WHY ANTHROPOMORPHIC USER INTERFACE FEEDBACK

CAN BE EFFECTIVE AND PREFERRED BY USERS

Pietro Murano

University of Salford, School of Computing, Science and Engineering, Computer Science Research Centre, Interactive

Systems and Media Laboratory, Gt. Manchester, M5 4WT, England, UK

Keywords: User interface feedback, Anthrop

omorphism, Human behaviour.

Abstract: This paper addresses and resolves an interesting

question concerning the reason for anthropomorphic user

interface feedback being more effective (in two of three contexts) and preferred by users compared to an

equivalent non-anthropomorphic feedback. Firstly the paper will summarise the author’s three

internationally published experiments and results. These will show statistically significant results indicating

that in two of the three contexts anthropomorphic user interface feedback is more effective and preferred by

users. Secondly some of the famous work by Reeves and Nass will be introduced. This basically shows that

humans behave in a social manner towards computers through a user interface. Thirdly the reasons for the

obtained results by the author are inextricably linked to the work of Reeves and Nass. It can be seen that the

performance results and preferences are due to the subconscious social behaviour of humans towards

computers through a user interface. The conclusions reported in this paper are of significance to user

interface designers as they allow one to design interfaces which match more closely our human

characteristics. These in turn would enhance the profits of a software house

1 INTRODUCTION

User interface feedback in software systems is being

improved as time passes and developers dedicate

more time to the feedback and realise that feedback

to the user is just as important as the rest of an

application.

In line with the goal of constant improvement

and better understanding

of user interface feedback

this research has looked at the effectiveness and user

approval of anthropomorphic feedback. This was

compared to an equivalent non-anthropomorphic

feedback.

Anthropomorphism at the user interface usually

volves assigning human characteristics or qualities

or both to something which is not human, e.g. a

talking dog or a cube with a face that can talk etc. A

well known example is the Microsoft Office Paper

Clip. It could also be the actual manifestation of a

real human such as a video of a human (Bengtsson

et al, 1999).

This issue has been considered because there was

a di

vision between computer scientists where certain

computer scientists are against (e.g. chapter by

Shneiderman in ((Bradshaw, 1997) and

(Shneiderman, 1992)) anthropomorphism at the user

interface and others are in favour (e.g. Agarwal

(1999), Cole et al. (1999), Dertouzos (1999), Guttag

(1999), Koda and Maes (1996a), (1996b), Maes

(1994) and Zue (1999)) of using anthropomorphism

at the user interface. However there has not been

concrete enough evidence to show which opinion

may be correct.

Experiments (summarised below and detailed in

rano (2001a), (2001b), (2002a), (2002b), (2003))

have been conducted where it has been shown with

statistical significance that in certain contexts

anthropomorphic user interface feedback is more

effective and preferred by users. However these

experiments concentrated on ‘what’ type of

feedback was better (i.e. anthropomorphic or non-

anthropomorphic) and not on ‘why’ a particular type

of feedback was better over the other.

This issue of ‘why’ was raised as an interesting

uestion at various international conferences

attended by the author. Hence firstly this paper aims

to address this question and provide an answer by

means of the body of evidence produced by Reeves

and Nass. It is believed by the author that no other

researchers outside of Reeves and Nass’ influence

have used and validated some of their results in such

a detailed manner. Secondly, the experiments

conducted and summarised below, are innovative in

that while they follow the guidelines of Reeves and

Murano P. (2005).

WHY ANTHROPOMORPHIC USER INTERFACE FEEDBACK CAN BE EFFECTIVE AND PREFERRED BY USERS.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 12-19

DOI: 10.5220/0002546300120019

 SciTePress

Table 1: Comparison of Video Vs Diagrams and text

Comparison of Video Vs.

Diagrams and Text

t-Observed 2.14

t-Critical (5%) 1.74

Table 2: Overall User Preferences

Overall User Preferences

Mean Standard Deviation

Video 8.17 1.10

Diagrams and Text 7.11 2.17

Nass, this is the first time that the guidelines have

been applied to a more realistic context. The

experiments by Reeves and Nass were more

artificial in nature.

This is because despite many computers and

applications being in homes and businesses, there

are still many prospective users who are afraid of

computers. These prospective users could become

actual enthusiastic users, thus potentially increasing

business profits for a software house. This could be

achieved by the improvement of user interface

feedback by using the findings of this paper.

2 SUMMARY OF EXPERIMENTS

In the next three sections below a brief summary is

presented of the three experiments. Full details for

repeatability can be found in Murano (2001a),

(2001b), (2002a), (2002b), (2003). However for

each of the three experiments within users’ designs

were used. This meant that in each experiment all

the subjects tried all tasks and had the opportunity to

use all relevant kinds of feedback. Considerable

efforts were made to maintain laboratory conditions

constant for each subject. Also efforts were made to

control possible confounding variables.

