Beverly L. Harrison (1,3), Hiroshi Ishii (2), Kim J. Vicente (1), and William A. S. Buxton (3,4)
(1) Dept. of Industrial Engineering
University of Toronto
Toronto, Ontario, Canada
M5S 1A4
beverly@dgp.utoronto.ca
benfica@ie.utoronto.ca
(2) NTT Human Interface Lab
1-2356 Take, Yokosuka-Shi
Kanagawa, 238-03 Japan
ishii@chi.xerox.com
(3) Alias Research Ltd.
110 Richmond St. East
Toronto, Ontario, Canada
M5C 1P1
(4) Dept. of Computer Science
University of Toronto
Toronto, Ontario, Canada
M5S 1A4
buxton@dgp.utoronto.ca
This paper describes a new research program investigating graphical user
interfaces from an attentional perspective (as opposed to a more traditional
visual perception approach). The central research issue is how we can better
support both focusing attention on a single interface object (without
distraction from other objects) and dividing or time sharing attention between
multiple objects (to preserve context or global awareness). This attentional
trade-off seems to be a central but as yet comparatively ignored issue in many
interface designs. To this end, this paper proposes a framework for
classifying and evaluating user interfaces with semi-transparent
windows, menus, dialogue boxes, screens, or other objects. Semi-transparency
fits into a more general proposed display design space of "layered" interface
objects. We outline the design space, task space, and attentional issues which
motivated our research. Our investigation is comprised of both empirical
evaluation and more realistic application usage. This paper reports on the
empirical results and summarizes some of the application findings.
This paper describes results from an experiment used to evaluate transparent
user interfaces against a proposed attentional model. The central research
issue is how we can better support both focusing attention on a single
interface object (without distraction from other objects) and dividing or time
sharing attention between multiple objects (to preserve context or awareness).
The technological problem addressed by transparent interfaces is that of
screen size constraints. Limited screen real estate combined with graphical
interface design has
resulted in systems with a proliferation of overlapping windows, menus, dialog
boxes, and tool palettes. It is not feasible to "tile" computer workspaces to
facilitate keeping track of things. There are too many objects. Overlapping
opaque objects obscure portions of information we may need to see and therefore
may also be undesirable. Transparent interfaces address these issues, but may
also introduce new challenges for designers.
The associated psychological problem we are addressing is that of
focused and divided attention. When there are multiple sources of information
we must make choices about what to attend to and when. At times, we need to
focus our attention exclusively on a single item without interference from
other items. At other times, we may need to time share or divide our attention
between two (or more) items of interest. In this case, we rapidly switch
attention back and forth between the items (necessitating minimal "switching
costs"). There is a trade-off between these attentional requirements (depicted
in Figure 4).
The need for focused or divided attention is largely determined by the
demands of the user's task. However, our ability to successfully focus
or divide (share) attention can be enhanced or degraded by the display design
choices we make. For example, opaque overlapping window designs are
problematic for divided attention (some information cannot be seen) but
facilitate focused attention (the hidden background window cannot create visual
interference). The interaction between the task characteristics and the design
characteristics determine the attentional requirements and performance (Figure
1).
Task characteristics largely determine attentional requirements and minimum
acceptable performance levels. These task characteristics are pre-determined
based on the nature of the work. Design characteristics (e.g., level of
transparency) facilitate or prevent the task goals from being attained,
limiting attentional performance. Our approach is: given an understanding of
the task, can we manipulate the design characteristics to produce the necessary
attentional performance?
Several key design issues need to be investigated if users are expected to
focus on or divide attention between two superimposed images. Can users
selectively attend to a chosen "layer" without visual interference from the
other? Are there certain display characteristics or task properties which
facilitate or preclude overlapping displays? How do these design choices
affect attentional performance?
The small amount of display real estate available relative to the amount of
data to be displayed presents a real challenge to user interface design. To
date, two main strategies have been applied to the problem. In the first, the
screen is partitioned, or tiled, into a number of non-overlapping
windows. This we refer to as the space multiplexed strategy. In the
second, windows lie on top of one another. Only the top one is visible at any
given time, but a mechanism is provided to rapidly change which window is
visible (temporal sequencing). This we refer to as the time multiplexed
strategy. Most frequently, a hybrid of the two is used. What we propose in
this paper, however, is a third strategy. Through the use of transparency in
the background of windows, the contents of windows underneath others is
visible, or at least partially visible. This "new" strategy we refer to as
depth multiplexing.
