MODELLING THERAPEUTIC

EMPATHY IN A VIRTUAL AGENT TO SUPPORT

THE REMOTE TREATMENT OF MAJOR DEPRESSION

Juan Martínez-Miranda, Adrián Bresó and Juan Miguel García-Gómez

ITACA Institute, Biomedical Informatics Group, Universitat Politècnica de València, Camino de Vera s/n, Valencia, Spain

Keywords: Virtual agents, Modelling emotions, Therapeutic empathy.

Abstract: The use of computer based psychotherapy has been one of the fields that have attracted the interest of

practitioners and computer scientists in the last years given the initial and promising results. In particular the

use of computerised cognitive behavioural therapy for the treatment of major depression has been supported

the evidence that psychological therapies can be delivered effectively without face to face contact.

However, the value of these tools for patients is limited by the difficulty of staying engaged during the long-

time periods of the treatment. The use of Virtual Agents as enhanced human-computer interaction brings the

opportunity to overcome this limitation by establishing effective long-term social relationships with

patients. We introduce the main ideas behind the design of a cognitive-emotional model aimed to generate

therapeutic empathic responses and support the remote treatment of major depression.

1 INTRODUCTION

The recent advances in the development of non-

invasive, wearable sensors and wireless

communications, have contributed to create

environments where hardware devices are

transparent, seamlessly integrated and connected in

everyday life people’s situations. One of the main

fields that has benefited from these novel

environments is healthcare through the development

of systems that provide telemedicine/pervasive care

capabilities for a higher patient’s empowerment.

Several applications have been developed in the last

decade where different physiological, physical and

environmental data are obtained through sensors that

continuously and remotely monitor the patients’

condition (Baga et al., 2009), (Farré et al., 2009).

The main advantage of these systems is that the

real-time generated data through monitoring can be

used to provide immediate feedback to patients,

enabling a more active involvement of the person in

his/her treatment. The treatment of mood disorders

is one of the cases where important part of the

success is due to the active participation of the

patient. In particular, during the treatment of major

depression each person should develop their own

personalised strategies for staying well and avoiding

relapses, strategies that are tailored to their

individual needs and characteristics (Kelly, 2000).

The use of telemedicine applied to mental health

(and particularly in the treatment of major

depression) is a relatively new area under research.

One of its main aims is to promote a participative

attitude of patients over the course of the treatment

and minimise the risk of a premature discontinuation

of it which appear one of the main reasons behind

relapsing. Some initiatives already exist in this

context such as computerised cognitive behavioural

therapy (CCBT) which uses stand-alone computer

software or a web application to encourage patients

to complete self-help tasks that involve altering

behaviour and reflecting on and reframing

cognitions. Examples of these applications include

Beating the Blues

(http://www.beatingtheblues.co.uk/), MoodGYM

(http://moodgym.anu.edu.au/) and FearFighter

(http://www.fearfighter.com/) among others. CCBT

appears only weakly effective when the motivation

to continue is left to the patient, but has greater

benefit when use is augmented by support

(Kaltenthaler, 2006); to date this has been provided

either face to face or by telephone.

A complementary approach is a system that

supports the remote patient’s treatment through the

264

Martínez-Miranda J., Bresó A. and García-Gómez J..

MODELLING THERAPEUTIC EMPATHY IN A VIRTUAL AGENT TO SUPPORT THE REMOTE TREATMENT OF MAJOR DEPRESSION .

DOI: 10.5220/0003833302640269

In Proceedings of the 4th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2012), pages 264-269

ISBN: 978-989-8425-96-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

provision of a continuous and personalised

monitoring over specific behaviour patterns to

collect relevant data allowing the identification of

current patients’ condition. The collected

information can then be used to provide specific

information and recommendations to the patient

complementing the treatment and more important,

prevent the recurrence of symptoms in the future.

Equally important to the suitable collection and

interpretation of patient’s data, is the manner to

provide the information and recommendations to

the patient in order to increase his/her engagement

and help him/her to form an alliance with the

system. The work presented in this paper describes

the first steps towards the design of a virtual agent

(VA) as the main user interface within a system to

support the remote treatment of patients diagnosed

with major depression. In particular, we present our

main ideas for the development of the internal

mechanism in the virtual agent that will lead an

effective therapeutic empathic behaviour to engage

patients and motivate them to keep interacting with

the system and in consequence, promote treatment

adherence.

The following section briefly presents some

current works where synthetic characters have been

developed for mental health interventions. Section 3

makes a general description of the system to support

the remote treatment of major depresssion in which

our VA is embedded. Section 4 describes the main

ideas behind the design of the VA mainly discussing

the differences between natural empathy and

therapeutic empathy and how the conceptualisation

of the second will be integrated in the VA’s

cognitive-emotional model to generate empathic

responses. Finally section 5 presents the conclusions

by describing the current and further work.

