INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION, 12(1), 151–164 Copyright © 2000, Lawrence Erlbaum Associates, Inc.
Visual Impairment: The Use of Visual Profiles in Evaluations of Icon Use in Computer-Based Tasks Julie A. Jacko Department of Industrial Engineering, University of Wisconsin–Madison
Robert H. Rosa, Jr. Ingrid U. Scott Charles J. Pappas Bascom Palmer Eye Institute, University of Miami
Max A. Dixon Department of Industrial and Systems Engineering, Florida International University
This research investigates an empirical link between characteristics of impaired vision and user performance on computer-based systems. The underlying premise of this research is twofold: specific aspects of visual dysfunction can be linked to the task performance demonstrated by computer users with impaired vision, and graphical user interfaces can be modified to evoke enhanced performance from low-vision users. Iconic selection time and accuracy within a graphical user interface were evaluated, comparing performance of low-vision users with performance of fully sighted users, and linking task performance to specific profiles of visual impairment. Results indicate that visual acuity, contrast sensitivity, visual field, and color perception were significant predictors of task performance. In addition, icon size, set size, and background color significantly influenced performance. This research confirmed the validity of both underlying premises and serves as a launching point for future research concerned with developing features that will assist users with a variety of visual deficits.
1. INTRODUCTION The ability to operate within our environment is a function of our capacity to detect, interpret, and respond appropriately to sensory information (Kline & Schieber, This research was supported by grants awarded to Julie A. Jacko by the National Science Foundation (BES-9714555 and BES-9896304). Max A. Dixon’s participation was made possible through a grant awarded by the 1997 NASA Graduate Students Researchers Program (NGT10-52614). We gratefully acknowledge the contributions of Dr. Elly du Pre’ at the Miami Lighthouse for the Blind and those of Dr. Frank T. Conway of the State of Wisconsin, Department of Workforce Development. Requests for reprints should be sent to Julie A. Jacko, Department of Industrial Engineering, University of Wisconsin–Madison, 1513 University Avenue, Madison, WI 53706. E-mail:
[email protected]
152
Jacko et al.
1985). Just as a large portion of the information in our environment is visual, so is the majority of the information presented during a computer task. Thus, our ability to utilize a computer effectively is largely dependent on the complex chain of visual processes that begin at the ocular media of the eye and extend to high-level perceptual processes in the brain (Bruce & Green, 1991). Advances in the computational power of personal computers have led to an increased use of graphics. Today’s graphical representations provide vivid details and colors to the “normal” eye. The use of graphics has been exploited for representing numerical and pictorial information. Moreover, graphical symbols, or icons, have become mainstream in the graphical user interface (GUI) environment of today’s computing systems. Icons serve as a means through which users can initiate higher level actions and concepts without the use of complex syntax (Shneiderman, 1998). Icon use requires a pointing device, most frequently a mouse. When using a mouse, two interaction tasks are critical to successful icon activation: selection and position (Foley, Wallace, & Chan, 1984). In selection tasks, the user chooses from a set of items displayed on the screen. In position tasks, the user specifies a point in a one-, two-, or three-dimensional space. Completion of these tasks requires complex integration of the visual and motor systems. In fact, successful use of iconic representations within GUIs places considerable demands on the human visual system. Visual impairment can significantly influence a user’s ability to perceive graphical and textual information in a GUI. Reduced visual capabilities, on the part of the user, result in poorly perceived images regardless of the quality of the interface or medium of presentation. Thus, researchers and developers need to understand how impairment of visual perceptual function affects a person’s ability to interact with computers requiring direct manipulation of objects on a computer screen. A comprehensive understanding requires moving beyond a shotgun approach to enhancing the perceptual experiences of low-vision computer users. Rather, researchers need to recognize that the term low vision includes a broad range of functional capabilities and limitations. To accommodate the needs of an entire population of low-vision computer users, basic research must be conducted that examines how impaired visual processes drive the performance of specific, interactive tasks, like positioning and selection, by low-vision computer users. In this way, the visual perceptual experience can be enhanced during human–computer interactions in a comprehensive, systematic fashion. Such fundamental inquiries must span a variety of visual disorders and functional capabilities so accurate models of performance can be developed.