On the one hand, the depth multiplexing approach offers the best of both
worlds: windows need not be tiled to be visible. Hence, ideally, less
information is obscured. On the other hand, the potential for content of one
window interfering with another above or below it is introduced. Our
prototypes show clearly that in some situations the technique works well, while
in others there are real problems. The objective of our research agenda, of
which the current paper is a part, is to develop a more formal understanding of
the constraints of such an approach.
We propose a design space that captures the above three strategies and applies,
in general, to foreground and background interface layers (Figure 2 and Figure
3). This design space allows us to methodically categorize and investigate
both existing technologies and more novel technologies.
In one dimension (upon which this paper focuses), we vary the level of
transparency/opacity between the two displays. Fully opaque objects
reflect traditional window, palette, and menu design in current graphical user
interfaces. Fully transparent designs reflect some of the more advanced
interfaces such as those used in Heads Up Displays (HUDs) in aviation [12, 18]
or in the Clearboard system [5]. In HUD design, aircraft instrumentation (a
graphical computer interface) is superimposed on the external real world scene,
using specially engineered windshields. In the Clearboard work, a large
drawing surface is overlayed on a video image of the user's collaborative
partner. Semi-transparent designs include such things as video overlays
(like those used in presenting sports scores while the game is playing), "3-D
silk cursors" [19] or Toolglass-like tool palettes [2,7].
Along another dimension we can vary the perceived depth of the planes between
two displays, where one image appears closer to the user while the other is in
the background. This can be accomplished using half-silvered mirrors,
polarizing filters, or special transparent LCD displays (creating binocular
disparity or stereopsis). In this case, the user looks through the display
presented in the foreground to see the display presented in the background
(e.g., [10]). Layers on this axis are distinguished by both transparency and
depth. There are limited examples of such systems. Knowlton [9] used
graphical overlays projected downwards onto half-silvered mirrors over blank
keyboard keys to dynamically re-label buttons and functions keys (e.g., for
telephone operators). Schmandt [16] built a system to allow users to manually
manipulate and interact with objects in a 3-D computer space using a 3-D wand.
Again a half-silvered mirror was used to project the computer space over the
user's hand(s) and the input device. Disney has also developed a product
called the "ImaginEasel" for animators and artists. ImaginEasel keeps the
user's hand and input device in the workspace (using mirrors).
The proposed design space provides us with a means of categorizing both
existing technologies and new technologies. However, the utility of any
particular design will depend upon how well it supports the task
characteristics and goals.
A number of situations arise as part of our day-to-day work which require us to
focus or divide our attention. A number of such situations are outlined below,
reflecting the diverse range of possible applications.
Focused attention examples:
Divided attention examples:
These situations all share a common attentional problem: we need to be visually
aware of multiple objects which overlap and obscure each other. All of these
scenarios have two (or more) "tasks". In some cases we wish to time-share the
two tasks (divided attention), while in other cases we selectively attend to
one task excluding the other (focused attention). By their very nature, many
of the proposed task pairs have an implicit active and passive task, We need a
peripheral awareness of the passive task while we temporarily divert
most of our attention to the active ask. The extent of this awareness
determines the extent to which we must divide or focus our attention. We also
must consider the visual contents and distinctiveness of the two layers within
the task. How similar are they? What is the information density and level of
detail of each? This determines how much interference may result when we focus
our attention on one object. These characteristics may be unique for each
task. A detailed task analysis is required to determine them (and hence the
appropriateness of transparent design solutions within a particular domain).
We are concerned with three critical attentional components: the ability to
divide attention between two items, the ability to separate the visual
characteristics of each source and focus on any single item with minimal
interference from other items, and the switching cost (time, mechanism,
learning, awareness) of shifting attention from one item to another.
To facilitate focused attention (ignoring information from the background layer
while focusing on the foreground) we want to make the attributes of the
information on foreground objects as different from the background as possible.
We also wish to reduce the visibility of the background objects. This will
minimize interference. By contrast, for divided attention (being able to see
both foreground and background layers), we need to support simultaneous
visibility of both layers. However, the user must still be able to separate
which features belong to the foreground and which to the background in order to
accurately perceive the objects.