2 SYNTHETIC CHARACTERS

IN MENTAL HEALTH

Semi-automated computer-driven interactions have

long been used in healthcare for reminder messages,

disseminating health information, administering

questionnaires, and even conducting interventions

(Glasgow et al., 2004). As the technology to create

and control believable synthetic characters has

become more widely available, the user interface

design has changed to include embodied

conversational agents. The key to successful

interfaces that are structured around an agent is the

relationship between user and agent (Bickmore and

Mauer, 2006). If patients build a rapport with their

agents, they are more likely to use the system, and

more motivated to complete longer sessions. Well-

designed VA’s have been shown to motivate users

(Baylor, 2009) and lead to higher user engagement

than traditional static visual user interfaces (Dehn

and van Mulken, 2000).

There are currently some promising examples of

VA-based interfaces in mental health applications.

The work presented in (Lisetti and Wagner, 2008)

discusses the issues and potential of animated

characters to promote healthy behaviours for helping

teenagers with alcohol abuse through motivational

interventions. Tartaro and Cassell (2008) describe

the development and evaluation of a virtual

character to engage children suffering from autism

spectrum disorders (ASD) in collaborative narrative

to produce contingent discourse. Bickmore et al.

(2010a) developed an agent which encouraged

people with schizophrenia to adhere to their

medication regime, as well as an agent who provided

discharge information to people with depression

who were about to leave the hospital where they had

been admitted for treatment (Bickmore et al.,

2010b). In (Pontier and Siddiqui, 2008) a VA that

guides the user through the Beck Depression

Inventory (BDI), –a questionnaire used to measure

the severity of depression– is presented. Through a

basic emotional model, the VA shows an empathic

behaviour (through its facial expression representing

sadness and/or happiness) to the user depending on

his/her assessed depression level.

All these previous efforts have contributed to

identify a set of key characteristics that these

interfaces should adopt. Such features include the

appearance, verbal and non-verbal communication,

coherent personality, referencing knowledge of prior

interactions, variability in agent behaviour and the

adoption of an empathic behaviour during

interaction. In this paper we focus on our current

activities towards the design of a VA able to produce

effective therapeutic empathy interactions.

3 HELP4MOOD: SUPPORTING

THE TREATMENT OF MAJOR

DEPRESSION

Our proposed VA is a key component of a FP7-EU

research project aimed to support the remote

treatment of people with major depression

(www.help4mood.info). The main aim of the project

is to support people with major depression in its

MODELLING THERAPEUTIC EMPATHY IN A VIRTUAL AGENT TO SUPPORT THE REMOTE TREATMENT OF

MAJOR DEPRESSION

265

mild to moderate form (those people who do not

require hospitalisation but need to follow the

treatment as part of their daily activities). The

proposed system will collect a relevant set of

parameters that allow the detection of specific

behaviour patterns in the patient, prompting

adherence to computerised cognitive behavioural

therapy, and promoting healthy behaviours in

response to monitored inputs.

The three main components of the Help4Mood

system include a Personal Monitoring System

(PMS), a Virtual Agent-based user interface, and a

Decision Support System that will support clinicians

in interpreting self-reported and monitoring data.

The PMS will collect two types of data: i)

behavioural data including patterns of sleeping,

motor activity and speech; and ii) subjective data

including brief validated scales to measure mood,

cognition and behaviours. The identified behaviour

patterns will then be used by the VA to prompt the

patient (when appropriate) to carry out potentially

helpful activities, such as relaxation or exercise,

offer the opportunity to add entries to a spoken diary

or, if the collected data suggest a potential treatment

failure and/or a suicide risk, urgently alert the

clinical site leading to a direct communication.

The specific feedback provided by the VA will

be mainly through a combination of dialogue

interaction and a basic set of body movements and

facial expressions designed to maintain the attention

of the patient and help him/her to effectively manage

important stages of his/her treatment. The VA is

composed of three main modules: the graphical

appearance, a dialogue manager system which will

implement and manage the interaction dialogues

between the VA and the patient, and the module

which generates the VA’s cognitive-emotional

behaviour which is introduced in this paper.

A patient-side Decision Support System will

facilitate intelligent, adaptive interpretation of self-

reported data. A clinician-side Decision Support

System will distil both self-reported and objective

data into a succinct textual and graphical overview

that will help clinicians in the assessment of the

patient’s current state and guide further treatment

decisions.