2.1 Experiment One

The first experiment Murano (2002a) was in the

context of software for in-depth understanding. This

was specifically English as a foreign language (EFL)

pronunciation. The language group used was Italian

native speakers who did not have ‘perfect’ English.

Software was specifically designed to automatically

handle user speech via an automatic speech

recognition (ASR) engine. Further, in line with EFL

literature by Kenworthy (1992) and Ur (1996)

exercises were designed and incorporated as part of

the software to test problem areas that Italian

speakers have when pronouncing English.

Anthropomorphic feedback in the form of a

video of a real EFL tutor giving feedback was

designed. This in effect was a set of dynamically

loaded video clips which were activated based on

the software’s decision concerning the potential

error a user had done (if no errors were made no

pronunciation corrections were made by the

software). This type of feedback was compared

against a non-anthropomorphic equivalent. In this

case two-dimensional diagrams with guiding text

were used. The diagrams were facial cross-sections

aiming to assist a user in the positioning of their

mouth and tongue etc. for the relevant pronunciation

of a given exercise. This type of feedback was based

on EFL principles found in Baker (1981) and Baker

(1998). No feedback type was ever tied to the same

exercise, i.e. feedback was randomly assigned to an

exercise.

The results for 18 Italian users (with imperfect

English pronunciation) taking part in a tightly

controlled experiment, going through a series of

exercises were statistically significant. Users were

scored (scores used in hypothesis testing statistical

analysis) according to the number of attempts they

had to make to complete an exercise successfully.

The statistical results suggested the

anthropomorphic feedback to be more effective.

Users were able to self-correct their pronunciation

errors more effectively with the anthropomorphic

feedback. The scores obtained were approximately

normally distributed. These were then used in a t-

test. The results are in the Table 1.

Furthermore it was clear that users preferred the

anthropomorphic feedback. The actual scores

obtained from the questionnaires using a Likert

scale, where 1 was a negative response and 9 was a

positive response, are detailed in Table 2.

Hence it was concluded that the statistically

significant results suggested the anthropomorphic

WHY ANTHROPOMORPHIC USER INTERFACE FEEDBACK CAN BE EFFECTIVE AND PREFERRED BY USERS

Table 3: Comparison of Video Vs Text

Comparison of Video Vs Text

t-Observed 10.21

t-Critical (5%) 1.67

Table 4: Overall User Preferences

Overall User Preferences

Mean Standard Deviation

Video 7.53 1.40

Text 6.35 1.84

feedback to be more effective and preferred by

users.

2.2 Experiment Two

The second experiment by Murano (2001a),

(2001b), (2002b) was in the context of software for

online systems usage.

This was specifically concerned with the using of

UNIX commands. This was an interesting area as

typically novice users of UNIX commands can find

it difficult to master the concepts of the command

structure and to remember relevant commands in the

first place. Software was designed to emulate a small

session at the UNIX shell covering a sub-set of

UNIX commands. As in the first session, an ASR

engine was used which allowed the users to ‘query’

the system verbally. The users which were recruited

for the experiment were complete novices to UNIX

commands.

In this experiment anthropomorphic feedback

was compared with a non-anthropomorphic

equivalent. In this case the anthropomorphic

feedback consisted of dynamically loaded video

clips of a person giving the command verbally for

the current context the user was in. The feedback

was prompted by the user requesting the feedback

from the system (through the ASR engine). The non-

anthropomorphic feedback was a textual equivalent

(based on the structure used in Gilly (1994))

appearing in a supplementary window next to the

main X-Window. A small set of typical tasks a

beginner might engage in, involving UNIX

commands, were designed. Since the users had no

knowledge of UNIX commands, they were obliged

to make use of the feedback if they wished to

complete the tasks. The two types of feedback were

randomly assigned to the tasks so that one task was

not tied to one type of feedback.

The results for this tightly controlled experiment,

which involved 55 users who were novices to UNIX

commands, were statistically significant. The users

were scored (scores used in hypothesis testing

statistical analysis) as they attempted a set of tasks

using UNIX commands. Scores were devised

according to the number of errors, hesitations and

completions/non-completions a user was able to

carry out. Further, scores were obtained via a

questionnaire for users’ opinions on the system

feedback given them.

The statistical results suggested the

anthropomorphic feedback to be more effective. The

scores obtained were approximately normally

distributed. These were then used in a t-test. The

results are in the Table 3.