2. BACKGROUND 2.1. Direct Manipulation Tasks Direct manipulation has been characterized (Eason, Johnson, & Fairclough, 1991) as a “visual interface which emphasizes eye–hand coordination skills as a prime requi-
Visual Search
153
site for successful and efficient interaction” (p. 116). Direct manipulation is attractive because its use often results in faster performance, fewer errors, easier learning, and enhanced user satisfaction (Buxton, 1985). Physical, spatial, or visual representations also appear to be easier to retain and manipulate than textual or numeric representations (Arnheim, 1972). However, for people with low vision, there are inherent limitations associated with the use of direct manipulation interfaces (Farrell, 1991). Challenges include difficulty distinguishing among icons on the screen, positioning the cursor on display items, and increased visual fatigue associated with prolonged screen viewing.
2.2. Characterizing Human Vision An assessment of visual functioning and related limitations includes baseline data on visual acuity, contrast sensitivity, field of vision, and color perception. Incorporating such clinical assessments into fundamental investigations of human–computer interaction for low-vision computer users is the foundation on which this research lies. Visual acuity refers to a person’s ability to resolve fine spatial detail (Kline & Shieber, 1985). Traditionally, visual acuity is measured using a Snellen visual acuity chart, which is a standard letter-based chart. The Bailey–Lovie style chart is favored when the patient’s visual acuity does not permit reading of the largest letters on the Snellen chart at the standard distance (Bailey & Lovie, 1976; Ferris, Kassoff, Bresnick, & Bailey, 1982). Contrast sensitivity tests a person’s ability to detect pattern stimuli at low to moderate contrast levels. The contrast sensitivity function provides an extensive representation of the spatial discriminating abilities of the visual channels (Wood & Troutbeck, 1994). The Pelli–Robson chart (Pelli, Robson, & Wilkins, 1988) consists of a series of letter charts of different contrasts. These charts enable mapping of a contrast-sensitivity function for letters. The useful field of view is the total area over which effective sight is maintained relative to a constant straight-ahead fixation point (Kline & Shieber, 1985). Ophthalmologists most commonly use standardized, automated perimetry to evaluate visual field. A person’s ability to discern and identify color within his or her useful field of view is called color vision. The Farnsworth D–15 color vision test can be administered to assess color perception (Kraut & McCabe, 1994). When there are losses in visual field, a person’s ability to maintain effective sight over a total area relative to a constant, straight-ahead fixation point is impaired (Kline & Schieber, 1985) and can disrupt the ability to read in sequence (Kraut & McCabe, 1994). Therefore, when a computer user’s visual field is impaired, and the individual is simultaneously viewing a fraction of the entire display because of screen enlargement, the opportunity for disorientation is greater than for a fully sighted user viewing a display in its entirety. Furthermore, visual acuity and contrast sensitivity are good predictors of real-world visual task performance, such as detection and identification (Evans & Ginsburg, 1985; Hills & Burg, 1977). Finally, congenital and acquired color defects typically result in color discrimination diffi-
154
Jacko et al.
culties on the basis of lightness, hue, and saturation. Colors of certain hues may also be more or less difficult to perceive depending on the type of color deficit a person possesses (Bruce & Green, 1991). Thus, screen features possessing color, presented on a background of color, may pose problems for some low-vision computer users. Therefore, impairments in visual field, acuity, contrast sensitivity, and color perception can slow performance and lead to errors in detection, identification, and selection of visual stimuli such as icons.
2.3. Computer Users Who Have Impaired Vision A common approach used by software designers of GUIs in an effort to accommodate people with low vision is to provide them with the ability to manipulate the sizes of objects on the screen, like icons. This approach is grounded in the knowledge that many people who have low vision use magnifiers to read printed text to make use of their residual vision (Ahn & Legge, 1995; Den Brinker & Beek, 1996). Furthermore, Jacko, Dixon, Rosa, Scott, and Pappas (1999) found that iconic enlargement significantly improved the visual search time component of iconic selection for low-vision computer users. Another feature of an interface that has the potential to influence performance is the number of objects present on the screen at any given time. Set size is a term used to represent the number of stimuli from which a person must make a choice. The role of set size on reaction time is well documented. Historically it has been shown that in typical reaction time experiments, participants’ reaction times increase when required to respond differentially to one of two equally probable stimuli instead of just one stimulus (Hyman, 1953). As early as 1885, Merkel demonstrated that when participants are required to respond to 1 stimulus chosen from a set of 2 to 10 equally probable alternatives, reaction time increases with the number of alternatives available. It follows that people with low vision will experience even larger effects of set size than their fully sighted counterparts because stimulus detection in the case of the former is a more challenging endeavor. In addition, people with impaired vision often encounter specific color defects such as red–green and yellow–blue defects that may further impede task performance (Kraut & McCabe, 1994). There may be as many as three times more people with low vision than fully blind people (e.g., Newell & McGregor, 1997). Furthermore, people with impaired vision prefer to use a device that allows them to make use of their residual vision (Edwards, 1995). Thus, it is important to understand the difficulties computer users with impaired vision experience when utilizing GUIs and how these difficulties can be overcome. Beyond simply determining whether someone is fully blind, functionally blind, or partially sighted, designers must understand the degree of visual impairment with respect to four characteristics of vision: visual acuity, contrast sensitivity, field of view, and color perception. These characteristics determine where a person resides in the space ranging from fully sighted to completely blind (Kraut & McCabe, 1994). With sufficient data, researchers can begin to understand how to predict performance given basic dimensions of human vision (Jacko & Sears, 1998).
