Japanese Psychological Research 2010, Volume 52, No. 3, 201–215 Special issue: Cognitive aging studies for quality of life
doi: 10.1111/j.1468-5884.2010.00438.x
Effects of age-related decline of visual attention, working memory and planning functions on use of IT-equipment SATORU SUTO1,2* and TAKATSUNE KUMADA Industrial Science and Technology
National Institute of Advanced
Abstract: The effects of age-related decline of cognitive functions, such as visual attention, working memory and planning functions on use of IT-equipment were investigated. Older participants (n = 34; aged 65–73 years) were divided into two groups based on AIST-cognitive aging test (AIST-CAT) scores. They were then asked to purchase a reserved seat for the Japanese bullet train, using a ticket vending machine. The results showed that: (a) the decline in visual attention caused a disruption in searching for objects on the screen; (b) the decline in working memory interrupted maintaining subgoals; and (c) information related to subgoals; and the decline in planning function, interrupted constructing and maintaining a behavior sequence for operating a ticket vending machine. These results indicate that the age-related decline of cognitive functions is differentially related to problems with using IT-equipment. jpr_438
201..215
Key words: cognitive aging, visual attention, working memory, planning function, usability for older adults.
In the past decade much of the equipment we use in our daily life has become increasing dependent on computerized information technology (IT). Technology generally adds a variety of functions to basically simple equipment. For example, computerized televisions provide a greater variety of information than traditional televisions that receive only broadcast or cable programming. However, it is not necessarily the case that computerized equipment (referred to as IT-equipment) is easy to use for all people. This is particularly relevant with regard to older users. In fact, pre-
vious studies have reported that older adults experience a variety of difficulties in using IT-equipment (Czaja & Lee, 2007; Nambu, Harada, Akatu, Sawajima, & Ishimoto, 2002). Although suppliers of IT-equipment have attempted to improve product usability, these attempts have not been entirely successful. One reason for this is that some important characteristics of older users, which may impact the use of IT-equipment, have not been fully considered. Previous studies suggest that the age-related decline in perceptual and physical functions is
*Correspondence concerning this article should be sent to: Satoru Suto, Education Development Center, Shizuoka University, Ohya, Suruga-ku, Shizuoka 422-8529, Japan. (E-mail:
[email protected]) 1
Current affiliation of the first author of this paper is Shizuoka University.
2
We thank Muneo Kitajima, Toshihisa Sato, Yoshiaki Suzuki and Yukie Motomiya for helpful discussion. We thank two anonymous reviewers for critical comments on this paper.
© 2010 Japanese Psychological Association. Published by Blackwell Publishing Ltd.
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related to the difficulty of using IT-equipment (Fisk, Rogers, Czaja, Charness, & Sharit, 2004). These studies have focused primarily on the compatibility of the perceptual and physical characteristics of the interface with the physical and perceptual functions of older adults. However, certain elements of IT-equipment presume effective functioning of particular cognitive functions in a user. This presumption can be questioned. For example, use of IT-equipment is often highly dependent on information contained in screen displays; in turn, these displays often require navigation of complex and hierarchical menu structures. Not all users are equally effective in such navigations. In particular, recently, greater attention has been devoted to the role of the age-related decline of specific cognitive functions as a causal factor in the poor performance of older adults in operating IT-equipment (Fisk et al., 2004; Harada & Akamatsu, 2003). Kumada, Kitajima, Ogi, Akamatu, Tahira, and Yamazaki (2005) proposed a framework of cognitive functions that outlines issues involving the usability of equipment for older adults. In this framework, the age-related decline of three domains of cognitive functions (visual attention, working memory, and executive function) is assumed to affect difficulty in the use of IT-equipment. In the following section, we briefly review the aging effects of these three cognitive functions on the usability of IT-equipment. The first cognitive function involves visual attention. Visual attention is a critical aspect of cognition that is related to the skills needed to search for relevant information in a cluttered visual field. With IT-equipment, visual attention is important for identifying relevant from irrelevant information on a visual display, such as a computer monitor. Previous studies have reported that older adults showed poorer visual search performance than younger adults (Czaja, Sharit, Ownby, Roth, & Nair, 2001; Grahame, Laberge, & Scialfa, 2004; Sharit, Czaja, Hernandez, Yang, Perdomo, Lewis, Lee, & Nair, 2004). Difficulties with visual searches became more pronounced in older adults as the visual displays became © Japanese Psychological Association 2010.