4 DESIGNING

THE VIRTUAL AGENT

An important aspect in the development of the

Help4Mood’s VA is to motivate a long-term use and

interaction between the user and the agent. This is an

especially important issue when the main objective

of a VA is to promote the use of the system and

complete longer sessions (Bickmore and Mauer,

2006). The key characteristics that highly contribute

to maintain the motivation of users while interacting

with virtual characters include the believability of

the VA’s behaviour and a good verbal and non-

verbal communication. To successfully obtain these

two characteristics in a VA, there should be an

internal mechanism that produces a coherent

emotional behaviour and communication that

complements the cognitive actions and decisions

taken during interaction.

A coherent and consistent emotional behaviour

(usually referred as emotional competence with

respect of two domains: emotion production and

emotion perception (Scherer, 2010)) has been

identified as particularly important in VAs used for

psychotherapy (Bickmore and Gruber, 2010),

(Lisetti, 2008) and it is still an open research line

where several considerations (including theoretical,

technical and ethical) need to be addressed. In the

last years, several computational models of emotions

have emerged trying to cover specific (and

sometimes theoretically incompatible) emotional

mechanisms (Marsella et al., 2010). The differences

in many of the existent computational models of

emotions are a logical consequence of the different

emotion theories where these models have their

roots (Scherer, 2010).

From the current existent theories of emotion, the

one that predominates above the others in the efforts

dedicated to implement computational models of

emotions is the cognitive appraisal theory of

emotions (Scherer et al., 2001). The core concept of

appraisal theories refer that the events in a person’s

environment are constantly identified and evaluated

by the individual. This cognitive evaluation (or

appraisal) process leads to an emotional response

(according to the event’s relevance for the person)

which in turn generates a specific behaviour to cope

with the appraised events. The high success in

choosing this theory as the theoretical background in

several computational models seems in part due to

the emphasis and explanation of the connection

between cognition and emotion which help in the

construction of artificial systems that simulate

complex human-like behaviours. Moreover,

appraisal theories of emotion appear the most

comprehensive way to represent the complexity of

the emotion process, covering the whole path from

low-level appraisals of the eliciting event to high-

level influence over behaviour (Scherer, 2010).

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Several computational architectures based on the

appraisal theory of emotions have been developed

since the early 90’s (Elliott, 1992) and as reflected in

(Marsella et al., 2010), virtual characters based on

this type of architectures has allowed the creation of

real-time interactive characters that exhibit emotions

in order to make them more compelling, more

realistic, or more able to induce desirable social

effects in the users.

4.1 Natural Empathy vs. Therapeutic

Empathy

The promising research results reported in the

literature such as the works mentioned above,

encourage us to consider the adoption of a

computational architecture based on appraisal

theories of emotion for the implementation of the

internal model in our Help4Mood’s VA. We of

course need to consider the particular characteristics

of our target users to adopt the most suitable

solution. As suggested by the clinicians of the

Help4Mood consortium, two of the important

characteristics in our VA should be an adaptable

empathic behaviour and a personality style that

motivates the use of the system.

A key difference in the empathic behaviour of

our proposed VA regarding existent similar works

such as e.g. the presented in Paiva et al., (2004) and

Prendinger and Ishizuka (2005), is that the empathic

responses needs to be modulated according to the

special characteristics of the patients. Although the

VA should be able to show empathy during

interaction, it is highly important from a clinical

perspective that the range of emotions displayed

should be restricted to a neutral stance or positive

emotions (such as joy and happiness) and not to

produce an empathic behaviour by just adopting the

same (frequently negative) emotions or affective

state reported by the patient.

In this sense, it is important to distinguish the

difference between natural empathy (experienced by

every people in every-day situations) and therapeutic

empathy in order to provide to the patients with a

useful feedback for their particular treatment and

promoting an effective therapeutic alliance. In

(Hoffman, 2000) natural empathy is associated to

psychological processes that make an individual to

have feelings that are more congruent with another’s

situation that with his/her own situation.

From the psychotherapeutic and counselling

perspective the term therapeutic empathy is defined

as when the therapist is sensing the feelings and

personal meanings which the client is experiencing

in each moment, when he can perceive these from

’inside’, as they seem to the client, and when he can

successfully communicate something of that

understanding to his client (Rogers, 1961 p. 62).

This particular type of empathic behaviour is the one

that it is clinically relevant and should include the

mechanism to modulate those therapeutic useless or

inadequate emotions.

There is currently some effort to identify and

dissect the theoretical key components of therapeutic

empathy. Thwaites and Bennett-Levy (2007) argue

that a therapeutic empathy system should contain the

following four components:

1. Empathic attitude/stance: infuses other aspects

of empathic skill with a sense of benevolence,

curiosity and interest.