Visual Search
155
3. OBJECTIVE The primary objective of this research is to establish a research basis for the field, offering well-grounded empirical findings that support intuition and, until today, ad hoc solutions. This is accomplished by establishing a groundwork for inquiries aimed at understanding how basic human–computer interaction is linked to the visual capabilities of the low-vision user. This research couples frequently cited anecdotal evidence with empirical evaluations of computer users’ performance with direct manipulation interfaces. Finally, this research will serve as a solid foundation for future investigations concerned with developing features that will assist users with a variety of visual deficits. Given the aforementioned context, it is hypothesized that the four visual parameters—visual field, visual acuity, contrast sensitivity, and specific color deficiencies—will significantly influence detection, identification, and selection of visual icons in a GUI. It is further hypothesized that specific interface features like icon size, set size, and background color will significantly influence detection, identification, and selection of visual icons in a GUI.
4. METHOD 4.1. Participants The general inclusion criteria were those English-speaking participants, 20 years of age or older, who possessed some residual vision (or full vision) and were capable of using computers with appropriate vision-enhancing devices, if necessary. To examine differences along the full range of vision, participants were selected from two pools: persons who have been diagnosed with an uncorrectable ocular disease (PSU) and persons who possess no known uncorrectable ocular diseases and who have fully corrected vision (FSU). Ten PSUs were identified with assistance from the Low Vision Clinic at Bascom Palmer Eye Institute and the Computer Systems Coordinator at the Miami Lighthouse for the Blind in Miami, FL. Thus, all PSUs were prescreened and known to possess knowledge of the utility of computers. In addition to their ocular diagnosis, information was also gathered from the PSUs concerning comorbidities, duration of disease, age (M = 36 years), gender (3 men and 7 women), and computer experience (M = 4.85 years). The 10 FSUs were experience-, age-, and gender-matched to the PSUs so that valid comparisons could be made between the two groups. Informed consent was obtained from all participants. The experimental protocol was reviewed and approved by the institutional review boards of all collaborating institutions prior to data collection. Participants were provided with a clinical assessment of their vision at the Low Vision Clinic at Bascom Palmer Eye Institute to ensure that the experimenters had knowledge of their current visual status. This also enabled the provision of corrective eyewear during experimentation so that performance could be evaluated under conditions of best corrected vision. Assessments of visual acuity were made using a Bailey–Lovie chart (Bailey & Lovie, 1976). A Pelli–Robson chart was used to
156
Jacko et al.
assess contrast sensitivity (Kline & Schieber, 1985). Field of view was assessed using the Esterman projection perimetry technique to evaluate binocular field of view, and the Humphrey Visual Field-SITA 60 was used to evaluate monocular visual field (Kline & Schieber, 1985). Color perception was evaluated using the Farnsworth D–15 color vision test (Kraut & McCabe, 1994). As an incentive, participants were provided this clinical assessment free of charge.