more cluttered with irrelevant items. For example, Grahame et al. (2004) reported that older adults took longer to search for a target in webpage displays containing a variety of information than in similar displays that contained less information. Second, the working memory function, which refers to the capability for mentally maintaining information over time, is also critical for effective use of IT-equipment. Takahashi, Murata, and Munesawa (2008) reported that individual differences in working memory capacity were related to performance levels in tasks requiring information searches of websites. They assumed that the working memory function is responsible for retaining the information presented on previous webpages.Therefore, age-related declines in working memory may render it more difficult for older users to link accumulating information as they move from one webpage to the next. Consistent with this interpretation, they also found that low memory span scores were associated with higher difficulty levels in another task in which participants had to operate a voice telephone system that required a high memory load (Morrow, Leirer, Carver, & Tanke, 1998; Sharit, Czaja, Nair, & Lee, 2003). This is due to the difficulty in retaining relevant information in working memory while using the equipment. The working memory not only maintains external information arising from environmental sources, it also must maintain internal information that we generate ourselves. Kumada, Suto, and Hibi (2009) hypothesized that the latter aspect of working memory is critical in constructing and maintaining various subgoals throughout the process of operating IT-equipment. In this regard, IT-equipment presents another challenge because it often contains complex hierarchical menu structures in which relevant goals and subgoals must be frequently updated. The third cognitive function proposed by Kumada et al. (2005) as critical to using IT-equipment is an executive function involving planning. The planning function plays a role in constructing and maintaining behavior sequences corresponding to various action
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goals. Because IT-equipment usually requires a sequencing of several basic actions to achieve a particular goal, and because these actions must be performed accurately and in the correct order (Norman, 1986), the planning function is essential for the proper use of IT-equipment. Kumada et al. (2005) developed a test battery for measuring the mild age-related decline of the three main cognitive functions (visual attention, working memory, and planning). The battery, termed Advanced Industrial Science and Technology’s Cognitive Aging Test (AISTCAT), assesses the decline of cognitive functions in healthy older adults who are considered neurologically normal. In the AIST-CAT, these three cognitive functions are assumed to decline at different rates in the early stage of cognitive aging, leading to the possibility of a number of different individual profiles of cognitive aging involving these functions. Although each cognitive function declines with age, there are individual differences in the onset and speed of cognitive aging. Additionally, there are differences within individuals in the progression of decline in specific cognitive functions. Therefore, it is also assumed that individual differences in usability problems for older adults can be explained by the specific cognitive function that has declined. Kitajima, Kumada, Ogi, Akamatu, Tahira, & Yamazaki (2008) used the AIST-CAT to screen older participants regarding declines in certain cognitive functions. Participants took part in a usability test of signage in railway stations. Older participants, recruited on the basis of their scores on the AIST-CAT, were sorted into four groups. The first group had no functional decline in any of the three cognitive functions discussed above. The remaining three other groups each consisted of participants with a decline in one of the three cognitive functions. The criteria for determining a decline within some function consisted of two factors: first, the score for a particular function had to be lower than the 25th percentile of the scores of all 168 participants; second, scores for the other two cognitive functions had to exceed the 25th percentile.
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In all four groups of the Kitajima et al. (2008) study, older adults participated in a usability test of signage in railway stations which required them, for example, to transfer, from one train line to another in actual train stations. The results were of great interest. They showed that older participants exhibited behaviors that were specific to a decline in a particular cognitive function. That is, the older participants with low planning function easily lost their way in the train station, whereas others, who scored low in the visual attention function, failed to visually search for a direction sign. However, no negative effects were observed for older participants with a decline in working memory. Behavior specific to a declined cognitive function has also been observed in the usability of IT-equipment (Suzuki, Motomiya, Kashimura, Suto, Sato, Kumada, & Kitajima, 2008). In this study, four groups of participants, recruited using the same criteria as in Kitajima et al. (2008), performed a usability test for digital televisions. The results were similar to those observed by Kitajima et al. (2008). When participants were required to operate a graphical user interface (GUI) menu, those who were deficient in planning functions did not push the menu button to use some functions and those who were deficient in visual attention failed to effectively search for information on the displayed menu. Assuming that a decline in each of the three cognitive functions occurs independently in the early stage of cognitive aging, these two previous studies (Kitajima et al., 2008; Suzuki et al., 2008) showed that individual usability problems could be explained by the agerelated decline of each of the critical cognitive functions. These studies also showed that focusing on the decline of cognitive functions is useful in the investigation of problems concerning the usability of IT-equipment for older adults. However, conclusions from these two studies were based on qualitative analyses, such as interviews and behavioral observation. In addition, due to the small sample size of one group, no statistical testing was performed. Therefore, to date no investigation of usability © Japanese Psychological Association 2010.
