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Computational Modelling of Culture and Affect

Journal: Emotion Review

Manuscript ID: Draft

Manuscript Type: Original Manuscript

Date Submitted by the Author:

n/a

Complete List of Authors: Aylett, Ruth; Heriot-Watt University, MACS Paiva, Ana; INESC-ID, GAIPS

Area/Discipline: affective computing < computer science, computational models < computer science

Keywords: Synthetic graphical characters, Affective models, Models of culture

Abstract:

This paper discusses work in implementing emotional and cultural models into synthetic graphical characters. An architecture, FAtiMA, implemented first in the anti-bullying application FearNot! and then extended as FAtiMA-PSI in the cultural-sensitivity application ORIENT, is discussed. We discuss the modelling relationships

between culture, social interaction and cognitive appraisal. Integrating a lower-level homeostatically-based model is also considered as a means of handling some of the limitations of a purely symbolic approach. Evaluation to date is summarised and future directions discussed.

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COMPUTATIONAL MODELLING OF CULTURE AND AFFECT 1

Running head: RUNNING HEAD FOR PUBLICATION GOES HERE

Computational Modelling of Culture and Affect

Ruth Aylett

MACs, Heriot-Watt University

Ana Paiva

INESC-ID, Lisbon

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Abstract

This paper discusses work in implementing emotional and cultural models into synthetic

graphical characters. An architecture, FAtiMA, implemented first in the anti-bullying application

FearNot! and then extended as FAtiMA-PSI in the cultural-sensitivity application ORIENT, is

discussed. We discuss the modelling relationships between culture, social interaction and

cognitive appraisal. Integrating a lower-level homeostatically-based model is also considered as

a means of handling some of the limitations of a purely symbolic approach. Evaluation to date is

summarised and future directions discussed.

KEYWORDS: Synthetic graphical characters; Affective models; Models of culture;

Computational modelling

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INTRODUCTION

This paper presents work on the modelling of emotion and culture and their interrelationship

from a computational perspective. In order to implement any model on a computer, the model

itself must be sufficiently specific. From this perspective, many psychological models are not

usable as they stand, but must be operationalised. Qualitative relationships must be quantified,

alternatives must be selected from, internal structures must be mapped onto software entities.

Those that cannot may be omitted altogether. Often this produces a simplified version of the

original theory, not always appreciated in the originating disciplines.

Thus, when computer scientists select models from psychology, they tend to favour those

that are already sufficiently specific or that can be made so relatively easily. It is for this reason

that the cognitive appraisal-based approaches discussed later have often used what is often

referred to as the OCC approach (after Ortony, Clore and Collins, 1988) in spite of the

availability of more recent and much more sophisticated models (Scherer, Schorr, Johnstone

2001). In the same way, the cultural dimensions model of Hofstede (Hofstede 2001), also

selected here, appeals because of its systematic and easily operationalised taxonomy.

One motive for the computer-based modelling of emotion and culture indeed lies in the

pressure that implementation puts on the original theory. This may tease out ambiguities or lack

of precision not initially visible to the constructors of the theory. Increasingly though,

computational models are being built for specific applications, often those in which graphical

characters perform as autonomous intelligent agents or synthetic characters. These may interact

with each other in graphical worlds (Marsella, Johnson, Labore 00; Paiva, Dias, Aylett, Woods,

Hall, Zoll 2005), or with human users (Cassell 2001, Narita & Yusihiko 2010). It is this

application domain that has prompted the work we report.

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Affect has been a part of work in synthetic characters for nearly twenty years (Elliot

1992). Work in the OZ project in the 1990s identified emotion as a key aspect of creating what is

known as believability for synthetic characters (Bates 1994). This term has become a touchstone

for research in synthetic characters.

It had become clear that users interacting with graphically represented characters as well

as robots would often treat them as if they were human interaction partners even though they

knew that they were not (Reeves & Nass 1996). This included applying the intentional stance

(Dennett 1989) to them, and acting as if they had their own inner life: motives, goals, beliefs, and

feelings. Believability may be taken as the extent to which human users are willing to suspend

their disbelief in an artificial computer-based synthetic character.

Affect relates to believability in synthetic characters in two main ways. The first is

through the requirement for contextually appropriate expressive behaviour such as glance, facial

expression, posture, and gesture. The impact of a synthetic character acting as a humanised

interface depends on expressive behaviour that is consistent and appropriate. Producing

expressive behaviour involves sending the right mark-up commands to a graphics engine.

However, synthetic characters that are responsive to user interaction in real-time must generate

such mark-up ‘on the fly’ rather than through pre-determined scripting. Generating expressive

behaviour in this way requires affective models linking inputs from the user, and from other

synthetic characters, to contextually appropriate outputs

The second requirement for affective models is a generalisation of the first. Synthetic

characters that are responsive in real-time must in fact be autonomous, that is, able to select their

actions in relation to real-time input, in the same way as an autonomous robot. It has become

clear from work on action-selection in the human case (Damasio 1994) that affect and cognition

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do not form two separate systems but are intimately linked. In the case of synthetic characters

this requires an affective model that generates overall motivations (Canamero 98, Valasquez 97),

helps to select which goals to pursue at a particular time, and allows alternative plans to be

weighed (Aylett, Dias, Paiva 2006). Expressive behaviour supports the intentional stance

precisely because it acts as a window for the user into the decision-making process of the

character, supporting the decoding of motive and intention referred to above. Thus the user will

track the supposed motives, goals and plans of the character, and if these are not appropriate then

they will not be believable either.

