EFFECTIVENESS AND PREFERENCES OF
ANTHROPOMORPHIC USER INTERFACE FEEDBACK IN A PC
BUILDING CONTEXT AND COGNITIVE LOAD
Pietro Murano, Christopher Ede
University of Salford, Computer Science, Multimedia and Telecommunications
Centre for Virtual Environments, Newton Building, Gt. Manchester, M5 4WT, U.K.
Patrik O’Brian Holt
Interactive Systems Research Group, School of Computing, The Robert Gordon University
St. Andrew Street, Aberdeen, AB25 1HG, Scotland
Keywords: Anthropomorphism, User interface feedback, Evaluation, Cognitive Load.
Abstract: This paper describes an experiment and its results concerning research that has been going on for a number
of years in the area of anthropomorphic user interface feedback. The main aims of the research have been to
examine the effectiveness and user satisfaction of anthropomorphic feedback in various domains. The
results are of use to all interactive systems designers, particularly when dealing with issues of user interface
feedback design. Currently the work in the area of anthropomorphic feedback does not have any global
conclusions concerning its effectiveness and user satisfaction capabilities. This research is investigating
finding a way for reaching some global conclusions concerning this type of feedback. The experiment
detailed, concerns the specific software domain of software for in-depth learning in the specific context of
PC building. Anthropomorphic feedback was compared against an equivalent non-anthropomorphic
feedback. The results were not statistically significant to suggest one type of feedback was better than the
other. It was also the aim to examine the types of feedback in relation to Cognitive Load Theory. The results
suggest that the feedback types did not negatively affect Cognitive Load.
1 INTRODUCTION
The user interface is usually the most visible part of
a software application and is often one of the main
aspects that users really think about if they have a
problem or if they like something about the
application they are using. It is therefore very
important to strive to achieve as good quality a user
interface as possible as this will affect the users’
perceptions about a system. In the worst case, if the
user interface is unusable, the whole application
could be abandoned.
The global aim of this research is to constantly
discover better ways of developing user interfaces,
specifically the feedback that is given to users. The
research is directly concentrating on investigating
the effectiveness and user satisfaction of
anthropomorphic feedback. To achieve this direct
comparisons are being made with non-
anthropomorphic feedback in an experimental
setting. Furthermore, the authors of this paper are
also trying to explain the results of conducted
experiments in terms of appropriate theories. One
such theory that is being investigated in conjunction
with the experimental results is the Cognitive Load
Theory.
Anthropomorphism at the user interface usually
involves some part of the user interface, taking on
some human quality (De Angeli, Johnson, and
Coventry, 2001). Some examples include a synthetic
character acting as an assistant or a video clip of a
human.
1.1 Background Literature
Other researchers have conducted some
investigations into the area of anthropomorphic
181
Murano P., Ede C. and O’Brian Holt P. (2008).
EFFECTIVENESS AND PREFERENCES OF ANTHROPOMORPHIC USER INTERFACE FEEDBACK IN A PC BUILDING CONTEXT AND COGNITIVE
LOAD.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - HCI, pages 181-188
DOI: 10.5220/0001687301810188
Copyright
c
SciTePress
feedback, but the wide spread results of these efforts
do not reveal an overall global picture indicating if
such types of feedback are preferable (or not). One
of the earliest studies into anthropomorphism at the
user interface was by Quintanar, Crowell, Pryor and
Adamopoulos (1982). This was an experiment in a
quiz context which tested anthropomorphic textual
feedback and non-anthropomorphic textual
feedback. The quiz was about ‘psychology’ and the
participant sample was undergraduate students. The
main aims of the study were to obtain the user’s
thoughts about the system and ascertain the
effectiveness of the two types of feedback. One
finding of the study showed that participants
perceived the anthropomorphic feedback to be ‘more
human, less honest and slightly less courteous…’
compared with the non-anthropomorphic feedback.
Also Quintanar et al (1982) found the
anthropomorphic textual feedback to be more
effective. This was based on the fact that the quiz
scores were higher under the anthropomorphic
condition and the amount of time spent thinking
about the questions presented and the system’s
responses was higher. While this was an interesting
study, it did not address other forms of
anthropomorphism, such as synthetic characters or
video clips etc. Also some methodological aspects
could have caused some bias in the results. One such
issue concerns the recruitment of the participants.