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problems has undertaken a quantitative examination of the effects of the age-related decline in cognitive functions on performance with IT equipment. he purpose of the present study was to quantitatively examine whether declines in visual attention, working memory, or planning function affect the performance of older adults using IT-equipment. As in previous studies, we assumed that these three functions decline independently in the early stage of cognitive aging, and that the decline of each function is related to a specific usability problem. However, because the present study employed a quantitative examination, we did not screen the participants and used only small groups, as we did in previous studies. In this study participants were divided into two groups (a high function group and a low function group) using the mean split method according to their scores on the AIST-CAT. For each of the three cognitive function groups, two types of behavioral measurements, involving accuracy and reaction time, were used to assess and compare performance in the high and low functioning groups. Group comparisons using a sufficient number of participants allowed statistical analyses of these measurements. We contrasted performance on the main task between two groups of participants, where the groups were determined by participant scores on the AIST-CAT. For example, participants were assigned to either the low attention or high attention group by the mean split method on the attention task in the AIST-CAT. This procedure was applied three times, once for each AIST-CAT subtest, under the assumption that attention, working memory, and planning functions decline independently. In this study, the experimental task simulated the use of a ticket vending machine (TVM) for the Japanese bullet train (Shinkansen). The choice of TVM as the equipment was based on two factors. The first factor relates to the fact that TVM is a typical example of the type of IT-equipment encountered in everyday life that presents difficulties for users of all ages. In a railway station, we frequently observed that instead of buying a © Japanese Psychological Association 2010.
train ticket at a TVM people tended to visit the customer service counter. This was true even when a long queue was present at the counter and no queue occurred at the TVM. This situation suggests that, regardless of user, the TVM presents some problems in its interface design that constrain ease of use. The second reason was that the main goal of the operation of a TVM (i.e. buying a train ticket) is a clear and familiar goal, even for older adults. Therefore, we can assume that problems related to usability may be attributable to interface design and not to a lack of understanding of the task in itself. In this study, 34 participants served in an experiment consisting of two phases that took place during the same day. In the first phase, the participants took the AIST-CAT. In the second phase, they completed the TVM task.
Experiment General method Participants. Thirty-four older adults (17 women and 17 men), ranging in age from 65 to 73 years (M = 67.9, SD = 2.4). They were recruited from a public job-placement office for senior citizens. Their mean years of education was 12.8 years (SD = 1.8, range = 9–17). Each received ¥4000 for participating. Prior to serving in Phase 1 and 2 of the experiment, participants received a series of questionnaires/tests. Each participant completed the Mini-Mental State Examination (MMSE; Folstein, Folstein, & McHugh, 1975). Each participant’s score on the MMSE was greater than 23 (M = 28.3, SD = 2.0, range = 24– 30). Because the scores of all participants were above the cut-off point for the diagnosis of dementia (between 23 and 24; Anthony, LeResche, Niaz, von Korff, & Folstein, 1982), these participants were deemed to be within the normal range of cognitive functioning (i.e. no dementia). The average rating on a self-report health questionnaire was 3.26 (SD = .45) on a 4-point Likert scale (1 = very poor, 4 = excellent). Participants had normal or corrected-tonormal vision (tested by Kowa AS-15). They
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Table 1 The correlations between age, education, the scores on the MMSE, and cognitive function tasks
1. Age 2. Education 3. MMSE score 4. Working memory task 5. Visual attention task 6. Planning task
1
2
– -.15 .00 –.41* .01 –.35*
– .17 .35* .12 .14
3
4
5
– .15 .24 .26 N = 33
– .21 .41*
– .32†
Note. MMSE = Mini-Mental State Examination. *p < .05. †p < .10.
showed no loss of visual field (tested by TOPCON SBP-3000), and no visual problems in reading letters on a touch panel screen or in reading the questionnaires used in this study. All participants took part in Phases 1 and 2 of the experiment consecutively on the same day.
Phase 1 AIST cognitive aging test In this phase, we assessed characteristics of the participants’ cognitive function using the AIST-CAT. Methods Materials. The AIST-CAT consists of three main tasks and five subtasks.3 The three main tasks consisted of a working memory task, a visual attention task, and a planning task. In the working memory task,4 24 normal Japanese “hiragana” letters were printed as 3
Kumada, T., & Suto, S. (2009, unpublished). AIST cognitive aging test manual (in Japanese).