FATIMA – AN AFFECTIVE ARCHITECTURE

In this section we discuss the development of a first version of an affective architecture for

synthetic characters as part of work on the application FearNot! (Fun with Empathic Agents

Reaching Novel Outcomes in Teaching). This version of FAtiMA (FearNot! AffecTIve Mind

Architecture) (Dias & Paiva 2005) modelled an integration of affect and cognition for

autonomous characters but did not specifically model culture. In the following section we

describe extensions to FAtiMA for the application ORIENT (Overcoming Refugee Integration

with Empathic Novel Technology) intended to incorporate an explicit model of culture.

Empathic Agents

In the work reported here, synthetic characters were developed for educational applications in

which the objective was not to produce greater knowledge per se, but to impact attitudes, and as

a result, change behaviour. FearNot! focused on education against bullying (Paiva et al 2005),

and ORIENT on education in intercultural empathy (Aylett, Vannini, Andre, Paiva, Enz, Hall

2009). In both cases, creating empathy between the user and synthetic characters was seen as a

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key requirement for meeting the educational objectives, and thus required empathic characters,

that successfully evoke this.

A modern definition suggests that empathy is:

“any process where the attended perception of the object’s state generates a state in the

subject that is more applicable to the object’s state or situation than to the subject’s own prior

state or situation” (Preston & de Waal 2002).

One can distinguish between two types of empathy: cognitive empathy and affective

empathy. For cognitive empathy, perception of the ‘object’ (another person in this case) produces

an understanding of their affective state. In the case of affective empathy, a change in the

affective state of the subject is produced.

One can also distinguish two different mechanisms (Bischof-Köhler 1989) mediating

empathy. The first is mediation by the situation in which the object is perceived to be: for

example seeing someone have their handbag stolen may produce the cognitive empathy effect of

understanding that they are sad and angry. Empathy may also be mediated by expression, where

any element of the full range of expressive behaviour produces the empathic effect. Thus an

affective empathy reaction of sadness may be produced by seeing the target crying.

FearNot! was designed as a virtual drama in which one synthetic character was bullied by

another in a virtual school (see Figure 1). The actions of the characters were not pre-scripted but

were dynamically generated by the parameters in their affective models: anger and hate in the

case of the bully and fear and distress in the case of the victim.

***FIGURE 1 around here ***

The child user watched the interaction and after it the victimised character would ask the

them for advice about how to deal with the situation. The child user was able to input free text

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(see Figure 2). The advice given would influence the behaviour of the victimised character

indirectly by altering some of the parameters of its affective model, and this in turn would make

specific actions more likely – though not certain – in the next scene.

***Figure 2 around here ***

Including an affective model both allowed the character to display appropriate expressive

behaviour (mediation by expression) and produced the necessary dramatic actions (mediation by

situation).

Modelling cognitive appraisal and coping

There are many models of affect in the psychological literature, more than can be reviewed here.

Not all of these are readily implementable. Of those that have been implemented, some derive

from a more neuro-physiological perspective (e.g Valasquez 1997, Canamero 1998), less

relevant to a synthetic character that interacts with users using natural language. Multi-level

models and notably Russell’s model of core affect (Russell 2003) and its extension by Mehrabian

(1996) have been widely implemented, but while the concepts of pleasure, arousal and

dominance are influential, and can be used to generate expressive behaviour, on their own they

provide no link between an event in the world, an affective response and a resulting action.

The required action repertoire of a FearNot! character included ‘physical’ actions such as

one character pushing another, or movement in the graphical 3D scene, as well as dialogue or

language actions such as ‘mock’ or ‘insult’. While expressive behaviour required a reactive

model – characters do not plan to cry, but may do so if they are sufficiently distressed – other

behaviour, such as actual bullying behaviour, involved planning from an initial set of goals.

Cognitive appraisal was chosen as a means of generating a modelled affective state.

Already implemented in earlier systems (Elliot 92, Marsella et al 2000, Marsella & Gratch

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2003), a cognitive appraisal mechanism can be represented as a set of symbolically-encoded

rules in software linking an event in the virtual world and the current goals of the synthetic

character to a generated affective state. The OCC model (Ortony et al 88) was selected because it

was straightforward to represent the events and goals of the FearNot! synthetic characters and

link them to the 22 affective states of the OCC taxonomy. Moreover the OCC taxonomy includes

emotions that concern behavioural standards and social relationships (like/dislike,

praiseworthiness and desirability for others) and thus it was felt that it would support appraisal

processes that take into consideration cultural and social aspects of interaction.

While in the human case, a taxonomy of emotions could be criticised as conflating

linguistic representations with much more complex underlying states and processes, from a

computational perspective, it leads to a structure representing each of the 22 OCC emotions for a

specific character as shown in Table 1.

***Insert Table 1 here ***

Here Valence corresponds to the Pleasure dimension of Russell, and Intensity can be

combined over all 22 emotions to represent his Arousal dimension. What OCC adds to this is an

account of how external events can be coupled to the generation of one of the 22 emotions,

readily implemented as a set of rules. To this static representation can be added numeric

thresholds, and decay factors, so that the level of an elicited emotion is reduced over the

succeeding time periods. By defining thresholds and decay factors differently for different

characters, it becomes possible to model different emotional dispositions (see Figure 3).

Thresholds represent a resistance to a specific emotion, so that though evoked by an appraisal, an

emotion only becomes part of the emotional state if above threshold. When emotions are linked

to actions as described below, this will produce different patterns of behaviour over time. These

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patterns can then be perceived by the user as personality, removing the need to model personality

separately from the affective model. This is an example of the modelling economy introduced by

using a cognitive appraisal approach.