The authors state that participants were screened for
their psychology knowledge, which is appropriate.
However they do not state how the different
experimental conditions were balanced in terms of
the participants’ psychology skills.
Also, Moreno, Mayer and Lester (2000) have
done some work in relation to anthropomorphic user
interfaces. Two experiments described in (Moreno et
al, 2000) looked at varying the kind of
communication an agent used towards the user. With
such variations they wanted to know if participants
in a certain experimental group could have ‘deeper
understanding’. They also wanted to discover if deep
understanding would be affected if using voice
compared to text. The first study varied the agents in
four different conditions in a botany context. The
first condition used a synthetic type agent able to
‘converse’. The second condition used only a
‘conversing’ agent (i.e. no image or animation). The
third condition used a synthetic type agent
communicating by means of text only. The fourth
condition used only text. The second study used the
same four conditions. However the synthetic agent
and its corresponding synthetic ‘voice’ were
replaced with a real human. Their results show that
the presence of an on screen image of an agent did
not significantly affect learning. However they
obtained significant results in favour of using a
voice to communicate information compared with
text. Their results suggest that using a voice helps to
improve learning. They found experimental
participants’ ability to remember and problem
solving skills to be improved. Also the voice agent
was rated more highly by experimental participants.
However some experimental design flaws suggest
that more work is required in this area. Their
publication (Moreno et al, 2000) does not detail
clearly enough if the participants were screened
properly for their prior botany knowledge. Some
screening did take place, but how this was done is
not detailed and could have biased the results if
some participants, e.g. happened to have more
botany knowledge in one condition. Also the paper
does not detail the level of difficulty of the botany
material used in the context of the experiment in
relation to the participants’ experience.
A further study concerning tutoring by
Moundridou and Virvou (2002) tested 2 conditions
in an algebra tutoring environment. The participants
were screened in advance for their mathematical
knowledge by means of a test and were deemed to
be approximately equivalent to one another. The
first experimental condition had a talking synthetic
face and the second was the same as the first
condition with text replacing the synthetic face. The
main results showed that there was no significant
difference between the 2 conditions for task time
completion. However the participants in the
anthropomorphic condition enjoyed the experience
more, found the system more useful and less
difficult to use. Lastly the participants were given a
post-experiment test and this did not reveal any
statistically significant difference between the
conditions in terms of overall results.
Further, the authors of this paper have been
investigating the appropriate use of anthropomorphic
feedback for some time and the results obtained are
not definitive with respect to obtaining global
knowledge in this area, e.g. (see Murano, 2005,
2003, 2002a, 2002b, 2001a and 2001b). However
related to the experiment reported in this paper, the
study by Murano (2002b) in the same domain of
online learning and the specific context of English
pronunciation, showed with significant results that
using an anthropomorphic feedback was more
effective and preferred by users. This in effect meant
that the anthropomorphic condition aided the
correction process more. This was an experiment
which used Italian participants with imperfect
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182
English pronunciation. Several tasks involving
pronunciation exercises were used where either an
anthropomorphic or non-anthropomorphic feedback
was used to assist in the correction process. The
anthropomorphic feedback consisted of a video of a
human and the non-anthropomorphic feedback
consisted of guiding text and a diagram.
2 PC BUILDING EXPERIMENT
2.1 Aims and Objectives
Therefore this paper investigates further the domain
of online learning. This time it is in the specific
context of PC building. The aim of this experiment
was to gather data regarding effectiveness and user
satisfaction in the PC building context. Specifically
the aim was to find out if anthropomorphic user
interface feedback fostered a better interaction
experience with fewer errors and therefore a better
task completion rate. It was also of interest to find
out if anthropomorphic user interface feedback led
to better user satisfaction.
The authors were also interested to find out if
cognitive load was a factor in some of the results
observed. This theory essentially argues that when
the overall cognitive load exceeds a particular
threshold, then activities such as learning are
impaired or become more difficult (Martin-
Michiellot and Mendelsohn, 2000). Furthermore
cognitive load has three basic strands. The first is
‘intrinsic cognitive load’. This concerns the activity
involved in learning some item of information and
typically how many units of information are being
learned at the same time. The second strand
concerns ‘extraneous cognitive load’. This has to do
with how learning materials are presented to a
human. Therefore the more complex the manner of
presentation is, the higher the extraneous cognitive
load tends to be (Martin-Michiellot and Mendelsohn,
2000). The third strand concerns ‘germane cognitive
load’. This strand has to do with the human faculty
of processing and understanding information, and
problem solving (Sweller, van Merrienboer and
Paas, 1998).