There are many ways of achieving differentiation between layers (with varying
success), such as different colors, content attributes - analog (images or
graphics) versus verbal (text based), font sizes or styles, etc. Many of these
features are pre-determined by the task. The level of transparency effects
visibility of the background. Low degrees of transparency (more opaque)
distinguishes the appearance of the foreground and background object, allowing
the user to easily focus attention on the foreground. For divided attention, a
high degree of transparency is desirable to support higher visibility of both
layers.
Clearly there is a trade-off between these two goals. We need to support this
trade-off since most real world jobs require both focused and divided
attention. We have characterized the trade-off in Figure 2 which provides a
framework for this research. We have used level of transparency as the
visibility control variable. From this analysis, we can predict that the
optimal degree of transparency is determined by the trade-off of supporting
both focused and divided attention. As degree of transparency increases, it
gets easier to divide attention between information on the top object and
information on the background object but more difficult to focus attention on
either object exclusively. The optimal transparency (OT) is a result of a
trade-off. The curves and the location of optimal transparency in the figure
are hypothetical but may reveal the trend. The non-linear nature of the curves
is also proposed but appears to be supported from our preliminary experimental
work.
Research in selective and divided attention, selective looking, and display
design suggest that transparency is a promising method of presenting foreground
and background layered information.
Kohler [11] originally investigated selective looking (monitoring dual tasks)
by building headgear using half-silvered mirrors which presented the scene of
the world in front of him superimposed on the scene of the world behind him.
He reported that he could easily switch between these two views; the unattended
scene seemed to "disappear" from sight.
Motivated by this work, further studies were carried out [15, 1] using two
superimposed video images presented on a single monitor. In the first study
[15] the tasks were visually distinctive: a hand slapping game and a ball
tossing game. In the later study [1] both tasks were visually similar ball
tossing games; the tasks were differentiated by the color of the shirts worn by
the players. In both cases, subjects were asked to monitor one task and
indicate the irregular occurrence of target events in this task. Meanwhile,
bizarre events were sporadically presented in the non-monitored task. Subjects
were easily able to monitor the target task to the exclusion of the unattended
task. Subjects did not notice the bizarre events, even when the experiment was
stopped during or immediately after the bizarre event occurred and the subjects
were asked about it. This result still held when the bizarre event was
presented in the exact same visual location where the target event occurred
(i.e., within foveal range). This seems to indicate that the intentionally
unobserved task goes virtually unnoticed. A number of alternative explanations
for this phenomenon were discussed and discounted. This work suggests that two
superimposed video tasks can be easily monitored with minimal
interference. However, the extent of simultaneous task awareness is unclear.
Similar results in selective looking have been found in studies of dual task
monitoring in Heads Up Displays typically used in aircraft control and
navigation tasks. Specific advantages cited include improved flight
performance, superior object tracking, [12, 18]. The primary disadvantage is
"attentional tunneling" - fixation on the HUD to the exclusion of events
in the real world, particularly unexpected events (or vise-versa) [18]. Again
subjects are easily able to differentiate either display layer easily.
Practice seems to improve simultaneous monitoring performance.
This previous research, though not applied directly to graphical user interface
design per se, suggests promising evidence for the use of superimposed
transparent displays. Based on these results, one would anticipate reduced
switching time and improved awareness by minimizing head and eye movement and
re-focusing. Also, one can reasonably anticipate that users will be able to
treat the sources separately and voluntarily attend to one or the other (with
varying degrees of interference).
As in most interface designs, one can anticipate some inappropriate
applications and pitfalls as well. In cases where "missed observations" have a
high cost, reducing visibility through transparency might be undesirable. Also
if both tasks must be simultaneously monitored and both have high attentional
demands, the attentional tunneling problems might arise. Finally, while this
would seem feasible for distinctive types of information, we must evaluate how
well this technique works for visually similar information types.
We are taking two complementary approaches to study transparent designs:
formal experiments and realistic field studies. This paper emphasizes our
empirical results.
To reveal how focused and divided attention changes, i.e. how the curves in
Figure 4 are shaped, we are conducting formal experimental studies with
well controlled models and simulations. By varying the degree of
semi-transparency in between the two layers, the experimental results provide
us with precise performance measures on how well the user can see both
foreground and background information and on how high the interference is
between the two "layers".
However, we realize that controlled experimental paradigms address a restricted
set of design dimensions only. Real applications consist of a much richer task
space. We have also developed several prototype systems which are more
representative of real world applications. We are evaluating these systems and
observing user behavior to gain further insights into the design of transparent
user interfaces. This combined research program allows us to further formulate
research issues while remaining confident that our research results have
external (real-world) validity. The two approaches are conducted in parallel.