**Insert Figure 3 here **

Cognitive appraisal links external events to generated emotion through emotional-

reaction rules, but not to action-selection. Reactive behaviour was modelled in FAtiMA by

taking up the idea of action tendencies, using the Lazarus (1991) view that action tendencies are

well-established biological impulses as compared to coping behaviour. Every character is

equipped with its own set of action-tendency rules, each linking a minimum level of a specific

emotion to a reactive action. For example, distress at a high minimum intensity may trigger tears,

an expressive behaviour. Action tendencies can vary across characters, so that not only might

one character experience more distress for longer (low threshold, high decay rate), this might

quickly trigger crying where some other distress behaviour might be generated for a different

character.

Coping behaviour is more likely to involve planned actions (Lazarus 1991). Problem-

based coping involves actions in the world in relation to the event that caused a strong emotion,

while emotion-based coping results in internal adjustment – for example mental disengagement

in which a goal or plan is dropped, or wishful thinking about the outcome when faced with a

threatening condition. In the computational paradigm, intelligent planning has been studied

largely as a means of sequencing actions together correctly such that they meet some goal(s)

efficiently. However efficiency is not the same thing as believability, and the standard AI

approach neglects the interaction between planning and affect (Damasio 1994). In an application

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where one character is bullying another, fear, anger and other emotions are intimately involved

with planning.

A detailed discussion of the planning process created for characters may be found

elsewhere (Aylett et al 06). It draws on the OCC prospect-based emotions, that is, emotions

relating to events or actions that have not yet taken place: hope and fear. Hope and fear may be

directly generated from the appraisal process, but through emotion-directed coping, may also be

generated from within planning itself as future actions are considered. From a computational

point of view, emotions can be considered as if they were planning heuristics, helping to control

which goals are selected for planning and which of a number of possible plans for each goals are

favoured for execution. We have already seen that the use of an affective model that includes

thresholds and decay factors, in a character subject to variable external events, can produce a

complex dynamic model. The addition of planning extends the dynamics of the model beyond

single-step action-reaction and introduces internal cognitive processes in a feedback loop with

the affective system.

Table 2, below, summarises the FAtiMA model elements specified when defining a

specific character.

**Insert Table 2 here **

There is no straightforward way of testing a model such as FAtiMA in isolation from a

particular application even though FAtiMA is a generic architecture. Actual characters, actual

events, and actual actions in a specific domain are required in order to run the model. This means

that one cannot test the generic mechanisms of cognitive appraisal and reactive and planned

coping behaviours independently of specific settings of the many variables in the model. Advice

from the psychologist members of the development team was taken, drawing on

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characterizations in the literature of the personality of bullies and victims. Variable settings were

then refined through trial and error in which values were tried and the resulting character

behaviour considered for appropriateness and believability.

FearNot! was extensively trialled in schools in the UK, Germany and Portugal. A large-

scale longitudinal evaluation (Sapouna, Wolke, Vannini, Watson, Woods, Schneider, Enz, Hall,

Andre, Dautenhahn, Aylett 2010) showed that FearNot! did have a positive impact and, as

important, that it did not produce cleverer bullies. This underlines the importance of characters

that can evoke empathy

EXTENDING FATIMA: INCLUDING MOTIVATIONS AND CULTURE

Though embodying a generic model, FAtiMA was initially developed in response to the

demands of FearNot! While there are strong cultural factors at work in human bullying

behaviour, these were incorporated in FearNot! through content and not through generic

modeling. Some different scenes/situations were authored for a German version as against a UK

version, and cosmetic changes such as characters having graphical school uniforms or not were

incorporated. Nevertheless the evaluation showed that the impact of FearNot! was indeed

different in the two different cultures in which it was tested (Watson, Vannini, Woods,

Dautenhahn, Sapouna, Enz, Schneider, Wolke, Hall, Paiva, Andre, Aylett 2010).

However, FAtiMA was next reapplied to education in intercultural empathy in the

ORIENT application. This required a generic model of culture such that characters could be

configured to behave according to the norms of different cultures.

In this section, we first consider ways in which FAtiMA as first implemented might be

modified to include a model of culture. These possibilities were inherent in the way the model

had been constructed both from a static and dynamic point of view.

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Having established that at least one of them requires much better goal management than

FAtiMA initially offered, we explain how ideas from the PSI model (Doerner 2003) were

included so that motivations could be used as a way of managing goals. We then consider a

computationally-feasible model of culture, Hofstede’s (2001) cultural dimensions. Finally, we

explain how part of Hofstede’s model has been incorporated into the extended FAtiMA needed

for the ORIENT application.

FAtiMA and extensions for modelling culture

Firstly, culture may impact the FAtiMA appraisal process linking character goals to

events or actions to generate a change in one or more emotions. Emotional reaction rules (above

in Table 2) link events or actions to values representing desirability (how far this supports or

impedes character goals) and praiseworthiness (how far it supports or impedes social norms).

Praiseworthiness and desirability values are then used to update all 22 OCC emotions for a

character. Social norms could be expressed as the praiseworthiness of specific events or actions

or as the desirability of specific social goals held by all characters in a culture.

Secondly, culture might be encoded within the network linking praiseworthiness and

desirability to emotion intensity updates, independently of specific goals or norms. This would

be to assert that a culture has a generic impact on the amount of emotional change generated

during appraisal – possibly differentially across emotions. For example a culture might a

censorious one in which everyone reacts more angrily to blameworthiness.