2.2 Users
All the participants taking part in the study were
of varied age groups.
30 participants were used in the experiment.
Although gender was not the main aspect under
consideration the sample used was
approximately 50:50 for gender.
The participants had varied occupations.
All the participants were novices to the area of
PC assembly. This ascertained by administering
a small pre-experiment test and only those with
low scores, (i.e. little knowledge about hardware
etc) were used.
2.3 Experimental Design
A between users design was used. The 30
participants were randomly assigned to one of the 2
conditions being tested –anthropomorphic or non-
anthropomorphic. Randomness was achieved by
alternately assigning a participant to one of the
conditions until all 30 had been assigned.
2.4 Variables
The independent variables were the types of
feedback, i.e.:
Textual instructions.
MS Agent synthetic character.
The dependent variables were the participants
performance in carrying out the tasks and their
subjective opinions.
The dependent measures were that the
performance was measured by counting the number
of errors made as each participant attempted to
assemble the components, whether the participant
completed a task and the time taken to complete a
task. These factors were then used in a scoring
formula in order to achieve a single score per
participant (see note below). The formula was
devised so as to allow groups of similar types of
errors to be catalogued under a dedicated category.
The errors were determined by the experimenter
physically examining the way the components had
been inserted into the PC case (see note below). The
time taken to complete the tasks and the number of
times a participant clicked a certain button in the
application were recoded automatically by the
application. The subjective opinions were measured
by means of a post-experiment questionnaire
(Appendix 23).
(NOTE – The formula used was as follows:
Each participant (unknown to them) was
started on 5 points for each task.
For a completed task with 1 minor error, 1
point was deducted. A minor error was of
the kind that led to a device working, but
still having some problem, e.g. not securing
the CD ROM drive to the case with screws.
EFFECTIVENESS AND PREFERENCES OF ANTHROPOMORPHIC USER INTERFACE FEEDBACK IN A PC
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183
For a completed task with 1 major error, 2
points were deducted. A major error was of
the kind leading to a device not working
but essentially still being fitted in place,
e.g. fitting the CD ROM drive
appropriately but not being able to insert
the required power cable.
For a completed task with 1 severe error, 3
points were deducted. A severe error was 2
or more major and minor errors e.g. not
securing the CD ROM drive to the case
with screws was a minor error and not
being able to insert the required power
cable was a major error.
For an unsuccessful attempt at completing a
task, 4 points were deducted, e.g. a
participant tried to do a task, but then gave
up or a participant tried to do a task, but
made 2 or more severe errors.
If no attempt at all was made for a task, all
5 points were deducted to leave a score of
0.)
2.5 Apparatus and Materials
A PC running Windows XP with 256 Mb RAM.
Microsoft Agent 2.0 ActiveX component.
Lernout and Hauspie TruVoice Text-To-Speech
engine. The prototype was engineered with
VB.NET.
External speakers.
Desktop microphone used for the
anthropomorphic condition.
An open PC case with the motherboard,
processor and power supply already assembled.
Disassembled RAM board, CD ROM drive,
ribbon cable and relevant assembly screws.
Screw driver.
2.6 Procedure and Tasks
The first step was to recruit a suitable number of
participants particularly meeting the requirement of
being novices to the assembly of computer
hardware. As stated above this was achieved by the
participants completing a pre-experiment
questionnaire and a small pre-experiment test
covering basic knowledge of PC components (only
participants with a low score in the pre-experiment
test were used – a high score would have indicated
too much knowledge for the experiment). Each
participant was briefed with the following points
before commencing the actual experiment:
The software was developed for evaluating its
suitability to teaching about PC building.
Help could be received from the system by using
the help button (non-anthropomorphic group) or
by asking for help via the microphone
(anthropomorphic group).
The information for each stage would be shown
for a limited period of time. If the information
was to be repeated, this could be achieved by
pressing the ‘repeat’ button (non-
anthropomorphic group) or by asking for the
information to be repeated via the microphone
(anthropomorphic group).
Video demonstrations were available for viewing
concerning the assembly of each part.
It would not be possible to backtrack to a
previous stage on screen.