Our first set of formal experiments used a very simple but robust task to
measure interference between two layers called the Stroop Effect [17]. In
traditional Stroop tasks, a series of words are presented in randomly chosen
colors (e.g., red, green, blue, yellow). Subjects must name the ink
color while ignoring the word. Some of the words are neutral (e.g.,
uncle, shoe, cute, nail); other words are the names of conflicting colors
(e.g., yellow, blue, green, red). Consistent, significant performance
degradation occurs when conflicting color words are used and subjects attempt
to name the color of the ink (e.g., the word "red" appears in green ink; the
correct response is green). In later studies (e.g., [8]), a consistent and
significant Stroop Effect was found even when the word was printed in black
ink, presented adjacent to a color bar. It is virtually impossible to
consciously block or prevent the Stroop Effect in selective looking tasks,
despite numerous experimental permutations (over 700 articles - for reviews see
[6, 13]).
Our experiments test how varying transparency effects interference
between the displayed word and the color target, using a traditional Stroop
test. The Stroop test was used to evaluate interference because it provides an
sensitive, extreme measure of the extent of interference. As such, it should
suggest worst case limitations. In our experiment, the word is seen by looking
"through" the color patch. At high levels of transparency (e.g., 100% - clear)
we anticipate that users will experience high levels of interference from the
word when they try to name the color (difficulty in focused attention). As the
color patch becomes more opaque the interference from the word should decrease
(making focused attention easier). This would support the focused attention
curve in Figure 4.
We used the word naming component of the Stroop Test to test the divided
attention curve proposed. In this case users are asked to ignore the color
patch and read the word in the background layer. This experiment reflects more
of a legibility test, necessary for divided attention. The color patch in the
foreground is always clearly visible and perceived. By reading the background
word the user is, in effect, creating a divided attention task. At high levels
of transparency (e.g., 100% - clear) it should be very easy to read the
background word (divided attention is easy). At more lower levels (opacity
increases) it should become progressively more difficult or impossible to read
the word (loss of ability to divide attention).
When combined, results from the two experiments suggest interface design
parameters where interference is minimized and the word is still fairly legible
(awareness is preserved).
H1: As transparency level increases (i.e., the word is more visible through the
color patch) the response time and errors will be unchanged in the color naming
task.
We anticipate more interference as transparency increases and therefore reduced
performance as shown in Figure 4. Furthermore we anticipate a leveling-off
point where performance does not continue to degrade.
H2: As transparency increases the response time and errors will be unchanged
for the word naming task.
We anticipate that as transparency increases the word gets easier to see and is
therefore faster and more accurate to read.
We used 4 colors: red, blue, green, and yellow. Words (helvetica, 78 point,
uppercase) appeared "through" the colored rectangular patch. We used neutral
words UNCLE, NAIL, CUTE, and FOOD in addition to the four color names.
Transparency levels were varied as: 0% (baseline condition - only one of the
word or color shows), 5%, 10%, 20%, 50%, 100% (clear - both the word and color
show). Task order (color naming versus word naming) was counter-balanced and
spaced one day apart. No cross task interference is anticipated [14]. The
word naming experiment baseline condition was a word only - presented with no
color patch. The color naming experiment baseline condition was a color patch
only - presented with no word. There were no other differences between the two
experiments. (The word naming experiment should not have any Stroop effects
but performance should be affected by the visibility of the word.)
A fully randomized, within subject, repeated measures design was used. There
were 4 conditions: non-conflict or neutral (the word was a neutral word),
incongruent color (a conflicting color word was present), congruent color (the
color word matched the color of the patch), and baseline (color or word only).
Transparency levels of 0%, 5%, 10%, 20%, 50%, 100% were used for all word-color
combinations for a total of 180 unique combinations. For each of 16 subjects,
three sequences of the entire set of 180 images were shown. Trials were
presented in random order at 5 second intervals. Each experiment lasted about
45 minutes. Verbal responses were logged within 1 msec of accuracy. Errors in
response were recorded. Error trials were removed from subsequent analysis of
response times.
The experiments were run using the PsyScope software and hardware [3] with a
headset microphone on a Macintosh IIfx. Audio levels were adjusted before each
subject was run. Subjects sat at a fixed distance of 100cm from the screen.