4.2. Experimental Tasks and Environment A computer interface was designed to test the ability of the participants to correctly identify and select icons common in a Microsoft Windows environment. The apparatus consisted of an IBM-compatible PII/266, with a 21-in. color monitor, running Microsoft Windows NT 4.0. The interface was designed with Microsoft Visual Basic 5.0. A total of six icons were employed throughout the experiments: Print, Paste, Save, Copy, New, and Open. These icons were chosen because they have been shown to be the most identifiable to Microsoft Word users (Sears, Jacko, Brewer, & Robelo, 1998). The following is a description of the interface employed in this experiment. The user was presented with instructions that read “Select the Following Icon.” Accompanying this instruction was one of the six icons shown in Figure 1. In the interest of clarification, textual labels are provided for each icon in Figure 1. However, textual labels were not displayed in the interface. This stimulus icon was randomly chosen and displayed on the screen at 58.3 mm to maximize the probability of detection by all participants. Then, presented on the target presentation screen was a set of two to six icons that were randomly chosen from the icon set described. On each trial the size of the icons varied from 9.2 mm to 58.3 mm. All icons on a single display were identical in size. The sizes of the icons were proportional to the sizes of the letters on the Bailey–Lovie acuity chart. For example, letters 23.2 mm wide on the Bailey–Lovie chart corresponded to an acuity level of 20/80 (see Table 1). The color of the background on which the icons were presented was also manipulated. Five colors—black, white, blue, red, and green—were employed at fully saturated levels. Saturation is a color’s perceptual difference from a white, black, or gray of equal lightness. In an effort to use standard Microsoft icons, colors within the iconic representations were not manipulated.
FIGURE 1 Iconic representations.
Visual Search
157 Table 1: Icon Sizes Icon Size (mm)
Corresponding Acuity Level
9.2 14.6 23.2 36.8 58.3
20/32 20/50 20/80 20/125 20/200
A timer, internal to the computer, was used to record the time required for a participant to execute iconic selection. This was measured in msec. Accuracy was also recorded electronically during experimentation. Accuracy was defined by an incorrect or correct selection of the target icon. The distance from the center of the home position to the center of the target icon was identified for each trial. The home position was a circular area located at the bottom center of the screen with an outside diameter of 58.3 mm and an inner diameter of 38.3 mm. The outside diameter was filled with a 60% gray tone and the inner diameter was completely filled black. The distance from the center of the home position to the bottom of the bottom row of icons was 90 mm for every trial. The icons were arranged in a 3 × 2 matrix. The space between icons was the same as the size of the icons used. This configuration allowed all icons to be visible to the user on every trial when using a 21-in. monitor.
4.3. Experimental Design A 5 × 5 × 5 nested factorial, repeated measures design was utilized to test the hypotheses of this study. Five set sizes of icons, five background colors, and five icon sizes were investigated. Thus, each participant within each group was exposed to 125 experimental conditions. Each condition was presented twice, resulting in a total of 250 randomly presented trials per individual. The dependent variables in the experiment were time for iconic selection and accuracy (proportion of correctly selected target icons). The independent variables were set size, background color, and icon size. Comorbidities, duration of disease, age, gender, and years of computer experience were also recorded.
4.4. Procedure The study was conducted in two parts. In the first part, participants’ vision was assessed at the Low Vision Clinic of the Bascom Palmer Eye Institute. Visual acuity, contrast sensitivity, color perception, and visual field of each participant was assessed. Total time for this assessment was approximately 1 hr. The second part of the experiment involved the execution of the computer-based tasks. Participants performed the tasks with the assistance of corrective eyewear (glasses, contacts, field glasses) if necessary, to allow experimentation under best corrected vision. Il-
158
Jacko et al.
lumination levels in the study room were low during the initial stages, to allow for dark adaptation of the eyes, while the experimenter gathered demographic information from the participant. The lights were then turned off in the room so that the only source of illumination came from the monitor. The effects of eliminating other sources of illumination included glare reduction and other confounding effects due to ambient light on the low-vision participants. The participants then positioned themselves such that the distance from the monitor to their eyes was approximately 20 in. The mouse was the only input device used by the participants. A practice session, composed of 50 trials, enabled the individuals to become familiar with the experimental environment. On completion of the practice trials, the participants rested for 5 min before beginning the 250 experimental trials. Participants were provided a 5-min rest every 50 trials to minimize fatigue effects. The duration of the computer-based evaluation was approximately 1 hr. Participants were given the opportunity to discontinue their involvement at any time without penalty.