4
The most typical working memory task is a memory-span test. However, span tests cannot be handily executed in a paper and pencil test. Accordingly, a special task was adopted for the AIST-CAT. Kumada et al. (2005) assumed that this task measured the general capacity of working memory, because this task required maintaining both verbal and visuo-spatial codes of sample letters and transforming the visuo-spatial letter image to their mirror images, while suppressing the well-learned tendency to write nonmirrored letters.
sample letters on a test sheet. Participants had to copy the mirror image of the normal sample letters into a space to the left of the given sample letter. They were asked to do this as many times as possible within 1 min. The number of correctly written letters was then scored. In the visual attention task, 36 target shapes and 132 distractors were printed inside a frame on a test sheet. The frame outlined a search area; above the frame was printed a target shape as sample. Participants were asked to check as many targets as possible within 1 min. The number of checked targets was scored. If participants checked distractors, the number of checked distracters was subtracted from the total score. In the planning task, participants were asked to describe a familiar daily activity (e.g. making a cup of tea) by writing, in correct order, each step of the entire sequence of actions. The first step and the last step were printed on the test sheet. They had to fill in the missing action steps within a 3-min time limit. Four critical intervening steps were preliminarily defined by the experimenter. A score was based on the number of reported critical steps to reach the goal. The range of scores in the planning task was 0 to 4. If participants wrote the same step in their list of action steps twice or more, one point was subtracted from the score. Procedure. Participants individually completed the AIST-CAT in a quiet room. The AIST-CAT was administered as a paper and © Japanese Psychological Association 2010.
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Table 2 The mean score for cognitive function tasks, for each group Group Working memory Visual attention Planning function
Low (N = 17) High (N = 16) Low (N = 11) High (N = 22) Low (N = 21) High (N = 12)
Working memory task
Visual search task
Planning task
2.29** 7.81 3.91† 5.50 4.00** 5.52
29.71 32.06 26.18** 33.18 29.25** 31.76
2.29 3.25 2.09* 3.09 1.33** 3.57
*p < .05. **p < .01. †p < .10.
pencil test. Following the experimenter’s oral instructions using the AIST-CAT manual3 and a practice trial, each task was performed by the participants. All participants completed all tasks in approximately 20 min. Subsequently, performance on each task was scored by an experimenter following the AIST-CAT guidelines outlined in the manual.
Results and discussion Correlations between age, education, and the scores of the MMSE and cognitive function tests are shown in Table 1. Data from one participant who was unable to complete the task in Phase 2 was not included in this and further analyses. As shown in Table 1, age significantly correlated with the scores of the working memory and planning tasks, but age did not correlate either with the MMSE scores or the performance on the visual attention task. The MMSE scores also did not significantly correlate with the scores of the cognitive function tests. It is possible the latter finding is due to a ceiling effect of the MMSE scores. Finally, the cognitive function scores did not correlate significantly with either the working memory scores or the scores in the visual attention task. However, the score of the planning task showed a significant correlation with the working memory task (r = .41, p < .05) and a marginally significant correlation with the score of the visual attention task (r = .32, p < .10). Participants were divided by a mean split of scores for each task into high and low function groups. Table 2 shows the mean scores of the cognitive function tasks calculated for each group.A t-test was conducted to compare group © Japanese Psychological Association 2010.
means for each cognitive task to determine if the score of each task was different between groups (See Table 2). In all three tasks, not surprisingly, the score of the high function group was higher than that of the low function group with respect to the task used for categorization (e.g. the working memory score for the high and low working memory function groups). In addition, group differences were found in other scores for tasks, as shown in Table 2. The scores for planning tasks were higher in the high visual attention function group than in the low attention function group. Similarly, the scores for the visual attention task and the working memory task were higher in the high planning function group than in the low planning function group. We suggest that these superficial correlations of scores between tasks (also shown in Table 1) result from individual differences in cognitive aging. Specifically, our assumption is that decline in the three cognitive functions is independent at the early stages of cognitive aging. This also implies that some older adults will show no decline in these functions for these tasks, if they are not in any stages of cognitive aging. In fact, seven participants belonged to high groups in all three tasks. Similarly, even if functional decline occurs independently in the early stage of cognitive aging, two or more functions may decline at a relatively later stage. In this study, eight participants were categorized into low function groups for all tasks. As a result, the inclusion of “all-high” scorers and “all-low” scorers might increase the correlations between tasks. However, the other 18 participants (i.e. excluding seven “all-high” scorers and eight “all-low” scorers from a total of participants)
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were categorized into a high function group with respect to at least one task, and into a low function group with respect to at least one other task. This suggested that there was a variety of individual patterns of cognitive aging with respect to the three cognitive functions.
3
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on the first TVM screen, participants were required to maintain subgoals, such as “push the ‘Reservation’ button.” Planning functions play a role in constructing and maintaining an effective action sequence for using a machine.