Thirdly, culture might impact emotional disposition or action tendencies (see Table 2) via

global modification of thresholds and decay rates. Adding a cultural increment to all thresholds

in all characters for example would reduce their emotional sensitivity to the appraisal process,

and subtracting globally would increase it and produce an increase in volatility. A global

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increment to decay rates would produce personalities that stayed in given emotional states for

longer and thus were more emotionally-driven, while subtracting would produce a set of

relatively calmer personalities. Action tendencies are expressed as rules triggered by a minimum

intensity of a specific emotion, so a cultural increment or decrement to emotion thresholds and

decay rates would itself impact the reactive behaviour of a character. However the minimum

intensities could themselves be adjusted with reference to a cultural model, affecting the

perceived impulsiveness or stolidity of characters.

Finally, relationships between culture and coping behaviour could also be modelled. For

example, culture could affect the goal selection process, in which a subset of possible goals are

fore-grounded for active planning. However in the first version of FAtiMA, pre-conditions are

hardwired in goals attached to specific characters, and goal priority is determined solely by the

amount of hope and fear generated by the planning process and the other emotions that result

from it. In the next section we discuss the addition of motivations to the model as a way of

improving goal management, and in the following section we discuss the use of Hofstede

cultural dimensions that build upon this.

Adding Motivations to FAtiMA

The cognitive appraisal approach discussed so far represents one broad paradigm, characterised

computationally by symbolic representations and explicit reasoning processes. However,

particularly in robotics, a different paradigm has often been applied, taking ideas from

neurophysiology (Valasquez 1997, Canamero 1998). This posits a small set of basic drives, some

survival-related, such that as for food, and some more general, such as that for novel

experiences.

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Drives have associated states – for instance, how much eaten recently, how many novel

experiences – whose value has upper and lower bounds relating to the overall comfort of the

agent. When a state moves outside of these bounds, the drive produces a motivation for actions

that will move it back inside its comfort range. Thus homeostasis is the primary mechanism for

the dynamics of this type of model. Affect is procedurally modelled through behaviour that can

be interpreted as affective rather than through an explicit representation as in cognitive appraisal.

There is no requirement either for symbolic representations or for explicit reasoning, with

stimulus-response mappings able to manipulate numerical sensor inputs directly.

Instead of a mutually-exclusive alternative, one can include this approach as part of a

heterogeneous model in which drives, and the ensuing motivations, act as a goal-management

system for symbolically-represented goals (Aylett 2006). In this account, a motivation is a long-

term and generic reaction to a drive that activates a set of relevant goals without necessarily

choosing between them. Thus a hungry agent has a motivation to find food, but this motivation

could be met by the different goals of opening the fridge, buying a sandwich or picking wild

berries, depending on the context and the resources of the environment.

Consider a cultural reason for satisfying hunger in a specific way – say not eating the

ham sandwich in the fridge but going out to buy an egg sandwich instead. It is hard to model this

purely at the level of drives. Plausibly, culturally-mediated food preferences operate through an

affective state, so that the agent finds ham sandwiches disgusting. However, a cognitive appraisal

seems a more convincing mechanism for generating disgust when the fridge door opens. In the

same way, an affiliation drive might produce the socially interactive behaviour that allows

cultural norms to be learned and internalised, but the drive alone seems too generic to invoke

culturally-specific behaviours.

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FAtiMA-PSI

In incorporating drives and motivations into FAtiMA, we could have picked an existing

computational implementation of a neuro-physiologically inspired theory. However these were

designed to act in isolation, not to be integrated with a cognitive appraisal-based system. We

therefore examined the PSI theory of Doerner (2003) since this already integrated cognition,

emotion and motivation for human action regulation and included links to planning. The PSI

theory starts from the definition of five basic drives or needs, as seen in Table 3 below.

*** Insert Table 3 here ***

A deviation from the threshold set for a need will give rise to a motive. These feed into

an action selection process and a goal is selected for execution based on its anticipated

probability of success, the degree to which it satisfies needs, and its estimated urgency. If the

character does not have any knowledge of how to satisfy this goal, the success probability will be

low, however, if its competence is high, it will perform exploratory behaviour and may still

consider selecting the goal. A PSI agent has three strategies for dealing with a goal. First, the

agent tries to recall an automatic reaction. If this is not successful or if no such reaction exists, it

attempts to construct a plan. If both automatic reaction and planning fail, the agent resorts to

applying trial and error, a type of exploratory behaviour.

Unlike a cognitive appraisal approach, PSI has no explicit representation of emotion.

Rather, cognitive and motivational processes are modulated under different environmental

circumstances in ways that are interpretable by an observer as emotionally-modified. The three

modulating parameters are:

1. Activation or arousal: the degree of preparedness for perception and reaction. A high level of

arousal produces faster behaviour, and arousal itself increases with the overall pressure from the

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motivational system as well as in relation to the strength (urgency and importance) of the

currently active intention.

2. Resolution level: this determines the accuracy and deliberateness of cognitive processes such

as perception, planning, and action regulation. It varies inversely with arousal: when arousal is

high, an agent will put less effort into considering the consequences of its actions.

3. Selection threshold: this prevents oscillation between behaviours by giving the current active

intention priority. It varies in proportion to arousal: an agent is easily distracted from its current

intention when the threshold is low, and is highly focused when it is high.