Furthermore it was clear that users preferred the

anthropomorphic feedback. The actual scores from

the questionnaires using a Likert scale, where 1 was

a negative response and 9 was a positive response,

are detailed in Table 4.

Hence it was concluded that the statistically

significant results suggested the anthropomorphic

feedback to be more effective and preferred by

users. The users were able to carry out the tasks

more effectively with the anthropomorphic

feedback, whilst indicating a preference for the

anthropomorphic feedback.

2.3 Experiment Three

The third experiment by Murano (2003) was in the

context of software for online factual delivery.

Specifically the context for this area was direction

finding. Software was developed to give directions

to two different but equivalent locations

(equivalence was concerned with approximately

equal distances and difficulty), where the aim was

for test subjects to physically find their way to the

given locations. The subjects were to use the

directions given to them by the system. Hence it was

a prerequisite that the subjects should not have

known where the locations were before taking part

in the experiment (this was determined as part of a

questionnaire).

In this experiment anthropomorphic feedback

was compared with a non-anthropomorphic

equivalent. In this case the anthropomorphic

feedback consisted of dynamically loaded video

ICEIS 2005 - HUMAN-COMPUTER INTERACTION

Table 5: F-test Results - Diagram Vs. Video

Comparison of Diagram(Map) Vs Video

F-Observed 1.85

F-Critical (5%) 1.67

Table 6: Overall user preferences

Overall User Preferences

Mean Standard Deviation

Video 6.42 1.68

Diagram (Map) 6.74 1.62

clips of a person giving directions to a location. This

was compared with an equivalent non-

anthropomorphic feedback consisting of a map with

guiding text based on the principles found in

Southworth and Southworth (1982). One type of

feedback was not tied to one particular location in

the experiment. The feedback was rotated so that

each location had either type of feedback at some

point in the experiment.

The results for this tightly controlled experiment,

which involved 53 users, were statistically

significant (for effectiveness in favour of the map).

The users in the experiment were scored (scores

used in hypothesis testing statistical analysis)

according to the amount of mistakes they made (i.e.

wrong turnings taken), visible hesitations and if the

subjects actually reached the prescribed location.

Users were also given a questionnaire which allowed

them to express their opinions concerning the

feedbacks. The results were statistically significant

in favour of the map with guiding text feedback. The

users overall performed the tasks of direction finding

more effectively with the map and guiding text.

The actual data collected was found to be

approximately normally distributed and was used in

an F-test. The results are in Table 5, suggesting the

map to be more effective.

The opinions of the users concerning their

preferences were much less clear. The actual scores

from the questionnaires using a Likert scale, where 1

was a negative response and 9 was a positive

response, are detailed in Table 6.

As can be seen from the above table, the scores

for the opinions (overall user preferences) showed

the map to be only slightly better than the

anthropomorphic feedback. Many of the users liked

very much the idea of having ‘someone’ give them

directions rather than the map. This resulted in them

scoring the anthropomorphic feedback much higher

than expected.

2.4 Overall Discussion of

Experiments

The experiments summarised in the last three

sections show clearly that anthropomorphic

feedback is generally liked by users in most

situations. However the effectiveness of such

feedback is dependant on the domain of concern.

Hence certain domains appear to not be suited to

anthropomorphic feedback, such as the domain for

online factual delivery, particularly the direction

finding context. This is also confirmed by the

suggestion based on other research discussed in

Dehn and van Mulken (2000). However as the third

experiment showed, users still like seeing and

interacting with anthropomorphic feedback even if it

is not the best mode of feedback for them to achieve

their tasks.

These results suggest the conclusion that it

would be better for designers of feedback to include

anthropomorphic feedback in the domains shown to

be better suited to such a style. For the domains not

suited to anthropomorphism it clearly needs stating

that a suitable non-anthropomorphic feedback

should be used instead. However based on what

users like, it may be suitable to combine non-

anthropomorphic feedback with some form of

anthropomorphic feedback. An example based on

the third experiment described above is to have the

map with guiding text (which was more effective),

and to perhaps have a video or synthetic character of

a person giving some ‘external’ (not the actual

directions) information. ‘External’ information could

simply be to introduce the user to study the map

being presented to them. This would give the user

the benefit of anthropomorphism and the

effectiveness of the map with guiding text. This

suggestion may seem simple. However many

software packages have failed due to bad user

interfaces and feedback. Sometimes the problems

could have been resolved by fairly simple means.

Hence this suggestion is in line with the idea that

sometimes minor adjustments can dramatically

improve the usability of a system.