Methods
Phase 2 The ticket vending machine (TVM) task In Phase 2, we investigated the role of each of the three cognitive functions in the use of IT-equipment. In this phase, we used a simulated TVM to represent IT-equipment. Although there were 15 screens in the TVM task, only performance with the first five screens was analyzed. These screens involved selection of an appropriate ticket type (Screens 1–2) and entry of route information (Screens 3–5). We proposed the following hypotheses about the role of each cognitive function in the participants’ operation of the TVM: 1 Visual attention plays a role in seeking relevant information in displays. 2 Working memory plays a role in maintaining subgoals and information related to subgoals when using a machine. For example,
Apparatus. A touch panel monitor and a personal computer (Hewlett-Packard, Tablet PC, tx-2000) were used to simulate the TVM. A software program was developed to convey the type of TVM typically used for purchasing Shinkansen tickets in Japanese railway stations. Materials. Using the simulator, we constructed a task based on an ordinary scenario in which participants buy a Shinkansen ticket. The participants were asked to buy a reserved seat ticket on the next available train from Tokyo station to Fukushima station. Exemplar displays appear in Figures 1 and 2. Procedure. The participants were seated in front of a touch panel monitor with a viewing distance of approximately 50 cm. They used a pen to interact with the simulator. First, the
Figure 1 Sample figures of screens for selecting the ticket type (a ticket for a reserved seat) and the train type (Shinkansen) on Screens 1 and 2. © Japanese Psychological Association 2010.
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participants received an explanation of the problem situation and the apparatus that was supposed to represent a TVM. Next, they were asked to read the initial task instructions displayed on the touch panel monitor. The
Figure 2
time it took each participant to read these instructions was automatically recorded. Participants were also instructed that they could access the task instructions on the monitor at any time by pushing the enter-key on the
Sample figures of screens for entering route information on Screens 3–5.
© Japanese Psychological Association 2010.
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Table 3 The mean and SD of reading times and frequency of reading instructions, and the correlation of reading times and frequency of reading instructions with scores on cognitive tasks
M SD Working memory task Visual search task Planning task
Initial reading time
Intermediate reading time
Frequency of intermediate reading
89.68 s 63.88 s -.30† -.09 -.34*
73.70 s 53.97 s -.64** -.26 -.23
5.36 3.50 -.27 -.24 -.27
Note. N = 33. *p < .05; **p < .01; †p < .10.
10-key keypad placed near the monitor. The display of task instructions covered the simulation display of the TVM. To return to the main task, participants were instructed to push the “back” button displayed on the monitor. Each of the 15 screens displayed several buttons, but only one was the correct button on a given screen. The correct button always advanced to the next screen. When a participant touched a button on the screen that was irrelevant to the current task goal, the screen became blank and this error message appeared: “This button cannot be used. Push the ‘back’ button.” At this point, a button appeared on the screen labeled “back to previous screen.” Participants could use this button to return to the main task. When participants seemed to be deadlocked, experimenters gave neutral advice such as “Please look closely at the screen” or “What do you want to do now?”. When a participant gave up, a hint to get to the next screen was presented by the experimenter. All participants completed the task in approximately 20 min. Results and discussion Analysis of reading time and reading frequency for instructions. Reading time for instructions was analyzed in two measurements. With respect to measurement of the two reading times for instructions, one was the
initial reading time measure, taken prior to a participant’s performance on the TVM task (initial reading time), the other was reading of the same instructions taken during a TVM task (intermediate reading time). In addition, we counted the frequency of reading instructions during the TVM task. The mean and SD of the two time measures (initial reading time and intermediate reading time) and the frequency of instruction are shown in Table 3. In addition, correlations of these two time measures and a frequency with scores on the three cognitive tasks are shown in Table 3. There was a significant correlation between the initial reading time measure and the scores measured in the planning task, r = -.34, p < .05). This result suggests that planning of future goal-oriented actions is associated with initial reading time. Thus, participants with a decline in planning function took longer to read the instructions prior to initiating the main tasks than participants with no decline in planning. In addition, there was a marginally significant correlation between the initial reading time and the working memory score, r = -.30, p = .09. Although information related to use of the TVM (e.g. the departure station, the arrival station and so on) was presented in text as a task instruction, this instruction disappeared when the task started. Therefore, participants may attempt to remember this information in order to operate the TVM. This type of situation can be © Japanese Psychological Association 2010.
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Table 4 The proportion of participants who responded correctly in operations on screens, shown in high and low function groups Groups with decline in cognitive functions Working memory Low function group (N = 17) High function group (N = 16) Visual attention Low function group (N = 11) High function group (N = 22) Planning function Low function group (N = 21) High function group (N = 12)
Screen (s) 1–2
3–5
3
4
5
.76 .88 ns
.76 .81 ns
.94 .81 ns
.88 1.00 ns
.88 1.00 ns
.64 .91 p < .10
.55 .91 p < .05
.80 .91 ns
.82 1.00 p < .10
.82 1.00 p < .10
.71 1.00 p < .05
.81 .75 ns
.85 .92 ns
.90 1.00 ns
.95 .92 ns
Note. On Screen 3, data for one participant were not included, because of failure of data collection.