A post-experiment questionnaire would need to
be completed at the end of the experiment.
Then the procedure described below was carried
out in the same way for all participants using the
same environment, equipment and
questionnaires/observation protocols. Each
participant was treated in the same manner. This was
all in an effort to control any confounding variables.
There were 2 tasks involving PC building.
Specifically, the first task concerned inserting a
RAM board into its appropriate slot in the
motherboard. The second task involved assembling a
CD ROM drive into the case, with a correct master
jumper setting and connecting the necessary cables
and screws.
Each participant was booked an appointment
during the day. The experiment took about 30
minutes to complete per volunteer. After completing
the initial pre-experiment questionnaire and test,
participants were able to view the PC case and
motherboard information screens. This allowed the
participant to become familiar with the software
before taking part in the assembly tasks.
Then for the first task, the participant was
directed to press the ‘Random Access Memory
Walkthrough button’. This would then initiate a
series of steps, with accompanying photographs of
the parts etc, describing what had to be done to
complete the task. For the anthropomorphic
condition, the Merlin character would narrate the
various aspects required and where relevant would
move on the screen and ‘point’ to certain elements
on the photographs. For the non-anthropomorphic
condition the same content was delivered textually
and to match the Merlin character’s movement and
pointing to various elements on the photographs,
clearly visible arrows were used to ‘point’ to the
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same elements on the screen. The textual
information was displayed on the screen for the
same amount of time as the Merlin character took to
narrate the information. However the participants in
each condition had the option of having the
information repeated whilst within a particular
information stage.
Once the information had been received the
participant would attempt to physically insert the
RAM into the appropriate slot. Although the actual
information regarding the RAM board could not be
viewed again at this stage, the participant did have
the software’s help available. Also the information
regarding the PC case and motherboard which had
been made available before starting the first task of
inserting the RAM was available at this stage. Lastly
the relevant video demonstration was playable by
the participant at this stage. Once the first task was
completed, the second task was undertaken in the
same manner as described above.
At all times during the experiment the
participants were informally observed and at the end
of a task, an inspection of the relevant components
and their positioning etc. was carried out by the
experimenter – any errors being noted.
Lastly the participants were asked to complete a
post-experiment questionnaire regarding their
subjective opinions of their experience with the
software.
2.7 Results
The data collected was analysed using MANOVA
analysis. Regarding effectiveness, the amount of
time taken for each task was recorded, the number of
errors committed were recorded and also if a task
was completed was recorded. For user satisfaction,
various subjective opinions were elicited from the
participants.
As discussed in section 2.4 above, the factors of
task completion and errors were used in a scoring
formula. The results for the first task involving the
RAM board are presented below in Table 2.1. The
analysis involved the actual scores obtained, the
experimental group, the age groups of the
participants and the gender of the participants.
The F-ratio of 1.14 shows that there are no
significant differences between the various factors
analysed.
Also the same analysis was carried out for the
second task involving the fitting of a CD ROM
drive. This is shown in Table 2.2 below. The
analysis involved the actual scores obtained, the
experimental group, the age groups of the
participants and the gender of the participants.
Table 2.1: Analysis of Variance, Task 1, Overall Score.
Source DF Sum of
Squares
Mean Square F Ratio
Model 5 5.161467 1.03229 1.1362
Error 24 21.805200 0.90855 Prob > F
C. Total 29 26.966667 0.3685
Table 2.2: Analysis of Variance, Task 2, Overall Score.
Source DF Sum of
Squares
Mean Square F Ratio
Model 5 10.994271 2.19885 1.8008
Error 24 29.305729 1.22107 Prob > F
C. Total 29 40.300000 0.1508
Table 2.3: Analysis of Variance, Task 1, Times.
Source DF Sum of
Squares
Mean Square F Ratio
Model 5 67156.03 13431.2 2.4924
Error 24 129330.63 5388.8 Prob > F
C. Total 29 196486.67 0.0593
Table 2.4: LSMeans Differences Student's t.
Alpha = 0.050 t = 2.0639LSMean[i] By LSMean[j].
Mean[i]-Mean[j]
Std Err Dif
Lower CL Dif
Upper CL Dif
female male
female 0
0
0
0
82.7179
26.9818
27.0302
138.406
male -82.718
26.9818
-138.41
-27.03
0
0
0
0
Level Least Sq Mean
female A 387.47313
male B 304.75522
Levels not connected by same letter are significantly different.