WHY ANTHROPOMORPHIC USER INTERFACE FEEDBACK CAN BE EFFECTIVE AND PREFERRED BY USERS

3 THE WORK OF REEVES AND

NASS APPLIED TO USER

INTERFACE FEEDBACK

As stated in the introduction, these issues really only

deal with the ‘how’. This means that it has been

discovered how we should give feedback in certain

domains, i.e. certain domains are better suited to

anthropomorphic feedback. However the issue of

‘why’ has not been addressed by the experiments,

i.e. why is it that in certain domains

anthropomorphic feedback is more effective and

preferred by users? The answer lies in us as humans.

Reeves and Nass (1996) have for many years

conducted research very compatible with the

research summarised in this paper. In their book

‘The Media Equation’ (1996), they have discussed

empirical findings which give us the answer to the

question posed in the previous paragraph. In this

large body of research they have found that people

in general (including computer scientists) tend to

interact with a computer in a social manner and in a

very similar manner to the way one interacts away

from a computer, i.e. with other people etc. Also

they have found that people apply the basic social

rules of every day life to their interaction with

computers. This is done automatically and

intuitively by humans. In fact they do not even

realise they are behaving in this manner.

These points are crucial to the findings of the

three experiments summarised above. The

suggestion here is that the subjects concerned were

subconsciously applying human social rules whilst

interacting with the feedbacks and because one of

the types of feedback (anthropomorphic) was more

compatible with the applying of social rules, the

results showed more effectiveness (in two of the

experiments) and very importantly high user

approval (in all three experiments).

This suggestion does not explain why the results

did not apply to one of the experiments (the

direction finding experiment). One explanation is

that whilst users would have been applying social

rules in all circumstances, the direction finding

experiment was much better suited to the map with

guiding text feedback. However the issue of user

approval for the anthropomorphic feedback in the

direction finding experiment does support the

findings that users will apply social rules whilst

interacting with computers.

This issue leads to other aspects of the research

by Reeves and Nass – the unique characteristics of

anthropomorphic feedback. There are various

characteristics which easily occur in

anthropomorphic feedback that are compatible with

the subconscious use of social rules. It is these

characteristics that account for the ‘why’ or the

reason for anthropomorphic feedback being more

effective in certain domains and being mostly

preferred by users.

One aspect concerns the fact that human-to-

human communication involves eye contact and it

has been shown in Ekman (1973) and Ekman et al

(1972) that if a person is looking at a ‘face’, about

half of the time used in this activity is used to look at

the eyes. Also if one matches modalities this usually

incurs a better response, e.g. if one sends an email to

a friend, usually the reply will be sent by email and

not by a telephone call, as stated by Reeves and Nass

(1996). Reeves and Nass (1996)argue that this

human phenomenon could work at the user interface

if one could overcome the obvious barriers to this,

e.g. if an electronic voice issues advice/information

to the user it would be better to have the system

accept input verbally from the user, via an ASR

engine. This is because communication modalities

are being matched and are closer to the human-to-

human social rules.

These two aspects are very important with

respect to the anthropomorphic feedback used in the

experiments. This is because as stated above the

anthropomorphic feedback consisted of dynamically

loaded video clips of a person. These clips showed

the face of the person clearly and within the limits of

the video one could see the eyes. This is very

important based on the material of Ekman (1973)

and Ekman et al (1972) because the users would

have been subconsciously looking at the face and

spending a good proportion of the time looking at

the eyes of the person in the video clips (whilst

listening to the help given). Furthermore the

communication modalities were well matched as the

user communicated with the system via the ASR

engine and clearly the anthropomorphic feedback

(video of human talking) was also communicating

verbally.

Another aspect that should be considered is the

way the experiments were conducted. Reeves and

Nass argue that when one is testing a product

presented by a computer the computer should not

ask the user for evaluations. If the evaluations are to

be done electronically it would be better to use a

different computer. Alternatively these would be

better conducted by some paper based means.

However caution would still need to be deployed

because if the person conducting the experiment was

also the person helping the user in some way, then

Reeves and Nass (1996) state that a subject may

subconsciously look for the most diplomatic

responses so as not to upset anyone. They suggest

that the best way to overcome these problems is to

test two ‘products’ against each other. In this way

ICEIS 2005 - HUMAN-COMPUTER INTERACTION

the subjects do not feel obliged to respond in some

socially acceptable manner.

The experiments were conducted in the manner

suggested by Reeves and Nass. In each case two

types of feedback were the basis of each experiment,

being tested against each other. The subjects were

asked for their evaluations on these for usability etc.

Furthermore the evaluations were carried out by the

subjects not in an electronic manner, but away from

the computer by means of carefully designed paper-

based questionnaires. Thus it is suggested that any

bias on the evaluation concerning the applying of

certain social rules should have been dramatically

reduced if not eliminated completely.