A big advantage in linking this to the FAtiMA architecture is that PSI incorporates a

built-in learning process. By trying different goals and actions under different circumstances, the

agent will learn which goal and action is the most effective in satisfying its needs. PSI agents’

differences in behaviour will then correspond to different life-experiences that lead to different

learned associations, offering a potential not only for the modelling of personality, but also of

culturally-mediated behaviours.

As shown above in Table 2, an emotional reaction rule has to be written for each action

of each FAtiMA agent to define the praiseworthiness and the desirability (or undesirability) of

the action, both for the agent itself and for other agents. However PSI agents can derive

desirability for events automatically from needs: the better an action or goal satisfies need(s), the

more desirable it is, eliminating the hardwired emotional reaction rule sets. FAtiMA goals and

actions then require an expanded representation that includes the potential effects on needs of

carrying out the corresponding goals or actions. The same applies to the action tendency rules

also seen in Table 2. A FAtiMA agent needed a reactive rule for example run when in danger.

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For a FAtiMA-PSI agent, this action is automatic because in this case, the need for certainty

would be high and the agent would choose the run action.

The FAtiMA-PSI architecture was developed for the ORIENT application in which

meeting the pedagogical goals required a greater degree of control over agent behaviour than a

pure PSI architecture can provide. While the desirability of actions for the agent itself can be

learned using the PSI mechanisms, the use of OCC and cognitive appraisal allows an explicit

model of desirability for others and praiseworthiness as described below. More detail on this

architecture, including all the equations used to calculate internal variables, can be found in Lim,

Dias, Aylett and Paiva (2010).

A computational model of culture to support ORIENT

It is widely accepted that culture pertains to the social world and determines how groups of

people structure their lives (Bennett & Bennett 2004). Thus culture can be seen as a collective

phenomenon shared by people that live in the same social environment (Hofstede 2001). A

constructivist definition of culture (Bereger & Luckmann1966) looks at culture in two ways. The

first covers the institutional aspects of culture, such as political and economic systems, and

products of culture: works of art, music, cuisine, etc. The second, subjective culture, covers the

experience of the social reality formed by a society’s institutions, that is, the worldview of its

people (Bennett & Bennett 2004).

However in order to operationalise a model of culture, we sought work that could easily

be expressed as a set of specific rules and focused on Hofstede’s taxonomy of cultural

dimensions (Hofstede 2001). This work views culture as those patterns of thinking, feeling and

acting that are shared and learned by members of the same culture. These patterns can manifest

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in several forms: values, rituals, heroes (persons that serve as models of behaviour) and symbols

(gestures, words, pictures to which members of the culture have assigned a particular meaning).

Values are defined as “broad tendency to prefer certain states of affairs over others (Bell

1997). They transcend specific situations, guide selection or evaluation of behaviour and events,

and are ordered by relative importance (Bell 1997). Values are often unconscious to those who

hold them and as such they cannot be directly observable. Instead they can be inferred from the

way people act under certain circumstances.

Unlike values, rituals are clearly observable in cultures and are essential to social

activities - it is known that humans have been involved in ritual activities since the earliest tribal

communities. According to (Bell 1997) rituals not only regulate the relationships between people

within a community but also between people and their natural resources. In general a ritual can

be defined as a particular set of actions, often thought to have symbolic value. The performance

of a ritual is usually prescribed by a religion or by the traditions of a community. Finally,

cultures also have associated symbols, which constitute words, gestures, pictures or object with

meanings specific to that particular culture.

Based on a large-scale study of IBM employees in different countries, Hofstede added to

these patterns five dimensions across which cultures vary, and that are indications of general

behavioural tendencies. These dimensions are: Power Distance, Individualism/Collectivism,

Masculinity/Femininity, Uncertainty Avoidance and Long Term Orientation, defined in Table 4.

The importance of these dimensions is that they can be associated with manifestations of cultural

difference, thus linking cultural parameters to cultural behaviour.

*** Table 4 here ***

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Two of these dimensions were modelled in FAtiMA-PSI. The first is Power Distance,

the degree to which less powerful members of the group expect and accept that power is

distributed unequally. In low power distance cultures, power relations are usually more

consultative or democratic, whereas in high power distance cultures, people tend to accept power

relations that are more autocratic, and usually respect and acknowledge the power of others just

by their formal status. The second dimension considered was Individualism/collectivism which

looks at the relations between the individual and the group. Collectivism pertains to societies in

which people are integrated into strong, cohesive in-groups, whereas individualism pertains to

societies in which everyone is expected to look after themselves and their immediate family.

Extending FAtiMA-PSI for ORIENT

Just as FearNot! was the motivating application for the development of FAtiMA, so

ORIENT motivated extending it to deal with culture. ORIENT is an intelligent graphical-

character based system designed to enhance intercultural empathy. It attempts to take a group of

three teenage (c14 year old) users, cooperating together, through the early stages of the Bennett

(1993) model of the development of cultural sensitivity. This application is discussed in more

detail elsewhere (Aylett et al 2009, Lim et al 2010); here we consider only the graphical

characters involved, visualised as ‘aliens’ called Sprytes, on a planet called Orient.

Sprytes are somewhat humanoid, but as seen in Figure 4 below, actually modelled

visually on tree frogs. Their culture is a synthetic one, not identifiable as any specific human

culture. There were several reasons for this choice. Firstly, the application itself was to be used

in different cultural settings and for evaluation purposes it was better that the culture portrayed

be unfamiliar to all users; secondly, there was desire to avoid real-world cultural stereotypes (and

for similar reasons Sprytes were ungendered); finally, it was very clear that representing all the

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richness of a real culture was infeasible in the current state of the art. We wished to avoid a

sketch or caricature that could be quite offensive to members of a real culture.

