behaviors of individuals. Hofstede (Hof-

stede, 1991) describes culture in terms of ﬁve cul-

tural dimensions, each of them explained in terms of

attachment to stereotypical behaviors. Trompenaars

(Trompenaars and Hampden-Turner, 1998) studies

culture in terms of the way in which people solve

problems. Hall (Hall, 1966) investigates the inﬂu-

ence of culture in physical distances and gazes while

people communicate. ScHall (Schall, 2010) identiﬁes

cultural differences as the main inﬂuence in the way

in which people obtain, interpret, and share informa-

tion. Regarding emotions, several studies state that

culture represents a high inﬂuence in the way in which

people manage and express emotions; Trompenaars

(Trompenaars and Hampden-Turner, 1998) identiﬁes

certain patterns of behavior deﬁning whether express-

ing emotions is acceptable or not in a given culture.

Culture is personal and unique. Despite of con-

sidering culture as a collective phenomenon, there are

personal traits that produce variations in the cultural

background of individuals in a same culture. Hofstede

(Hofstede, 1991) recognizes the mutual inﬂuence of

personality and culture, which produces unique cul-

tural conﬁgurations in each individual. According to

Hall (Hall, 1966), each individual develops his main

culture, maintaining a continuous process to adapt it

according to the contexts in which he is involved in

the course of his life.

4 MODELING CULTURE IN CCAs

In the previous section we identiﬁed some key char-

acteristics of culture. The identiﬁed set of features

shows that a synthetic model of culture may become

as complex as needed, and that there will always exist

a trade-off between complexity and realism. We pro-

pose interactive cognitive agents as a good approxi-

mation for facing such trade-off. In this section we

brieﬂy discuss some cognitive capabilities needed in

CCAs in order to illustrate the complexity of model-

ing culture. We then introduce a cognitive integrative

framework that aims to serve as the underlying archi-

tecture of CCAs, which provides a favorable environ-

ment for modeling the different aspects of culture.

4.1 Cognitive Capabilities for CCAs

Cognitive architectures of AAs are regarded as in-

tegrative frameworks aimed at unifying a number

of heterogeneous components whose interaction pro-

duce cognitive behavior. Often, these components

represent computational models of cognition and

emotion and implement mechanisms to reproduce the

behavior of processes such as perception, reasoning,

and decision making. The main assumption is that

from the joint operation of this type of process will

stem believable and intelligent behavior in cognitive

agents, which are software components that proac-

tively act in order to reach some objectives, based on

knowledge, skills, and the information retrieved from

the context. In order to show such kind of behavior,

cognitive agents have the following capabilities:

• Autonomous execution and proactive behavior.

Agents have knowledge about their goals and au-

tomatically perform actions for achieving them.

• Representation and storage of knowledge. Agents

can maintain a repository of structured knowledge

(e.g., using an ontology, trees and rules).

• Reasoning capabilities. Agents implement infer-

ence mechanisms to produce new knowledge by

reasoning over their knowledge base.

• Perception of the environment. Agents perceive

information of the context in order to update their

knowledge and therefore adapt their behavior.

• Learning from experience. Agents evaluate the re-

sults of performing certain actions under certain

situations. They are able to adapt their actions in

order to improve their performance.

• Communication and cooperation with other

agents. Agents are able to communicate in order

to coordinate their activities and achieve global

goals.

The main reason of proposing cognitive agents as

the underlying technology for developing a synthetic

model of culture is that they present many capabili-

ties which simulate to some extent the cognitive ca-

pabilities of human beings. As discussed in Section

3, culture involves a series of aspects that CCAs must

model in order to capture the complexity of cultural

behaviors. The acquisition of a cultural conﬁgura-

tion by the agent requires the capacity for represen-

tation and storage of Knowledge. Knowledge bases

are a convenient approach for such requirement given

that they are attached to structures like ontologies and

desicion trees which allow computations and infer-

ences. For example, ontologies are suitable for rep-

resenting models such as those proposed by Hofstede

(Hofstede, 1991), Hall (Hall, 1966), and Trompenaars

(Trompenaars and Hampden-Turner, 1998), which

consist of a set of independent variables that describe

cultural preferences in people. In order to model such

acquisition process, CCAs require to take information

from the environment and construct a cultural conﬁg-

uration from such information. Two main capabilities

are involved: communication and perception. While

the former allows the agent to capture information

about cultural conﬁgurations of other agents and com-

pute an inﬂuence over its own cultural conﬁgurations,

the latter enables the agent to take information from

symbols in the environment.

CCAs must be assigned a speciﬁc cultural conﬁg-

uration, which is stored in their knowledge bases. The

structure of such the knowledge bases depends on the

needs of the application. For example, if the applica-

Figure 1: Integrative approach for modeling culture in cog-

nitive agents.

tion is based on the model of Hofstede, such conﬁgu-

ration can be represented as a set of values represent-

ing the cultural dimension of the model. Furthermore,

CCAs must be able to tune their culture by perceiv-

ing their environment and learning from the actions of

humans and other agents. For example, a CCA whose

objective is to model the culture of a user could obtain

an initial cultural conﬁguration based on the demo-

graphical information of the user (e.g., its nationality,

age, and sex) and personalize such conﬁguration as

the agent learns from the actions of the user.

