Matching artificial agents’ and users’ personalities: designing agents with regulatory-focus and testing the regulatory fit effect Caroline Faur ([email protected])

Matching artificial agents’ and users’ personalities: designing agents with

regulatory-focus and testing the regulatory fit effect

Caroline Faur ([email protected])Jean-Claude Martin ([email protected])

Celine Clavel ([email protected])LIMSI-CNRS, rue John Von Neuman, bt 508

91403 Orsay Cedex, France

AELEE KIM Cognitive Science, Ph.D. CandidateMethodology in Cognitive Science

Professor Byoung-Tak ZhangSeoul National University Fall 2015

Artificial agentsArtificial Companion

PersonalitySocial Cognition

Regulatory FocusRegulatory Fit

Keywords

Designing agents with personalities to the benefits of users.

PURPOSE / Challenge

INTRODUCTION

Artificial Agent

Human-Computer Interface

Artificial Com-panion User

Personality

Social Cognition

Regulatory Focus

Regulatory Fit

INTRODUCTION

Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit

Theory

Human

Believability

INTRODUCTION

Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit

Theory

”a personalised, multi-modal, helpful, collaborative, conversational, learning, social, emotional, cognitive and persistent computer agent that knows its owner, interacts with the user over a long period of time and builds a (long-term) relationship to the user”(Sviatlana, Busemann, & Schommer, 2012)

Artificial + Companion인공의 , 인위적인 , 인조의 + 친구 , 동반자 , 동료 , 반려 , 벗

INTRODUCTION

Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit

Theory

A coherent patterning of affect, behavior, cognition, and desires (goals) over time and space (Revelle & Scherer, 2009).

Personality : 성격 , 사람 , 인격 , 개성

Help to increase the companion’s believability

INTRODUCTION

Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit

TheorySocial cognition is a sub-topic of social psychology that focuses on how people process, store, and apply information about other people and social situations.

It focuses on the role that cognitive processes play in our social interactions.

Social cognition is the study of how people process social information, especially its encoding, storage, retrieval, and application to social situa-tions.

INTRODUCTION

Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit

Theory

Human

INTRODUCTION : Regulatory Focus Theory

Originated by Tory E. Higgins from Columbia University, 1997 http://www.columbia.edu/cu/psychology/higgins/

Self-regulation strategies

A fundamental Motivational Theory

Promotion VS Prevention types

People’s tendency toward promotion vs prevention focus when they consider

what goals to pursue and how to pursue goals.

Regulatory focus can be situational, induced by the context, but theory states that

people have a chronic focus, an “habitual” focus used by default.

http://www.columbia.edu/cu/psychology/higgins/

Promotion Focus

Gain vs. Nongain

Approach strategies

Errors of omission

Prevention Focus

Loss vs. Nonloss

Avoidance strategies

Errors of commission


Regulatory + Focus규제의 , 조정력을 가진 , 단속의 초점 , 집중하다 , 중심 , 주안점

Promotion Focus

Gain vs. Nongain

Approach strategies

Errors of omission

Prevention Focus

Loss vs. Nonloss

Avoidance strategies

Errors of commission

Regulatory + Focus규제의 , 조정력을 가진 , 단속의 초점 , 집중하다 , 중심 , 주안점

Not performing an act or behavior – just didn’t do it

Something left out by accident. Transaction is to be left out to regis-

ter. Partial entry of one transaction.

Performing a different act or behavior – not to norm

Something wrong is done RS. 1500 recorded as RS. 5100


INTRODUCTION : Regulatory Fit

NonGains NonLosses

Regulatory-fit : A feeling of rightness about the pursued goal and increases task engagement (Higgins, 2005)

Regulatory + Fit규제의 , 조정력을 가진 , 단속의 꼭 맞는 , 어울리는 , 건강한 , 맟추다 , 들어맞다


적극성 조심성

QUESTIONS

1.How can we implement regulatory focus for artificial agents ?

Artificial agents 에 어떻게 RF 를 적용시킬 수 있을까 ?

2. Is the intended personality perceived as such ?

RF 를 적용시켰을때 사용자들이 Artificial agents 의 퍼스널리티를 알 수 있을까 ?

3. Can we reproduce a regulatory fit effect between such an agent and users?

사용자와 Artificial agents 간에 Regulatory fit 효과를 재현해 낼 수 있을까 ?

Computers As Social Actors (CASA) paradigm (Nass & Moon, 2000)

People tend to adopt social attitudes with machines that can elicit social heuristics.

BACKGROUND

Personality MeasurementThe Five Factors Model (FFM) (Costa & McCrae, 1992) also known as the Big Five:

1. Openness Experience ( 개방성 )2. Conscientiousness ( 성실성 ) 3. Extraversion ( 외향성 )4. Agreeableness ( 친화성 )5. Neuroticism ( 신경성 )

BACKGROUND

Traits Theories ( 성격이론 ) Social Cognitive Models

Useful for the description of the personality.

But by looking at the global structure of

personality, they hide intra-individual differences.

The socio-cognitive approach to personality

underlines the importance of a situation in

exhibiting personality behaviors.

(Bandura, 1999).

This approach attempts to understand

cognitive and social processes that lead to

personality.

For that purpose, it focuses on the

interaction between the person and the

social context and highlights the intra-

individual differences

(Mischel, Shoda, & Smith, 2004).