All sessions were video taped.
Subjects were given 20 practice trials. These trials were randomly selected
from the set of 180 possible combinations. Following this, subjects were shown
three sequences 180 combinations (15 minutes per set), with rest breaks in
between each set.
Subjects were debriefed at the end of the experiment. Open ended comments were
recorded and the experiment was video and audio taped for analysis purposes.
Response times and errors were logged by the computer.
A total of 16 students from the University of Toronto were run as subjects
They were pre-screened for color-blindness. Subjects were paid for their
participation and could voluntarily withdraw without penalty at any time.
A univariate repeated measures ANOVA was carried out on the data. As
hypothesized, significant main effects were found for transparency F(5,
719)=11.12, p< .0001 and word type F(3, 719)= 36.19, p < .0001. This
suggests that the Stroop Effect was present and that transparency may indeed
dilute the interference. Not surprisingly, color also showed a significant
main effect F(3, 719)=15.51, p < .0001, suggesting that saturation or
luminance might dilute the interference (i.e., affects word legibility - see
below). There were no significant interaction effects across factors.
Post-hoc analyses were carried out to compare means for the transparency and
word type (Student-Newman-Keuls test with alpha levels = .05). Response times
for transparency levels occurred in four statistically significant groupings:
100%+50%+20%, 10%, 5%, and 0% (baseline condition). As expected, word types
were grouped according to the predicted Stroop Effect: incongruent (color name
conflicted with color word), neutral+congruent, and blank (color only -
baseline condition). Our primary interest is in the effect of transparency
under maximum interference conditions (incongruent word). The mean response
times of primary interest are shown in Figure 5.
At 5% transparency (word was only slightly visible) the means across all word
types are not statistically different from 0% (no interference/Stroop effect).
At levels above 10%, three groupings of means occur (as the Stroop effect would
predict): blank, congruent+neutral words, and incongruent words. Interference
peaked at 50% - increasing transparency did not degrade performance.
Subject errors in response occurred only occasionally (average of 4 per 540
trials) and almost exclusively on the color-incongruent trials. Errors were
approximately evenly distributed across all levels above 5% (5% showed few
errors). Error trials were not used in the above analysis.
A univariate repeated measures ANOVA was carried out on the data. As
hypothesized, a significant main effect was found for transparency F(5,
8614)=25.94, p< .0001. Word type and color also showed significant main
effects: word type F(3, 8614)=16.06, p < .0001 and color F (3, 8614)=26.55,
p < .0001. Additionally there was a significant interaction between
transparency and color F(15, 8614)=4.36, p < .0001. This suggests that word
legibility is affected by not only level of transparency (i.e., visibility) but
also the properties of the color used (i.e., saturation and luminance).
(Figure 5 shows overall mean response times.)
Post-hoc analyses were carried out to compare means for the transparency and
transparency * color interactions (Student-Newman-Keuls test with alpha levels
= .05). Transparency levels occurred in three significant groupings: 5%, 10%,
100%+50%+20%+0%. The baseline word only condition (0%) was not statistically
different from the 100% condition (word with color background). Analysis of
word type showed an unexpected Stroop Effect (despite counter-balancing order
with the color naming experiment).
For levels of transparency of 5% subjects reported great difficulty in seeing
the word, about 15% of the trials were errors. (Subjects reported "none" when
they could not make out the word.) At 5% and 10% levels, certain colors
produced better performance (lower response times, fewer errors) than others.
Yellow was "easiest" followed by green (by post-hoc analysis of means). Blue
and red were "hardest" and not statistically different. For transparency
levels above 10%, subjects made virtually no errors and performance was
consistent across colors. At 20% levels and higher, all words were easily read
and there were no significant differences in response times.
Word naming seems highly error prone at levels of 5%. At levels of 10%
subjects could accurately name most of the words, though they seemed to perform
slightly better, depending upon what the background color was. It seems that
there was an interaction between saturation/luminance and legibility. This
suggests that certain colors might be more profitably used in transparent
windows or interfaces - though this remains to be tested. Word naming
performance improved more dramatically than hypothesized, with performance
leveling off at 20%. Our hypothesized divided attention curve seems to
underestimate the effect of increased transparency. Also we did not observe
the hypothesized continual performance improvement but rather saw performance
roughly peak and remain constant from 20% transparency to 100%.