Table 2.5: Analysis of Variance, Task 2, Times.
Source DF Sum of
Squares
Mean Square F Ratio
Model 5 54857.62 10971.5 0.6896
Error 24 381815.34 15909.0 Prob > F
C. Total 29 436672.97 0.6361
As with the previous task, the F-ratio of 1.80
shows that there are no significant differences
between the various factors analysed.
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The tasks were also timed and included in the
analysis. For the first task (RAM Board insertion)
the analysis involved the actual times obtained, the
experimental group, the age groups of the
participants and the gender of the participants. This
is shown in table 2.3 above.
The F-ratio of 2.49* is tending towards
significance (p < 0.05). The initial results were then
subjected to post-hoc testing using a t-test, where
significance was shown for the gender, where
female participants were significantly slower than
the male participants. This can be seen in Table 2.4
above.
For the second task (CD ROM fitting) the
analysis also involved the actual times obtained, the
experimental group, the age groups of the
participants and the gender of the participants. This
is shown in table 2.5 above.
The F-ratio for the task involving the times
shows no significance for the various factors being
analysed.
Regarding the participant subjective responses
various aspects were considered. The main aspect of
interest concerns the application being considered
helpful for PC assembly. This was in relation to the
Table 2.6: Analysis of Variance, Application Considered
Helpful.
Source DF Sum of
Squares
Mean Square F Ratio
Model 5 6.979297 1.39586 2.1175
Error 24 15.820703 0.65920 Prob > F
C. Total 29 22.800000 0.0981
Table 2.7: LSMeans Differences Student's t.
Alpha = 0.050, t = 2.0639LSMean[i] By LSMean[j].
Mean[i]-Mean[j]
Std Err Dif
Lower CL Dif
Upper CL Dif
Anthropomorphic Non-
Anthropomorphic
Anthropomorphic 0
0
0
0
0.81598
0.32733
0.1404
1.49157
Non-
Anthropomorphic
-0.816
0.32733
-1.4916
-0.1404
0
0
0
0
Level Least Sq Mean
Anthropmorphic A 7.7819451
Non-Anthropomorphic B 6.9659605
Levels not connected by same letter are significantly different.
Table 2.8: Analysis of Variance, Application Frustrating
to Use.
Source DF Sum of
Squares
Mean Square F Ratio
Model 5 18.272845 3.65457 3.3699
Error 24 26.027155 1.08446 Prob > F
C. Total 29 44.300000 0.0191
experimental group, age group and gender. This is
shown in table 2.6 above.
Strictly the F-ratio of 2.12 is only approaching
significance (p < 0.05). However the initial results
were then subjected to post-hoc testing using a t-test,
where significance was shown for the experimental
group, where the anthropomorphic group
significantly considered the application more helpful
than their counterparts in the non-anthropomorphic
group. This can be seen in Table 2.7 above.
A further aspect of interest concerns the
application being frustrating to use. This was in
relation to the experimental group, age group and
gender. This is shown in table 2.8 above.
This result is significant (p < 0.05) with an F-
ratio of 3.37*, particularly in relation to the
experimental groups. The non-anthropomorphic
group significantly rated the application as more
frustrating to use compared to their counterparts in
the other anthropomorphic condition.
2.8 Conclusions
As can be seen in the previous section various
aspects were statistically analysed. Regarding the
effectiveness issues, no statistical significance can
be seen in the scores of the two groups which
included the task completion successes and the
errors made by participants.
The times were also analysed and for the first
task, there was no statistical significance for the
experimental group. However there was statistical
significance to show that the female participants
were slower than the male participants. Although
this research is not primarily about gender, it would
have been interesting to know why this result is in
this direction. Unfortunately none of the female (or
any of the participants) participants were available
again for interview. Also the initial demographic
data that was collected as part of the recruitment
process does not reveal any information that could
enlighten the authors on the matter. However the
authors believe that it is possible that something in
the female group’s background could shed light on
the matter. Further, for the second task, there is no
statistical significance for the time taken to complete
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the task. This is for the experimental group and the
gender.