5. RESULTS 5.1. Clinical Assessments of Vision Table 2 summarizes the results of the visual profiles of the PSUs. Acuity is represented as the logarithm of the minimum angle of resolution for each eye. Contrast sensitivity scores are represented as summations across both eyes for the number of letters identified correctly. Color perception is represented by a categorical variable where 2 = deutan (green deficient), 3 = tritan (blue deficient), 4 = anomalous (a more general color confusion), and 5 = normal color vision. Visual fields are depicted by Esterman Efficiency Scores, representing the percentage of stimulus points detected in a participant’s field of view. Table 2: Visual Assessments for Person With Uncorrectable Ocular Disease Participant
A (L)
A (R)
CS (L)
CS (R)
VF
C
Diagnosis
26 29 30 33 44 49
+0.2 +1.0 0 +1.1 +0.9 +0.9
+0.4 +0.9 +1.0 +1.0 +1.1 +0.6
36 32 0 18 32 32
32 36 17 18 27 35
15 100 27 13 93 90
4 5 2 3 5 5
54
+1.2
+1.1
21
24
71
3
56 63 65
+0.4 +0.8 +0.8
+0.5 +0.9 +0.8
24 32 29
15 28 30
1 100 100
4 4 4
Retinitis pigmentosa Albinism Optic neuritis Retinitis pigmentosa Myopic degeneration Congenital cataract; nystagmus Age-related macular degeneration Retinitis pigmentosa Congenital achromotopsia Congenital achromotopsia
Note. A = acuity (Visual acuity status was summarized for the analyses in terms of weighted average LogMAR with the better eye given a weight of 0.75 and the worse eye given a weight of 0.25 [Scott et al., 1994].); CS = contrast sensitivity; VF = visual field; C = color perception; (L) = left eye; (R) = right eye.
Visual Search
159
FSUs demonstrated best corrected vision of 20/20. Free of ocular diseases, FSUs generated maximum contrast sensitivity scores of 48, Esterman Efficiency Scores of 100%, and normal ratings of color vision.
5.2. Visual Icon Identification Performance The aims of this research are to explain sources of variation in performance on computer-based tasks among patients with impaired vision. Given vast differences from individual to individual, variability in visual capabilities will be expected between PSUs and FSUs. Thus, analysis of covariance (ANCOVA) was used for the statistical analyses concerning time for iconic selection, to control for this variability. In addition, ANCOVA (Lindman, 1992) enabled us to treat the visual profile parameters of acuity, contrast sensitivity, field of view, and color perception as covariates in the analyses. To correct for deviations from sphericity, the Greenhouse–Geisser correction factor was implemented (Stevens, 1996). Further attempts to reduce the error term, ε, were made by selecting FSUs with the same gender, age, and level of computer experience as the PSUs. Accuracy, the remaining dependent variable, was dichotomous in nature. This necessitated use of a nonparametric analysis, the Kruskal Wallis test (Daniel, 1990), to identify significant effects of the independent variables on accuracy. To investigate post hoc, pairwise comparisons between factor levels, Fisher’s Exact test was utilized. There was a significant difference between PSUs and FSUs with respect to time, F(1, 12) = 6.38, p < .05. Similarly, there was a significant difference between the PSUs and FSUs with respect to accuracy, χ2(1, N = 5000) = 71.60, p < .001. The covariates, contrast sensitivity, F(1, 12) = 10.89, p < .01, green color deficiency, F(1, 12) = 11.27, p < .01, and blue color deficiency, F(1, 12) = 35.43, p < .001, were significant in describing time across all participants. Acuity, χ2(12, N = 5000) = 353.43, p < .001, contrast sensitivity, χ2(6, N = 5000) = 146.48, p < .001, visual field, χ2(6, N = 5000) = 111.78, p < .001, and color perception, χ2(3, N = 5000) = 309.43, p < .001, also had a significant effect on accuracy.
5.3. Icon Size The main effect of icon size was not significant. The two-way interactions of vision with icon size, F(1.6, 19.4) = 4.75, p < .05, green color deficiency with icon size, F(1.6, 19.4) = 15.37, p < .001, and blue color deficiency with icon size, F(1.6, 19.4) = 34.08, p < .001, were significant. The influence of icon size on time for both groups of participants along with the means after covariate adjustment are shown in Figure 2. The means and standard deviations for the data displayed in Figure 2 can be found in Table 3. There was also a significant effect of icon size on accuracy, χ2(4, N = 5000) = 140.63, p < .001. In addition, all pairwise comparisons were significant except for comparisons between icon sizes of two (14.6 mm) and three (23.2 mm), three and four (36.8 mm), and four and five (58.3 mm).