described as a high load working memory task. It is for this reason that participants with a decline in working memory function may have used more time to initially read the instructions. The second measurement was intermediate reading time, which involved the reading time for instructions during the task performance. There was a strong positive correlation between the intermediate reading time and the working memory task score, r = -.64, p < .01. However, the scores on the frequency of reading instructions were not correlated with any of the three cognitive functions (cf. third column of Table 3). In sum, these results indicate that reading time per once was longer in participants with a decline in working memory than in participants with no decline in working memory, suggesting that the working memory function affects encoding of relevant information during the reading of instruction. Analysis of the performance on each screen regarding the relationship with the three cognitive functions. Screens 1–2 (selecting a ticket type and a train): On Screen 1, an instruction displayed at the top of the screen asked users to select a ticket type (e.g. for a reserved seat ticket, a nonreserved seat or a discount ticket, etc.). To follow initial © Japanese Psychological Association 2010.
instructions, a participant had to select the “Reservation” button.5 Next, on Screen 2, initial instructions indicated that a participant had to select the “Reserving seat for Shinkansen” button. The percentages of correct responses on Screens 1 and 2 are shown separately for the high and low function groups in Table 4. A correct response is defined as a selection of the correct button on the first trial. There was a significant difference in the proportion of correct responses in the planning function group, Fisher’s exact probability test, p < .05. In addition, there was a marginally significant difference in the visual attention group, p < .10. The mean reaction time for Screens 1 and 2 is shown separately for the high and low function groups in Table 5. Reaction time was defined as the time from the onset of a display on each screen until a correct button press to the display. Reaction time increased not only when participants made more errors, but also when the operation time per task step increased, even
5
On Screen 1, when participants push the “Searching train” button, the selection of this button was counted as a correct response. However, we excluded the route from pushing this button, because the route from the “Searching train” button was so complex.
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Table 5 Means and SD of the reaction time for screens, shown in low and high function groups Groups with decline in cognitive functions
Working memory Low function group (N = 17) High function group (N = 16) Visual attention Low function group (N = 11) High function group (N = 22) Planning function Low function group (N = 21) High function group (N = 12)
Screen (s) 1–2 M (SD)
3–5 M (SD)
3 M (SD)
4 M (SD)
5 M (SD)
75.9 (103.0) 52.9 (55.9) ns
48.6 (50.4) 36.0 (37.2) ns
16.7 (15.9) 17.0 (19.6) ns
18.9 (35.8) 5.5 (2.8) p < .10
12.5 (13.1) 5.2 (2.4) p < .05
102.0 (128.1) 46.1 (40.2) p < .05
61.9 (59.2) 32.8 (31.9) p < .05
23.7 (21.5) 13.7 (14.9) p < .10
24.8 (43.9) 6.2 (4.2) p < .05
13.6 (15.4) 6.6 (5.1) p < .05
80.0 (128.1) 38.0 (40.2) p < .10
44.0 (46.2) 39.9 (42.4) ns
18.2 (20.2) 14.7 (12.3) ns
16.3 (32.5) 5.6 (3.5) ns
8.9 (7.9) 9.0 (13.5) ns
without errors. There was a significant difference in reaction time for the visual attention function group due to high versus low visual attention functioning, t(31) = 1.89, p < .05; in addition, a marginally significant difference between the high and low function participants was found in the planning function group, t(31) = 1.42, p < .10. Within both of these cognitive groups, reaction time was longer for the low than for the high function group. On Screens 1 and 2, we also observed increases in operation failures (errors), as well as longer reaction times in the low functioning participants (i.e. relative to high functioning participants) in both the planning and visual attention tasks. However, the reasons for operational difficulties may differ for planning versus visual attention tasks. Participants in the low planning function group committed errors, or made correct responses with longer reaction times on Screens 1 and 2. These results showed that such participants might fail to construct or maintain an action sequence for achieving the subgoals of the task. Because participants were given task instructions in text prior to viewing Screens 1 and 2, the information relevant for configuring subgoals was not explicitly stated on Screens 1 and 2. Consequently, these participants had to configure an appropriate subgoal
by extracting relevant information from these screens. Such situations present a high load on the planning function.Therefore, participants in the low planning function group showed difficulty in performing the task on these screens. In contrast, with Screens 1 and 2, participants in the low visual attention function group responded relatively slowly with both erroneous and correct responses. This finding implies that these participants failed to identify the relevant visual information for the operation. Participants in this group might have difficulty in searching for relevant information even from the relatively smaller number of buttons: eight buttons on Screen 1 and five buttons on Screen 2. Screens 3–5 (selecting a rail line, a departure station, and an arrival station): For Screens 3–5, participants were required to enter route information on each screen (Figure 2). First, on Screen 3, participants had to select a train name from three candidates. Second, in Screen 4, participants had to select a departure station from a list of two stations along the train’s (previously selected) route. Finally, with Screen 5, participants had to select an arrival station from among a list of 36 stations. We assumed that memory loads on the management of route information would be higher in these screens than in Screens 1 and 2, due to © Japanese Psychological Association 2010.