The user preference issues analysed suggest that
overall the preferences tended towards the
anthropomorphic condition. Participants in the
anthropomorphic condition rated the application as
more helpful than the participants in the non-
anthropomorphic condition. Also the non-
anthropomorphic group significantly rated the
application as more frustrating to use than the
anthropomorphic condition participants. Despite the
statistical analysis indicating a user preference
towards the anthropomorphic feedback, the authors
suggest caution in categorically declaring the
anthropomorphic feedback as being more satisfying
to use. This is because as described in section 2.6
above, the non-anthropomorphic condition had the
feature incorporated where the textual information
remained on the screen only for a certain amount of
time. This aspect was designed into the system to
more closely match the fact that the
anthropomorphic feedback condition, consisting of a
character with accompanying speech bubbles, only
appeared on the screen for the time it took the
character to utter the information. In essence the
authors did not want the non-anthropomorphic
condition participants having an unfair advantage by
having the information available to them for a much
longer period of time. While it is argued that this
feature should have balanced the two conditions
more closely, the authors suggest that this
potentially incurred the side effect of participants in
the non-anthropomorphic group rating the
helpfulness of the application significantly lower
than the other participants in the other experimental
group and rating the application as significantly
more frustrating to use.
Comparing the results for this experiment with
the results of previous work carried out by Murano
(2002b), it is clear that the effectiveness issues do
not match because in the English pronunciation
context the anthropomorphic feedback was more
effective, while in the experiment detailed above
there were no significant differences in the two
tested conditions. The only slight agreement
between the two experiments concerns the
subjective opinions of the participants. In both cases
it appears that participants tend to prefer the
anthropomorphic feedback given the similar domain
and contexts being considered. However as argued
in the previous paragraph, the subjective responses
for the experiment described above need to be
tempered with the knowledge that the attempts at
having well balanced experimental conditions may
have caused more negative opinions being given
towards the non-anthropomorphic feedback.
3 THE EXPERIMENT AND
COGNITIVE LOAD
As stated in the introduction of the paper, it was also
of interest to know if cognitive load could have been
a factor somewhere in the conditions and user
interface being tested. Informally, the fact that there
were no significant differences in effectiveness
could indicate that the cognitive load was equivalent
under both conditions. However the following
discussion in relation to cognitive load should help
to make things clear.
Firstly the participants were definitely beginners
to PC assembly as indicated by the recruitment
process. This characteristic could mean there could
have been some intrinsic cognitive load as they were
all learning something new to them with a few units
of information. However this would have spanned
both conditions and not been affected by the
feedbacks being tested, so therefore should have
been the same under both conditions. Regarding the
possibility of extraneous cognitive load, the very
nature of the character moving on the screen could
have increased this form of cognitive load, as
perhaps more integration on the part of the user
would have been needed, compared with the static
textual explanations which were definitely in
integrated format. The possibility of one condition
incurring a higher germane cognitive load is
unlikely, as the content of the information was the
same in both conditions. Also one could argue that
the textual information was static and perhaps gave
more advantage to the user compared to the Merlin
character’s speech and speech bubbles. However this
was not the case as the textual information was not
on the screen all of the time as discussed above.
Therefore if cognitive load issues had been at play, it
would have been expected that participants in the
non-anthropomorphic condition should have overall
had significantly more success in the task – this was
not the case though. Lastly this experiment timed the
participants whilst they did the tasks and generally
no significant result was observed between the
conditions (except for female participants). The
reason this is also of interest is that cognitive load
has been linked to task completion times as an
indicator of increased cognitive load (Neerincx, van
den Dobbelsteen, Grootjen and van Veenendaal,
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187
2003 and Wang, Kaufman, Mendoca, Seol, Johnson
and Cimino, 2002).
It can therefore be argued that the two conditions
being tested were approximately equivalent in terms
of cognitive load. As stated, if cognitive load had
been a significant factor in one of the feedbacks, it
would have been expected that the performance of
participants in the anthropomorphic condition should
have had worse scores and worse overall times for
task completion.
3.1 Overall Conclusions
As can be seen by the results of this experiment and
also the previous work briefly considered in this
paper, there is still work to be done in this area to try
and determine the suitability of such types of
feedback. More work is being carried out. Results
are being further statistically analysed and also
analysed in terms of other theories of cognition and
human processing.
ACKNOWLEDGEMENTS
The authors would like to thank for their support:
The School of Computing Science and Engineering,
University of Salford. Heriot-Watt University,
Edinburgh, Dept. of Computer Science. The Robert
Gordon University, Aberdeen, School of
Computing.
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