2. Empathic attunement: a perceptual skill referred

to as an active on-going effort to stay attuned on

a moment-to-moment basis with the client’s

communication and unfolding process

3. Empathic communication skills: are the skills to

explicitly communicate empathic understanding

as well as emotions not only through the content

of the speech itself, but also by non-verbal

behaviour and tone of voice providing a sense

of safety, warmth, understanding and

acceptance.

4. Empathy knowledge: defined as what therapists

learn from teachers and from reading during

training and professional development and it is

one of the key factors that differentiate

therapeutic empathy from natural empathy.

Although our VA will not have the role of a

therapist (but more related to a helper/guide during

the treatment’s short every-day sessions), it is

interesting to analyse how the conceptualisation of

therapeutic empathy can be modelled to implement a

computational therapeutic empathy system. A

central aspect is the integration of the four concepts

at the different components of the computational

appraisal architecture (Figure 1).

An empathic attunement can be modelled in the

VA when specific events are detected in the

environment. These events are inferred using the

data collected from the personal monitoring system

and the patient’s self-reports. Following the VA’s

goals (which are activated according to the particular

objectives of each treatment’s session), the event

could be appraised as desirable or not in terms of the

patient’s perspective. Every event needs to be

understood in these terms for the VA remains

empathically attuned on a moment-to-moment basis.

MODELLING THERAPEUTIC EMPATHY IN A VIRTUAL AGENT TO SUPPORT THE REMOTE TREATMENT OF

MAJOR DEPRESSION

267

The result of this appraisal process is used to

select the specific emotion in the VA, which is also

influenced by the modelled personality in the VA

and the empathy knowledge layer. For the modelling

of personality we are considering those trends

related to an agreeableness personality to facilitate

an empathic attitude in the VA. One of the main

functions in the empathy knowledge component will

be the modulation of the emotion triggered by the

emotion selection mechanism. The empathy

knowledge modulates the negative emotions to suit

the clinical perspective for depression treatment, i.e.

adopting a neutral stance when patient indicates

negative moods, thoughts and feelings, and display

positive emotions in the rest of situations.

Figure 1: The Help4Mood VA’s internal model.

The specific triggered emotion will be used to

select the action for coping with the detected event.

The empathy knowledge will also be used by the

action-selection mechanism to choose the most

adequate action during the interaction with the

patient. As already introduced, the empathy

knowledge is one of the key factors to distinguish

therapeutic empathy from natural empathy and its

inclusion in both, the emotion and action selection

mechanisms, intends that the VA emulates the

emotional involvement during specific interactions

with the patient and an emotional detachment that

allows for a more objective appraisal of the situation

(Clark, 2007).

The actions take the form of high level

commands (such as SUGGEST_ACTIVITY(x) or

APPLY_QUESTIONNAIRE(y)) which are passed

jointly with the emotion as a <task, emotion> pair to

be implemented through the concrete utterances by

the dialogue manager system and the corresponding

facial expression in the graphical component of the

VA. The <task, emotion> pair will affect the verbal

and non-verbal communication of the VA by

affecting the voice, the actual wording of the prompt

and the facial expressions of the VA leading to an

empathic communication.

5 CONCLUSIONS

The model presented in the past section is currently

under development and we are analysing existent

computational architectures of emotion that can be

extended with our proposal. A good candidate is the

known as the FAtiMA (FearNot Affective Mind

Architecture) architecture (Paiva et al., 2004).

FAtiMA is an open source software and it has been

an evolving (appraisal theory based) architecture

developed in the context of three EU projects: the

FP-5 VICTEC (http://www.macs.hw.ac.uk/victec/

index_geral.html), the FP-6 ECIRCUS

(http://www.macs.hw.ac.uk/EcircusWeb/) and the

FP-7 LIREC (http://lirec.eu/) projects. The original

objective of FAtiMA was the creation of empathic

agents within a computer application to tackle and

eventually help to reduce bullying problems in

schools. The core of the architecture has been

subsequently extended to include additional

components of the emotion phenomenon such as the

implementation of a double-appraisal mechanism in

order to evaluate the emotional impact of possible

actions (Aylett and Louchart, 2008); the memory

retrieval (and re-appraisal) of emotionally past

episodes (Gomes et al., 2011); or the generation of

emergent empathic reactions in social agents (Paiva,

2011).

Once implemented and integrated with the rest

of the system, the outcome behaviour produced in

the VA can be clinically evaluated in the further

stages of Help4Mood and assess its suitability in the

building of VA – patient interactions.

ACKNOWLEDGEMENTS

This paper reflects only the author’s views. The

European Community is not liable for any use that

may be made of the information contained herein.

This research is carried out within the EU FP7

Project “Help4Mood – A Computational Distributed

System to Support the Treatment of Patients with

Major Depression” [ICT-248765].

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