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the requirement that the contents of route information be memorized (i.e. the exact name of line, departure and destination stations). If participants failed to memorize the correct information, they would be unable to respond correctly to the displays on Screens 3–5. Therefore, maintenance of specific route information, related to subgoals, played an important role on Screens 3–5. Hence, we predicted that participants with a decline in working memory function would exhibit difficulty in performing the tasks on Screens 3–5. Table 4 presents the proportion of people who responded correctly on Screens 3–5; data are shown separately for the low and high function groups within each of the three cognitive function groups. There was a significant difference between low and high functioning participants in the visual attention function group, Fisher’s exact probability test, p < .05. The corresponding reaction times on Screens 3–5 for the low and high function groups are shown in Table 5. Here too, a significant difference emerged between low and high functioning participants in the visual attention function group, t(31) = 1.85, p < .05. These results suggest that a decline in visual attention function generally had a negative impact on people’s ability to extract and use task-relevant information in the presence of distracting irrelevant information. On Screens 3–5, the memory load required was different for each screen. On Screen 4, the Tokyo station was very familiar to all participants, as they lived in the Tokyo area. This means that the memory load for this screen should be fairly low. In contrast, the arrival station, specified on Screen 5, might be less familiar because it was an arbitrarily chosen station on a train line. Therefore, memory load should be higher on Screen 5 than on Screen 4. For this reason, data for the three screens comprising Screens 3–5 were separately analyzed and are shown in columns 4–6 of Tables 4 and 5. On Screen 3, there was no significant difference in correct responses for groups in any of the tasks. Only a marginally significant difference in reaction times between low and high © Japanese Psychological Association 2010.
functioning participants was found for the visual attention task, t(31) = 1.52, p < .10. For Screen 4, a marginally significant difference of correct responses was observed in the visual attention function task, p < .10. Also for this screen, there was a significant group difference in the reaction time in the visual attention function task, t(31) = 1.99, p < .05, and a marginally significant group difference in the working memory task, t(31) = 1.45, p < .10. For Screen 5, there was a marginally significant group difference in correct responses for the visual attention function task, p < .10. Also for this screen, there was a significant group difference in the reaction time in the working memory function task, t(31) = 2.18, p < .05, and a significant group difference in the visual attention function task, t(31) = 1.96, p < .05. For Screens 4 and 5, reaction times in the working memory task were slower for those participants with low working memory function than those with high working memory function. This tendency was most evident in performance for Screen 5 (i.e. relative to Screen 4 data). This result showed that a decline in working memory function was related to difficulties in the maintenance of subgoals on Screens 3–5.
General discussion We investigated the effects of a decline in three cognitive functions on older adults’ use of a TVM simulator. The three cognitive functions of particular interest involved working memory, visual attention, and planning. We hypothesized that these functions involve different aspects of the cognitive activity that a participant needs to effectively use IT- equipment. Furthermore, we hypothesized that an age-related decline in each of these functions might cause, respectively, different problems relating to the usability of the machine. The results in Phase 2 of this study supported our hypotheses. First, participants shown to have a decline in visual attention function were consistently longer in responding to relevant visual information than their counterparts who measured
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high in visual attention functioning. For example, when participants were required to select a target from among some buttons on Screen 5, average reaction time increased more for participants in the low attention function group than for those in the high function group. A similar pattern of results was found on other screens that also required participants to search for a target among visual distractors. This suggests that a decline in visual attention function is associated with problems in searching for task-relevant information on displays of IT-equipment. This is also consistent with a recent study showing that a decline in the visual attention function causes difficulty in searching for objects on webpages (Grahame et al., 2004). The results of Phase 2 also showed certain negative effects of decline in working memory on performance. A low working memory function lengthened the time spent reading instructions, but it had relatively little impact on the frequency of reading instructions. These results suggest that the decline in working memory affected encoding instructional information related to subgoals. The results of Phase 2 also showed that a decline in working memory function affects other aspects of operating IT-equipment. Participants with a decline in working memory function took longer to specify a button when the task required recalling concrete taskrelevant information from working memory. This was the case with Screens 4 and 5, when participants were expected to choose the exact names of certain stations that were given in the initial instructions. These results are consistent with the hypothesis that working memory serves as a temporary storage for subgoals and for information related to subgoals (Kitajima et al., 2008). It is interesting to note that participants with a decline in working memory function did not show any disruptive effects on Screens 1 and 2, except for extended reading times of initial and intermediate instructions, despite the fact that some configuration of a subgoal was required on each screen. This might be due to the fact that the tasks on Screens 1 and 2 were