160
Jacko et al.
FIGURE 2 The effect of icon size on iconic selection time for persons diagnosed with uncorrectable ocular disease and persons with fully corrected vision (adjusted means in msec). Table 3: The Effect of Icon Size on Iconic Selection Time for PSUs and FSUs Icon Sizes
PSU FSU
1
2
3
4
5
918.6 (159.8) 17.87 (159.8)
668.5 (157.2) 16.2 (157.2)
557.7 (126.2) 31.04 (126.2)
530.0 (84.0) 32.18 (84.0)
490.1 (90.8) 28.64 (90.8)
Note. PSU = person with uncorrectable occular disease; FSU = person with fully corrected vision. Adjusted means in msec and standard errors shown in parentheses.
5.4. Set Size Although the main effect of set size was significant in a multivariate ANCOVA, on correction with Greenhouse–Geisser, it was no longer found to have a significant influence on performance. However, the interactions of vision with set size, F(1.5, 17.4) = 4.41, p < .05, green color deficiency with set size, F(1.5, 17.4) = 5.16, p < .05, and blue color deficiency with set size, F(1.5, 17.4) = 7.23, p < .01, were significant. Additionally, there was a significant effect of set size on accuracy, χ2(4, N = 5000) = 24.45, p < .001.
5.5. Background Color The main effect due to background color was not significant. The two-way interactions of green color deficiency with background color, F(1.2, 14.3) = 4.52, p < .05, and blue color deficiency with background color, F(1.2, 14.3) = 4.52, p < .05, were significant. Background color did not have a significant effect on accuracy.
5.6. Trial Each participant encountered each treatment combination twice, yielding exposure to 250 total trials. There was a significant main effect due to trial, F(1, 12) = 20.41, p < .001. Trial 1 had an adjusted mean of 336 msec, whereas Trial 2 had an
Visual Search
161
adjusted mean of 321 msec, indicating an improvement in performance across all participants from Trial 1 to Trial 2. The two-way interactions of trial and contrast sensitivity, F(1, 12) = 22.29, p < .001, and trial and set size, F(1.3, 15.6) = 4.50, p < .05, were significant.
6. DISCUSSION As expected, the low-vision participants, on average, required significantly more time and were significantly less accurate identifying visual icons within a GUI than the fully sighted participants. This result is intuitive, as one would expect this task to be more challenging for visually impaired users. This is the first time such differences have been examined in such detail for a human–computer interaction task and therefore, the findings highlight the importance of characterizing the nature of the differences between both groups of users.
6.1. Effect of Visual Profiles on Performance The visual characteristics, contrast sensitivity, green color deficiency, and blue color deficiency had significant effects on selection time. All four visual characteristics had significant influences on accuracy. This is strong evidence that such metrics of human visual capabilities can be used to characterize computer users and predict performance in basic iconic selection tasks. The theoretical implications of this contribution are notable. In classic models of psychomotor task performance, the process of visual perception is typically represented for normally sighted people (i.e., Salvendy & Knight, 1982). Given the knowledge gleaned from this study regarding the influence of the visual characteristics on performance, we have established the groundwork for modifying such models to more accurately reflect individual differences among users based on visual capabilities. There are practical implications of these results from the perspective of adaptive interfaces. This research provides evidence that clinical assessments may be useful for user profiling in the design of adaptive interfaces. More specifically, designers are often challenged with how to profile specific populations of users so that adaptive interfaces can be built that adequately adapt to their unique capabilities and limitations. These data suggest that clinical assessments of vision have the potential to be useful in this context.
6.2. Effect of Icon Size on Performance For the smallest size icon (9.2 mm), the PSUs were, on average, approximately 50 times slower selecting icons than the FSUs. The gap between the two groups decreased as icon size increased. For the largest size icon (58.3 mm), the PSUs were only 17 times slower, on average, than the FSUs. There are technological limitations associated with increasing icon size, however. Computer monitors are only capable of displaying a limited number of icons at large levels of magnification before they
162
Jacko et al.
begin occluding each other. Therefore, screen magnification should not be viewed as the total solution. Rather, screen magnification should be regarded as a vital component of enhancing the perceptual experience for visually impaired users. Icon enlargement may be less effective for people with diseases like retinitis pigmentosa because this disease causes losses in visual field, beginning with peripheral vision. Thus, portions of enlarged icons may not be visible if they happen to reside in a portion of the user’s visual field that is impaired. Although this research suggests that, in general, larger icons are preferred for PSUs, and smaller icons are preferred for FSUs (due to screen size), more research, with more participants, is needed that focuses on the characteristics of specific diagnoses so that more focused guidelines and designs can be developed. Most importantly, designers must recognize that people with low vision have a variety of visual loss patterns and thus, may have very different needs depending on individual ocular diagnoses and functional vision loss.