The Stroop test was used to evaluate interference between transparent layers
because it provides an sensitive, extreme measure of the extent of
interference. As such, it should suggest worst case limitations. Our results
suggest that for divided attention tasks, substantial performance gains occur
within the first 20-25% transparency, but may not occur from 20% to 100%.
Levels of 5% or less do not seem usable. For focused attention tasks, there is
a rapid performance degradation between 5% and 50% transparency. At 50%
performance is at it worst and does not deteriorate substantially with further
increases in transparency.
Clearly, different tasks will have different levels of error tolerance and
acceptable performance limits. Also the legibility of layers will be
determined by visual distinctiveness in addition to overall transparency
levels.
The above experiment tested one of the most stringent interference tasks
possible and gave us insights into both the proposed attention model and some
of the upper and lower threshold values for transparency. In addition to the
empirical work, we wish to evaluate our theories of attention, performance, and
interface design in more realistic prototype and application domains. This
work is briefly summarized here (see [4] for more detail ).
We installed transparency into some interactive dialog boxes within a 3-D
modeling/animation system. In this system the user needs to see a potentially
large model (full screen, background) while changing various attributes of the
model or of the drawing tools (using windows in the foreground), resulting in a
divided attention problem. Typically, a user might have 3 or 4 such
interactive dialog windows open at all times.
We had several users of varying levels of expertise evaluate the transparent
windows. We also asked users to select a "personal favorite" transparency
level using a slider bar. Substantial in-depth investigation is still being
conducted. However, several insightful comments have already been noted.
The degree of visual distinction between the two tasks strongly influences the
extent of possible interference and perceived difficulty. Users found
transparent windows (text, buttons) were easier to use over solid models/images
than those superimposed over wire frame drawings. Higher levels of "opacity"
seemed to partially compensate in the more difficult task situation (by
minimizing interference as in the Stroop experiment). This suggests that level
of detail or information density might also be a determining factor when
choosing transparency levels.
As familiarity with the interactive window layout improved, users preferred
corresponding increases in transparency. They preferred to see "less" of the
interactive dialog boxes and more of the underlying image. The dialog box
items were needed only as outlines to target selections - the actual legibility
of the text was substantially less important. This suggests that border of
windows and buttons and data entry areas might be handled in a different way
than the actual names and labels. Performance improvements are similar to
Heads Up Display research findings. However, this suggests new and intriguing
possibilities for dynamically evolving interfaces based on increased expertise.
We additionally developed anti-interference (AI) outlines for text and borders
of objects, based on feedback from prototyping (Figure 6) [4]. These AI
graphics use an opposing contrast level outline to encircle the object or
letter (e.g., white objects have black border outlines). This has dramatically
improved visibility and distinctiveness of items in transparent foreground
menus and windows. Work and evaluation in this area is on-going.
Abstract
Keywords:
display design, evaluation, transparency, user interface design, interaction
technology
Introduction
FIGURE 1 Design, Task, and Attentional Performance
DESIGN CHARACTERISTICS
FIGURE 2Design Space Dimensions
FIGURE 3 Concept of Layered Displays
CHARACTERISTICS
DIVIDED AND FOCUSED ATTENTION
FIGURE 4 A simple model of transparency selection
PREVIOUS RESEARCH IN ATTENTION AND DISPLAY DESIGN
RESEARCH METHODOLOGY
EXPERIMENT -- TRANSPARENCY EFFECTS ON TASK INTERFERENCE AND
LEGIBILITY
Hypotheses (stated as null hypotheses)
Experimental Design: Color Naming Experiment and Word Naming Experiment
Experimental System Configuration
Procedure
Subjects
RESULTS - COLOR NAMING TASK
RESULTS - WORD NAMING TASK
FIGURE 5 Mean response times
DISCUSSION
For the color naming experiment, we have found that degree of transparency
dilutes the interference/Stroop effect in a seemingly logarithmic fashion, with
performance leveling off at 50% transparency. At levels of 5% (and likely
less) minimal or no interference seems to take place (using means comparison
tests). This supports our proposed focus attention curve, with performance cut
off points at 5% (lower) and 50% (upper). The error rates also seem to support
this: errors only occurred below 5%. . CURRENT WORK IN REAL APPLICATIONS
FIGURE 6 (a). Plain font style, (b). "Anti-interference" 20%
transparency
(AI) font style, 20% transparency