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qualitatively different from the tasks on Screens 3–5, in that instructions for Screens 1 and 2 were more abstract than the instructions for Screens 3–5. On Screens 1 and 2, the simple instruction was given as “Select a ticket type” on the top of both screens. “Ticket type” could have several meanings depending on the context. Therefore, in order to select the correct buttons on these screens, the participants had to understand the exact meaning of the instruction through understanding the context given by the meaning of the labels on the buttons on the screen. The fact that participants with declining working memory function had little difficulty with this, suggests that the decline in working memory did not affect the understanding of the abstract instruction. In contrast, Screens 3–5 presented very concrete instructions on the top of each screen: for instance, one instruction on Screen 5 was “select the name of your arrival station.” According to this instruction, participants were asked to select a button that corresponded only to the station name previously specified in the initial instructions. On these screens, participants had to match memory content to the information on the screen. Therefore, the task required on Screens 3–5 placed a high demand on memory. Accordingly, it is not surprising that participants with a degraded working memory function showed some difficulty in responding to the tasks presented on Screens 3–5, relative to the tasks given on Screens 1 and 2. In conclusion, the decline in working memory affected the functions of maintaining and encoding of subgoals and specific information related to subgoals. Participants with a decline in planning function showed a pattern of results that differed from the profiles found in the two other cognitive function groups. The score for the planning function task significantly correlated with the initial reading time for instructions in Phase 2. We assumed that when participants read the initial instructions, they extracted relevant information for constructing a mental plan of an action sequence consistent with these instructions. Kumada et al. (2009) hypothesized that the planning function plays © Japanese Psychological Association 2010.
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a role in constructing and maintaining behavior sequences for the using of machines; thus, a decline in planning function should lead to difficulty in constructing action plans even prior to starting the task. In the case of a TVM, users may not mentally construct a step-by-step action sequence before performing a given task if they are unfamiliar with the TVM. However, we would argue that, to some degree, task instructions, when presented in a text format, should be understood as components of actions. Therefore, because older users with low planning function might not be able to extract information relating to action components from the instruction, they should have difficulties even before initially performing the TVM task. We also found that when task instructions were abstract, participants in the low planning function group committed more errors before selecting the correct button than participants in the high planning function group (on Screens 1 and 2). This result should be contrasted with the result that participants with a decline in the planning function did not show difficulty in performing the tasks on Screens 3–5, although participants with low working memory function did show difficulty with these tasks in that they had trouble selecting the correct response. The present results suggest that concrete task instructions are helpful for users with a decline in planning function. The present study quantitatively demonstrated that the decline in cognitive functions affected different aspects of difficulty for users of IT-equipment. Consistent with the results of previous studies (Kitajima et al., 2008; Suzuki et al., 2008) using a qualitative procedure, the usability of IT-equipment for older adults was affected by their individual pattern of decline in cognitive functions. So far, usability tests for older adults have generally been performed without considering individual differences in cognitive functions (Grahame et al., 2004; Murata & Iwase, 2005). Compared with these previous studies, the approach taken in this study has, at least, two merits. First, in the present study, the appearance of individual differences in using the TVM was demonstrated as © Japanese Psychological Association 2010.
group differences with regard to cognitive functions. If such group differences are not taken into account, the data only show a wide variety of “nonsystematic” individual differences. Second, the grouping of participants with respect to their decline of cognitive functions permitted an interpretation of why some participants have difficulty using the TVM whereas others do not. Error responses or prolonged reaction times could be interpreted in terms of the degraded cognitive functions of a group of participants. This means that if a user’s individual decline pattern of cognitive functions is considered in a usability test, it is possible to improve the design of IT-equipment in specific ways. Relative to conventional approaches, in which individual patterns of cognitive decline are not considered, this is a persuasive advantage. The new approach, proposed here, suggests that current IT designs can be improved by focusing on reducing specific cognitive loads for older adults with specific decline of cognitive functions. In the future, we will offer suggestions for further investigations into whether such designs really reduce older users’ difficulties in operation of IT-equipment in studies with real settings. Finally, we note the limitations of the present study. First, in this study, the same participants were used to examine two subgroups within each cognitive function due to the cost of running experiments. More formally, however, the effects of each cognitive function should be tested using independent groups of participants in three different experiments. Second, in this study, we assumed that the three cognitive functions declined independently in the early stages of cognitive aging, and thus had independent effects on the usability of the equipment. Therefore, we did not examine the effects of interaction between cognitive functions on usability. When the effect of one cognitive function was examined, the individual status of the other cognitive functions was not taken into account. However, users with decline in two or more cognitive functions might show difficulties in their use of IT-equipment that are different from those reported in this study. Thus, apparently further studies are required for
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verifying and extending the findings of the present study.
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