6.3. Effect of Set Size on Performance Set size significantly affected accuracy. Participants were four times more accurate when choosing between two icons compared to when choosing among six icons. However, set size did not significantly affect selection time. This could be due to the limited manipulation of set size. If set size was manipulated over a broader range, differences may have been detected. FSU and PSU selection times were no different in the presence of 2, 3, 4, 5, or 6 icons on the screen at one time. Given that the role of set size on reaction time is a well-documented phenomenon (e.g., Hyman, 1953; Merkel, 1885), a broader set of set size configurations will be investigated in future inquiries. Uncovering set size effects on selection time would broaden the applicability of the Hick–Hyman Law to the low-vision user community.
6.4. Effect of Background Color on Performance Of the 7 participants who had impaired color vision, 3 were either blue or green deficient and the other 4 experienced varying levels of general color confusion. Given these profiles, it was interesting to note the significant interactions of background color with green and blue color deficiencies. These results indicate that common color deficiencies that exist even in “normally sighted” populations interact with the background color of the interface to effect performance. However, there was no main effect of background color, suggesting that the influence of background color warrants additional attention. This research provides documented evidence that one’s ability or inability to detect certain colors can have a substantial influence on task performance.
7. CONCLUSION This research investigated an empirical link between the physiology of low vision and user behavior on computer-based systems. The underlying premise of this re-
Visual Search
163
search was twofold: Specific aspects of visual profiles can be linked to the performance of low-vision computer users, and interfaces can be modified to evoke enhanced performance from low-vision users. It is concluded from these results that researchers must account for specific visual capabilities like visual acuity, contrast sensitivity, field of view, and color perception when designing and evaluating basic human–computer interaction tasks like visual icon selection. Furthermore, it is concluded that, in some cases, PSUs perform very differently depending on the size of the icons in the interface and the number of icons from which to choose. This research suggests that designers may be able to enhance the perceptual experience over a variety of visual disorders by appropriately modifying the environment to empower the visual sense. Knowledge of the influence of visual profile on performance contributes in a substantial way to the existing knowledge base in the field of human–computer interaction. Such information can also serve to influence critical design decisions made by software and hardware developers so that GUIs can more closely accommodate a wide range of visual capabilities. This study generates empirical evidence that the visual profiles of low-vision users must be considered when evaluating performance of tasks requiring visual icon selection. Furthermore, this research illuminates the need for researchers and designers to acknowledge that visual perceptual function can be well-represented by clinical assessments of vision. Once it is fully understood how clinical and functional assessments can account for the task performance of low-vision computer users, researchers can more accurately model performance. This research establishes a solid foundation for future investigations that aim to couple clinical assessments of low vision with psychomotor task performance. In addition, this study serves as a launching point for a larger, more comprehensive research agenda in this area. Future research will focus on further delineating the respective roles of visual acuity, contrast sensitivity, visual field, and color perception on psychomotor task execution. This will enable specific design recommendations given a user’s visual profile and moves us closer to accomplishing the goal of universal accessibility.
REFERENCES Ahn, S. J., & Legge, D. K. (1995). Psychophysics of reading—XII: Predictors of magnifier-aided reading speed in low vision. Vision Research, 35, 1931–1938. Arnheim, R. (1972). Visual thinking. Berkeley: University of California Press. Bailey, I. L., & Lovie, J. E. (1976). New design principles for visual acuity letter charts. American Journal of Optometry & Physiological Optics, 53, 740–745. Bruce, V., & Green, P. R. (1991). Visual perception: Physiology, psychology, and ecology. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Buxton, W. (1985). There’s more to interaction than meets the eye: Some issues in manual input. In D. A. Norman & S. W. Draper (Eds.), User centered design: New perspectives on human–computer interaction (pp. 319–337). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc. Daniel, W. W. (1990). Applied nonparametric statistics (2nd ed.). Boston: PWS-KENT. Den Brinker, B. P. L. M., & Beek, P. J. (1996). Reading with magnifiers. Ergonomics, 39, 1231–1248.