***Figure 4 here***

It is clear that in creating an affective agent architecture, culture should be taken into

account in the modulation of affective states - both at the expression and at the generation level.

Some emotionally-based agent architectures already do consider cultural differences and have

explored this issue for concrete applications.

CUBE-G (Rehm et al 2006) is an interesting project that also uses Hofstede's cultural

dimensions (Hofstede 2001) for modelling nonverbal communication aspects of different real-

world cultures. Agents in CUBE-G establish conversations between themselves and users, and in

those interactions the cultural background of a user is inferred by sensing their nonverbal

behaviour while using a Nintendo Wiimote controller. The nonverbal behaviour of the agents is

then dynamically adapted according to the culture inferred by the system. The Culturally

Affected Behaviour (CAB) model (Solomon 2008) takes a different approach, allowing the

encoding of specific ethnographic data on cultural norms, biases and stereotypes. These are used

to modulate the behaviour of agents. However, neither of these systems considers how culture

might influence emotional processes, a requirement for the creation of intelligent agents for

ORIENT.

Our own approach allows the cultural dimensions of an agent society to be explicitly

represented through individual culturally-specific behaviours. This in turn supports the

emergence of collective behaviours for a society of agents. Our aim in extending FAtiMA-PSI

was to parameterise the two Hofstede cultural dimensions mentioned, so that different settings of

these parameters changed the cognitive-affective processes of FAtiMA-PSI, modelling the

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impact of a specific culture. The elements in our model affected were the appraisal process,

appraisal variables and goal selection. A set of values for these parameters are specified for the

symbols, cultural dimensions and culturally-specific rituals for a particular culture, and all the

characters that are part of that culture inherit these settings.

Symbols, Rituals and Appraisal in FAtiMA-PSI

When an event is perceived by the agent it now passes through a Symbol translator to

obtain the meaning of that event according to the particular agent’s culture (for instance a

waving hand may be considered a greeting in one culture but insulting in another). The perceived

event is then used to update the agent's Knowledge Base (containing its knowledge of the world),

its Autobiographic Memory (containing events organised as episodes) and the motivational state

discussed above.

Some events affect the agent’s motivational state: for example if the agent finished an

eating action, its “need” for energy should go down. However, if the agent sees another agent

finishing eating, then it should also capture that event, and then predict the other agent’s level of

energy. FAtiMA-PSI includes a mechanism to model other agents and their relationship to the

individual agent, which is able to build and update a record of the motivational state of other

agents according to events perceived. This information is used later in the cultural goal selection

and cultural appraisal processes.

After updating its motivational states, the event is finally appraised by the agent. A

“Cultural Appraisal” process was integrated into the reactive appraisal component of FAtiMA.

This process is based on the idea that the appraisal variable Praiseworthiness (using the OCC

appraisal variables) is culturally dependent. Indeed, as stated in (Ortony et al 88), events with a

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positive praiseworthiness will potentially cause the character to feel pride or admiration, and a

negative praiseworthiness result will potentially cause the character to feel shame or reproach.

According to Markus and Kitayama (1991), people in an individualist culture appraise

events in terms of their individual achievements and properties whereas collectivists appraise

events in terms of the group the person belongs to or the interpersonal relationships of the group.

Collectivist cultures therefore try to avoid conflicts that would disrupt the harmony of the group.

The extensions to FAtiMA-PSI incorporate a cultural appraisal process where the

praiseworthiness variable calculated in appraising an event depends on the agent who caused the

event and the impact that it has on the other characters. This means praiseworthiness is

calculated differently for behaviours that involve others depending on the degree of

individualism or collectivism.

Thus, the more collectivist a culture is, the more an event that is undesirable for others

but beneficial for the responsible character will be blameworthy (for example, stealing

something). In addition, the more an event is good for others, even if it is bad for the responsible

character, the more praiseworthy it will be (e.g. giving food). By taking into account the benefits

that an event has for the self (the agent) and for others (as modelled by the agent) according to

the culture parametrisation, the agent reacts differently.

If Ia(e) is the impact of an event on the agent that causes it, and if Io(e) is the sum of the

impacts on other agents according to the first agent’s models of them, and IDV is the Hofstede

degree of individualism (0-100; 0 more collectivist) then the praiseworthiness P(e) of an event e

can be calculated as:

P(e) = {0 (if Ia(e) > Io(e) >= 0); (Io(e)- Ia(e) ) x (100 – IDV)/100)}

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The link between culture and agent behaviour also impacts the agent’s goal selection. In

cognitive architectures, goal utility is a value relating to how useful a goal is to the agent. For

example, if an agent has the goal of drinking some water then that goal’s utility rises as the agent

becomes thirstier.

A Cultural Goal Selection process was added that also calculates the expected cultural

utility for each active goal. This is the expected impact the goal will have on the agent's own

motivational state and on the motivational state of the goal's target (determined using the

representation of that other agent). This allows the modelling of individualistic agents that are

primarily concerned with themselves, and only for another agent if they have a strong

interpersonal attraction (symbolizing a close bond) with them.

Collectivistic characters are however equally concerned with themselves and with others

and treat everyone alike (regardless of social bonds). The details of how goal utility is calculated

can be seen in (Mascarenhas, Dias, Prada & Paiva 2010). This link between culture and goal

utility also allows us to capture the “power distance” dimension. It can be applied so that

characters belonging to a high power culture favour goals that positively affect others with a

higher status than themselves (Mascarenhas et al 2010) by giving a higher utility to such goals.