Another aspect requiring consideration is that

Reeves and Nass (1996) and Reeves at al (1992)

discovered that people tend to have similar reactions

with a picture of a person as they do with a real

person in front of them. When a person sees another

person that is near them, the human subconscious

result is that people will evaluate that person more

intensely, pay more attention to them and remember

them better. They found that these principles still

applied if one looked at a picture of a person. This is

important as it affects the way a person could view

some anthropomorphic feedback, either of a person

or some synthetic character.

The experiments conducted by the author had as

stated above anthropomorphic feedback consisting

of video clips of a person. These were filmed in such

a manner so as to follow the principles found by

Reeves and Nass. The person in the clips was seen to

be near to the person using the feedback. This was

achieved simply by filming the person from not too

far a distance. Also there were no large ‘open

spaces’ around the person being filmed. This

resulted in the person appearing quite close to the

user viewing the feedback. This filming strategy

would have resulted in users feeling a more intense

evaluation of the feedback along with enhanced

memory results and actually paying more attention

to the feedback during the session. It is suggested

that this would have resulted in the users basically

performing their tasks better and also evaluating the

feedback very positively.

Reeves and Nass (1996) also discuss the effects

of having ‘unnecessary peripheral motion’. They say

that having this in an interaction leads the user to be

distracted from their current attention giving activity

to the ‘item’ moving at some other position in the

screen or window. This clearly results in something

being ignored from the primary interaction.

This suggestion was put into practice for the

feedbacks. Simply no ‘peripheral motion’ was used

so that all the attention could be put onto the

feedback and the help being given for achieving the

tasks.

4 CONCLUSION

The issues discussed above provide a reasonable

explanation based on empirical findings concerning

the reasons for the anthropomorphic feedback being

more effective (in two of the three contexts tested)

and liked by users in all cases.

One aspect is that humans, whether they admit it

or not, behave in a social manner with computers,

applying various social rules as Reeves and Nass

found. This has been crucial to the effects observed

in the experiments summarised above.

The next important aspect as discussed above is

the fact that anthropomorphic feedback and the way

it is presented to users can very naturally provide

humans with the appropriate ‘cues’ for them to

behave in a more social manner towards the

feedback. This in turn results in better task

completions and a higher satisfaction rate in certain

contexts.

This work has also shown the validity of the

work by Reeves and Nass in this area as the findings

of the reported experiments corroborate some of the

findings by Reeves and Nass. This work also takes

the work of Reeves and Nass further as this work

has been conducted in a much more realistic set of

contexts compared to the contexts used by Reeves

and Nass.

Concerning interface designers it is suggested

that they should take seriously the use of

anthropomorphic feedback, as the suggestion is that

it leads to better more productive interactions and

more usable interfaces. This is significant as more

and more people who are not ‘professionals’ are

using computer systems and their software. This in

turn brings the requirement of developing better user

interfaces by using results such as the ones discussed

in this paper. This in turn could have a beneficial

effect to the profits of a software house. Clearly if

they can apply these principles they could

potentially attract a new market, particularly

composed of those who may be afraid of computers.

However despite the potential for applying these

findings immediately in a business context, there is

more work required. One of the experiments showed

that the anthropomorphic feedback was not as

effective as the non-anthropomorphic equivalent.

This leads to the requirement of investigating other

areas of user interface feedback to try and find other

domains not suited to this type of feedback.

Ultimately, a taxonomy of possible domains and

suitable types of feedback could be devised over

time. If this was available based on empirical

findings, user interface designers could be helped

when they are faced with the many decisions they

have to take when designing user interfaces.

WHY ANTHROPOMORPHIC USER INTERFACE FEEDBACK CAN BE EFFECTIVE AND PREFERRED BY USERS

A further area that would need investigating is

feedback in a virtual reality setting and also the very

different setting of performing two or more tasks at

once. An example would be a car driver driving a

car and trying to find directions to some location

(currently this is done with navigation software and

any future study should take into account issues such

as cognitive load and divided attention etc.).

Alternatively, investigating this type of feedback for

pilots could lead to some interesting findings and

perhaps a more comprehensive taxonomy of the kind

suggested in the previous paragraph.

ACKNOWLEDGMENTS

The School of Computing, Science and Engineering

at the University of Salford, Prof. Sunil Vadera,

Prof. Tim Ritchings and Prof. Yacine Rezgui are

thanked for their support.

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WHY ANTHROPOMORPHIC USER INTERFACE FEEDBACK CAN BE EFFECTIVE AND PREFERRED BY USERS