A CCA must embody cognitive processes to be

able to combine and make operations over different

cultural conﬁgurations in order to represent the mix-

ing of cultures of different groups. Given that cultural

conﬁgurations are stored in knowledge bases, mixing

operations are performed by reasoning and making in-

ferences over such knowledge. For example, consider

an application aimed at supporting collaboration of

users with the same nationality. It is agreed that cul-

ture of individuals with the same nationality may be

quite diverse depending on their age, sex, social po-

sition, and interests. In such case, cultural conﬁgura-

tions of different CCAs, would be based on the same

initial cultural conﬁguration (i.e. the national culture)

and each of them will be adapted by computing the

impact of different groups to which the user belongs.

In applications involving several CCAs, each of them

must maintain a knowledge base of his own cultural

conﬁguration, but also an own perception of the cul-

tural conﬁguration of other CCAs.

4.2 Cognitive Architecture for CCAs

The analysis in the previous section reveals that cul-

ture is a human phenomenon that results from the op-

eration of several cognitive processes. In this line,

a computational model of culture must implement

several mechanisms associated to various cognitive

and affective processes as well as psychological con-

structs. We present an integrative cognitive frame-

work that aims to provide a favorable environment for

the uniﬁcation of several computational models im-

plementing diverse cognitive and affective processes

associated to culture, such as decision making, learn-

ing, and personality (see Figure 1).

As Figure 1 shows, this integrative framework rep-

resents the underlying architecture of the CCA (level

1 in ﬁgure), which enables the emergence of cultur-

ally restricted behaviors. The architectural design

and operational assumptions of such agent architec-

ture are based on concepts, theories, and models for-

mulated in disciplines devoted to investigate human

behavior and its relation to the information process-

ing in the brain, such as psychology and neuroscience.

On the one hand, psychology offers high-level ex-

planations of the brain processes underlying human

behavior (including cultural behavior), enabling the

understanding of their main functions, their working

assumptions, and their interactions with other brain

processes. On the other hand, neuroscience pro-

vides theories and models that investigate the cog-

nitive and affective processes involved in culture in

terms of brain functions, brain structures, and neural

pathways, which is useful to understand in more detail

the internal procedures necessary for the modeling of

cultural behavior.

As explained above, culture is a complex phe-

nomenon that is subject of study in various disci-

plines. This psychological construct is being ad-

dressed at different levels of abstraction and from dif-

ferent perspectives. As a result, several theories and

models explaining culture in individuals and groups

have been formulated. Hence, in order to keep up with

such advances and develop a computational model of

culture able to capture new evidence, the cognitive ar-

chitecture of a CCA should meet the following two

requirements:

1. Integrative and scalable architecture: CCAs have

to be designed so that they incorporate frame-

works that consistently unify theories and mod-

els that explain diverse aspects of culture, and

other cognitive and affective aspects as well. Ad-

ditionally, such architectures should provide suit-

able environments for the steadily incorporation

of new ﬁndings about this human phenomenon.

2. Consistent model for the interaction of various

cognitive and affective processes: since cultural

behaviors are the result of the operation of a se-

ries of cognitive processes, computational models

of culture must embody proper interfaces for the

interaction of the affective and cognitive functions

underlying culture.

The framework introduced above is based on evi-

dence from psychology and neuroscience, which sim-

pliﬁes the achievement of these two requirements. As

shown in Figure 1, our proposed framework includes

a series of computational models of cognitive and

affective processes that represent psychological con-

structs or brain functions (level 2 in Figure), such as

personality, learning, and culture. The interaction be-

tween these components enables the dynamics of cul-

tural behaviors in CCAs. Furthermore, they represent

abstract models whose behavior emerges from the op-

eration of a number of architectural components (level

3 in Figure), which simulate the functionality and ar-

chitecture of brain structures.

In this manner, such proposed cognitive frame-

work allows us to implement an integrative and scal-

able computational model of culture, since any avail-

able or new neuroscientiﬁc theory addressing some

brain function or psychological construct can be im-

plemented by using the structural and operational

basis within the agent architecture. Similarly, this

framework meets the second requirement since com-

putational models of cognitive and affective functions

are implemented using the same or compatible struc-

tural and operational machinery used to implement

the model of culture.

5 CONCLUDING REMARKS

In this paper we identiﬁed key characteristics to

be modeled in interactive cognitive agents aimed at

addressing the complex requirements of contempo-

rary applications in HCI. We presented an integra-

tive framework as the underlying architecture of such

interactive cognitive agents that attempts to integrate

the various components of culture. Considering that

the current state of knowledge of most aspects related

to culture in individuals is still limited, but in de-

velopment, this integrative approach becomes conve-

nient for the computational modeling of culture. Ad-

ditionally, neuroscience offers theoretical models of

the processes that underlie human behavior and which

are common to all individuals, allowing the model-

ing of the process of culture in autonomous agents

through the implementation of the basic mechanisms

from which all kind of cultural behaviors emerge.

Thus, instead of dealing with the question of why and

how culture is different among people and societies,

we focus on the synthesis of the brain mechanisms

that underlie the development of cultural aspects in

humans.

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