164
Jacko et al.
Eason, K. D., Johnson, C., & Fairclough, S. (1991). The interaction of sensory modalities in the man–machine interface. In J. A. J. Roufs (Ed.), Vision and dysfunction: The man–machine interface. Boca Raton, FL: CRC. Edwards, A. (1995). Computers and people with disabilities. In A. Edwards (Ed.), Extra-ordinary human–computer interaction (pp. 19–43). New York: Cambridge University Press. Evans, D. W., & Ginsburg, A. P. (1985). Contrast sensitivity predicts age-related differences in highway-sign discriminability. Human Factors, 27, 637–642. Farrell, F. E. (1991). Fitting physical screen parameters to the human eye. In J. A. J. Roufs (Ed.), Vision and visual dysfunction: The man–machine interface. Boca Raton, FL: CRC. Ferris, F. L., Kassoff, A., Bresnick, G. H., & Bailey, E. (1982). New visual acuity charts for clinical research. American Journal of Ophthalmology, 94, 91–96. Foley, J. D., Wallace, V. L., & Chan, P. (1984). The human factors of computer graphics interaction techniques. IEEE Computer Graphics and Applications, 89(8), 13–48. Hills, B. L., & Burg, A. (1977). A reanalysis of California driver vision data: General findings (Tech. Rep. No. LR768). Crowthorne, Berkshire, England: UK Transport and Road Research Laboratory. Hyman, R. (1953). Stimulus information as a determinant of reaction time. Journal of Experimental Psychology, 45, 188–196. Jacko, J. A., Dixon, M. A., Rosa, R. H., Scott, I. U., & Pappas, C. J. (1999). Visual profiles: A critical component of universal access. In Proceedings of the Association for Computing Machinery Conference on Human Factors In Computing Systems (ACM CHI 99), Pittsburgh, PA, May 15–20 (pp. 330–337). New York: ACM. Jacko, J. A., & Sears, A. (1998). Designing interfaces for an overlooked user group: Considering the visual profiles of partially sighted users. In Proceedings of the 3rd ACM/SIGCAPH Conference on Assistive Technologies, Marina del Rey, CA (pp. 75–77). New York: ACM. Kline, D. W., & Schieber, F. (1985). Vision and aging. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 296–331). New York: Van Nostrand Reinhold. Kraut, J. A., & McCabe, C. P. (1994). The problem of low vision. In D. M. Albert, F. A. Jakobiec, & N. L. Robinson (Eds.), Principles and practices of ophthalmology (pp. 3664–3683). Philadelphia: Saunders. Lindman, H. R. (1992). Analysis of variance in experimental design. New York: Springer-Verlag. Merkel, J. (1885). Die zeitlichen Verhaltnisse der Willensthatigkeit. Philosophical Studies, 2, 73–127. Newell, A. F., & McGregor, P. (1997). Human–computer interfaces for people with disabilities. In M. G. Helander, T. K. Landauer, & P. V. Prabhu (Eds.), Handbook of human–computer interaction (pp. 813–824). Amsterdam, The Netherlands: Elsevier. Pelli, D. G., Robson, J. G., & Wilkins, A. J. (1988). The design of a new letter chart for measuring contrast sensitivity. Clinical Vision Science, 2, 187–199. Salvendy, G., & Knight, J. (1982). Psychomotor work capabilities. In G. Salvendy (Ed.), Handbook of industrial engineering (pp. 1–15). New York: Wiley. Scott, I. U., Schein, O. D., West, S., Bandeen-Roche, K., Enger, C., & Folstein, M. F. (1994). Functional status and quality of life measurement among ophthalmic patients. Archives of Ophthalmology, 112, 329–335. Sears, A., Jacko, J. A., Brewer B., & Robelo, L. (1998). A framework for visual icon design. In Proceedings of the 42nd Annual Meeting of the Human Factors Society, Chicago (pp. 521–525). Santa Monica, CA: HFES. Shneiderman, B. (1998). Designing the user interface: Strategies for effective human–computer interaction. Reading, MA: Addison-Wesley. Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Wood, J. M., & Troutbeck, R. J. (1994, October). Effect of age and visual impairment on driving and vision performance. Transportation Research Record, 1438, 84–90.