The outcome of the cultural evaluation of goal utility is that different goals will be

selected in the same situation by an agent depending on its cultural parametrisation. As a result,

different actions will be carried out. For example, consider a scenario in which a sick character

reports their sickness to some other characters. Using the mechanism just discussed, if the culture

is highly individualistic, a character that has medicine but is not a friend of the first character,

will criticize them for complaining. Conversely, if the culture is highly collectivistic, the same

character will promptly offer their medicine to help (Mascarenhas et al 2010).

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Evaluating the extended model

As with the initial FAtiMA model, the culturally-extended version can only be evaluated

in the context of a specific application – in this case ORIENT - with actual settings for its large

number of parameters. The same combination of expert advice followed by trial-and-error of the

selected settings was followed in ORIENT as in FearNot! When character behaviour was

compliant in the view of the development team, ORIENT was evaluated with groups of users.

Evaluation of a complex model embedded in a specific application is not an all-or-

nothing affair but involves a set of evaluation metrics. One can pose questions relating to user

perceptions of the characters in relation to themselves in the storyworld context, both in relation

to a very unfamiliar cultural context and to the overall interaction experience. One can also

evaluate whether the cultural model just discussed produces perceptibly different behaviour for

users and whether they ascribe this to personality or to culture. Finally, the pedagogical

objectives - desired changes in attitudes and/or behaviours – must be evaluated.

This paper focuses on the modelling issues tackled in the development of FAtiMA and

FAtiMA-PSI and so the first two types of evaluation are the most relevant to this discussion.

Pedagogical evaluation of ORIENT has not in any case yet been carried out. There are logistical

problems in evaluating an application in which three users jointly interact with a large screen in a

real physical space using a variety of interaction devices (mobile phones, a dance mat, a

Wiimote) that are outside the scope of this discussion (but see Lim, Aylett, Enz, Kriegel,

Vannini, Hall, Jones 2009). The pedagogical objectives would also need to be explained within

the context of the Bennett (1993) stages model and space does not allow this here.

Four groups of four in Germany (N=12, all female) and 3 groups in the UK (N=9, 6 male,

3 female) were evaluated through a whole engagement session with ORIENT using a set of

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questionnaires with 5-point Likert scales. The results were not of course statistically significant

with these small numbers but gave interestingly suggestive differences between German and UK

subjects on some questions relating to perception of the Sprytes and their culture. While all

German subjects felt Sprytes culture was “friendlier” than theirs, the majority of UK subjects

thought it was “less friendly”. On the Intergroup Anxiety Scale (Stephan, Diaz-Loving, & Duran,

2000), while values were similarly high for adjectives comfortable, confident, at ease,

entertained, interested, and happy, only UK subjects scored high for frustrated and concerned.

Finally, the UK subjects rated the Sprytes low on consider you as an enemy/friend (i.e Sprytes

seemed hostile towards them) while the German subjects rated the Sprytes high.

Perception of cultural difference for different cultural parameter settings was evaluated in

two studies, reported in (Mascarenhas, Afonso, Dias, Enz & Paiva 2009, Mascarenhas et al

2010). These did not use the ORIENT Sprytes but humanoid figures with slightly archaic robes

and hats, meeting together for a meal. These were presented in a non-interactive form as pairs of

videos. The first study showed different rituals (Mascarenhas et al 2009), where one video

represented a culture with high power distance and the other a culture with low power distance.

In the second study (Mascarenhas et al 2010) one video had extreme individualist parameter

settings, and the other extreme collectivist parameter settings. Questionnaires were used to see if

users perceived a difference when the cultural parameters were varied.

The first study asked users to identify attributes that related to high and low power

distance. Nearly all the sample characterised the cultures according to the power distance of the

ritual that had been depicted. In response to a question asking whether culture or personality was

the cause of the differences in behaviour between the two videos, 67% associated the differences

with culture, 30% with personality, and 3% answered neither. In the second study, relating to the

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attributes of individualistic and collectivist cultures, nearly all users again showed an ability to

distinguish between them correctly. Interestingly however they also characterised the collectivist

culture as more hierarchical (higher power distance) than the individualist one. This suggests that

the Hofstede dimensions are not completely orthogonal. The question about the source of the

difference produced the opposite result from the first study: 63% associated the differences with

personality, 30% with culture, and 7% with neither.

This result might be explained by the argument of Hofstede (2001) that behaviours

associated with the cultural dimensions are implicit cultural manifestations, while rituals and

symbols are explicit manifestations. However it is not clear how far an observer of an unfamiliar

culture is able to distinguish between ritual and non-ritual behaviours. Both culture and

personality are assessed as patterns of behaviour over time. An attribution to personality is an

individually-centred explanation of these patterns, while seeing them as culture is a socially-

centred view of behaviour. The dinner party scenario is an inherently social event in which there

is a great deal of common behaviour in both the high and low power distance cases. The

alteration of collectivist-individualist parameters in contrast produces more changes in individual

behaviour. This may make it inherently more likely that an observer will interpret the differences

as due to personality.

This contrasts with the ORIENT study above in which users showed no indications of

differentiating the Sprytes by personality. However Sprytes were not equipped with many

variable expressive features – for example they had no facial expression changes. They were

equipped with a range of gestures, but these were deliberately chosen to be unfamiliar to human

users. Though their behaviour was driven by the FAtiMA-PSI affective architecture, only the

content of their utterances and their movement in space (advancing or retreating for example)

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could provide emotional cues. Finally, rather than watching a video, ORIENT users interacted

with Sprytes, mimicking the unfamiliar gestures, and trying to understand the meaning of

culturally-specific artefacts. Arguably this forced users to focus on social rather then individual

aspects of behaviour.