Personality Can’t Stop

Board Game(Designed by

Sid Sackson)

Stop-or-Again

Stopping a turn, saving the current gains.

But loosing in speed

Playing again, taking the risk

of loosing the current gains

to win more

METHODOLOGY : Convey personality via game strategies

Game Rule

Personality Can’t Stop

Board Game(Designed by

Sid Sackson)

Stop-or-Again

Stopping a turn, saving the current gains.

But loosing in speed

Playing again, taking the risk

of loosing the current gains

to win more

METHODOLOGY : Convey personality via game strategies

Game Rule

Regulatory Focus Questionnaire

Proverbs Form (RFQ-PF)

-> Measuring the strength of the two self-regulatory strategies

Participants : 15 = 13 men + 2 women

three models :

1. one for the choice of a move during the game

2. two for the ”stop-or-again” decision

1) With and without taking into account personality scores as a feature;

2) The latter should smooth intra - individual differences to produce a

”depersonalized” strategy.

METHODOLOGY : Data-driven implementation

METHODOLOGY : Experimental Design2 types of strategies + 4 types of agent

Strategy

Random

AI

Agent

1. Rand (Random Agent)

Which chooses randomly its moves and has a 50%

probability to stop its turn

2. Avg ( Average Agent)

which follows the ”deper-sonalized” strategy

3. RF-Pro (Promo-tion Agent)

Which has a promotion score of 7 and

a prevention score of 1

4. RF-Pre

(Prevention Agent)

Which has a promotion score of 1 and a prevention

score of 7.

USER STUDY

Hypothesis

H1 : The differences in agents personalities are perceived by the human player

( 사용자가 에이전트 퍼스널리티의 차이를 인식한다 )

H2 : The credibility of the agent is increased by the presence of personality. The RF-agents are perceived as more likeable and more intelligent than the Rand-agent and the Avg-agent.

( 퍼스널리티가 있는 경우 에이전트에 신뢰성이 증가된다 ) (RF-agents 는 Rand-agent 와 Avg-agent 보다 호감도와 지적인 면이 높게 인식된다 )

H3 : According to the regulatory-fit theory, human player oriented as promotion find RF-Pro agent more credible than other agents (respectively for RF-Pre).

R-fit 이론에 의하면 promotion 경향의 사용자는 RF-Pro 에이전트에 다른 에이전트 보다 더 신뢰성을 보인다 . (prevention 경향 사용자에도 같은 가설 적용 )

Participants : 20 = 11 men + 9 women (age M = 30.6 years, SD = 8.1)



-> Measuring the strength of the two self-regulatory strategies

14 Participants : A chronic promotion focus

6 Participants : A chronic prevention focus

Played Can’t Stop Game



+

The Godspeed Questionnaire (likeability, the perceived intelligence of the agent)

USER STUDY

USER STUDY : Result

Hypothesis Result

H1 : The differences in agents personalities are perceived by the human player

( 사용자가 에이전트 퍼스널리티의 차이를 인식한다 )

Almost Validated RF-Pro and RF-Pre agents has been respectively perceived as promotion-oriented and prevention- oriented

H2 : The credibility of the agent is increased by the presence of personality.

The RF-agents are perceived as more likeable and more intelligent than the Rand-agent and the Avg-agent.

( 퍼스널리티가 있는 경우 에이전트에 신뢰성이 증가된다 )

(RF-agents 는 Rand-agent 와 Avg-agent 보다 호감도와 지적인 면이 높게 인식된다 )

Partially Validated Found a difference in favor of the RF-Pre agent regarding the perceived intelligence. The RF-Pro agent was rated as more intelligent than the Rand and Avg agents but the difference was not significant.

H3 : According to the regulatory-fit theory, human player oriented as promotion find RF-Pro agent more credible than other agents (respectively for RF-Pre).

R-fit 이론에 의하면 promotion 경향의 사용자는 RF-Pro 에이전트에 다른 에이전트 보다 더 신뢰성을 보인다 . (prevention 경향 사용자에도 같은 가설 적용 )

Partially Validated Found an interaction between the user’s focus and the type of agent regarding the likeability score : prevention-oriented users found the RF- Pre agent and the Rand agent more likeable than the RF-Pro agent and the Avg agent. Because RF-Pre and Rand agents were both perceived as prevention-oriented, we could say that regulatory fit happened for prevention- focus users.

PersonalityScores

CredibilityScores

USER STUDY : Result

H1

H2

Promotion Focus Prevention Focus

USER STUDY : Result

H3

Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit

Theory

Human

Believability

Key Concept Review

CONCLUSION

1. It is possible to successfully endow artificial agents with regulatory-focus and that this regulatory-focus can be accurately perceived by users.

2. Provided data which point to the possibility of using the concept of regulatory fit with artificial agents.

PERSPECTIVESTo better understand the regulatory fit effect with artificial agents :

1. Making more longitudinal studies because only repeated interactions could allow users to form a real model of the agent’s personality

2. Using multi-modality to enhance the interaction, such as verbal and non-verbal behaviors during the game by providing a physical representation of a virtual Agent

3. Complementing self-report measures by users’ behaviors measures, such as engagement for example.

The Linkage between this article and my research Interests


Artificial Agent



Personality

Social Cognition

Regulatory Focus

Regulatory Fit


Documents

Matching artificial agents’ and users’ personalities: designing agents with regulatory-focus and testing the regulatory fit effect Caroline Faur ([email protected])