CONCLUSIONS AND FUTURE DIRECTIONS

In this paper we have reviewed work taking certain theories from psychology and cultural

modelling and bringing them together in computationally-implemented models. These models

have been developed for specific applications in which the affective engagement of the human

user is the basis for a desired pedagogic effect. Inevitably this means that models originally

developed from a descriptive or analytical perspective are applied generatively in order to

produce behaviour rather than to analyse it.

The model adopted is inherently interactional, in that the moment-to-moment affective

states generated within characters depend entirely on the events they perceive in their

environment. While the number of affective states modelled and the thresholds and decay rates

of each are givens for a specific character, we have already described how the intensity of

specific affective states and the extent to which they result in actions are contingent and learned.

Two different contributions can be made by this type of work, one to the theoretical field and one

to the external world.

Once a computational model has been constructed, then its precision lays the basis for

questions that the theorists may not have yet posed. In considering how to extend FAtiMA

above, we asked questions about the relationship between more physiological based theories and

cognitive-appraisal theories that challenge both theories. In the same way, our detailed

consideration of how to include culture in an affective architecture raises detailed questions

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about how culture impacts emotional responses: for example, are some cultures more prone to

certain emotions, do some cultures nurture particular types of temperament?

However it may be that the greatest impact of this style of work lies in the external world.

As computational resource spreads from the desktop into everyday human social environments

via powerful handheld devices and the embedding of computational power into the environment,

affective and culturally-sensitive characters may become the new generic interface. We have

noted the human tendency to project social-partner status onto autonomous graphical characters.

If the models that sustain this engagement can take its weight, affective technology could be the

currency of all our lives in the future.

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Figure 1: FearNot! – education against bullying

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Figure 2: interaction with FearNot!

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<EmotionalThreshold emotion="Love" threshold="3" decay="5" />

<EmotionalThreshold emotion="Hate" threshold="3" decay="5" />

<EmotionalThreshold emotion="Hope" threshold="4" decay="8" />

<EmotionalThreshold emotion="Fear" threshold="1" decay="2" />

........

<EmotionalThreshold emotion="Reproach" threshold="2" decay="8" />

<EmotionalThreshold emotion="Gratitude" threshold="3" decay="5" />

<EmotionalThreshold emotion="Anger" threshold="3" decay="5" />

Figure 3: Defining a character’s emotional disposition

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Figure 4: Spryte characters in ORIENT

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Table 1: Representing an emotion computationally

Attribute Definition

Type The type of the emotion being represented

Valence Positive or negative value

Target The agent/object targeted by the emotion

Cause The event/action causing the emotion

Intensity An integer value representing how much emotion

Time stamp Time emotion created or last updated

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Table 2: Model elements used for FAtiMA character definitions

Model Element Definition

Emotional Dispositions Thresholds and decay rates for each of the 22 OCC emotions

Emotional Reactions Rules defining desirability and praiseworthiness of events and actions for the character

Action Tendencies Reactive actions triggered by an emotion above a given intensity

Goals Goals with activation pre-conditions and success and failure conditions

Actions The action repertoire available to the character for satisfying goals

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Table 3: PSI Drives/needs

Drive Definition

Existence preserving/survival needs

Food, water and maintenance of physical integrity. Relieved by the consumption of matching resources; increased by metabolic processes (in the case of food and water) or inflicted damage (in the case of integrity)

Species preserving need Sexuality - the need of the organism to reproduce itself.

Affiliation The need for social experiences, e.g. to belong to a group or to be accepted by others. Increases with anti-legitimacy signals such as signs of social exclusion.

Certainty Ability to predict what will happen in a certain situation, also to predict consequences of (one’s own) actions. Relieved through exploration (increases knowledge of the environment, improving prediction). Raised when outcomes mis-match expectations.

Competence Ability to master problems and tasks, including satisfying one’s needs. Achievement of goals relieves competence drive, failure raises it.

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Table 4: Hofstede’s cultural dimensions model

Power Distance: how far less powerful group members expect/ accept power is distributed unequally

High-PD: more coercive and referent power is used. Power Distance Index scored as 104 in Hofstede sample

Low-PD: more reward, legitimate, and expert power is used. Power Distance index scored as 11 in Hofstede sample

Individualism/ Collectivism Individualism: ties between individuals are loose: everyone expected to look after themselves and their immediate family

Collectivism: integration from birth into strong, cohesive in-groups protecting members in exchange for unquestioning loyalty

Masculinity/ Femininity: distribution of roles between genders

High-M: men’s values very assertive and competitive; maximally different from women’s values

High-F: men’s values modest and caring and similar to women’s values

Uncertainty avoidance High: Acceptance of familiar risks; fear of ambiguous situations and of unfamiliar risks

Low: Comfortable with ambiguous situations and unfamiliar risks

Long term/Short Term orientation Long-term: values of thrift and perseverance

Short-term: values of respect for tradition, fulfilling social obligations saving ‘face’

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Figure 1: FearNot! – education against bullying

Figure 2: interaction with FearNot!

Figure 3: Defining a character’s emotional disposition

Figure 4: Spryte characters in ORIENT

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For Peer Review - Heriotruth/Papers/agents-affect/... · discussed. We discuss the modelling relationships between culture, social interaction and cognitive appraisal. Integrating