Angela Mahr
email: angela.mahr@dfki.de

Sandro Castronovo
email: sandro.castronovo@dfki.de

Rafael Math
email: rafael.math@dfki.de

Christian Müller
email: christian.mueller@dfki.de

Assisting the Driver with Distance Estimation: Usability Evaluation of Graphical Presentation Alternatives for Local Traffic Events

Keywords: Distance, Assistance, Design, Usability Evaluation, In-car, Driving

When integrating numerous in-car information and assistance systems, a consistent way of spatial distance presentation for drivers is required. A common practice is to use discrete textual information (e.g. 500 meters) in combination with a graphical bar representing relative spatial information. Hitherto there exists no design consistency with respect to bars for distance illustration. Contemporary solutions differ in terms of movement direction (upward vs. downward), composition type (decreasing vs. increasing), or alignment (horizontal vs. vertical). We conducted a driving simulator experiment to investigate user preferences, perceived location, and eye gaze data for a meaningful subset of bars in a dynamic scenario. When approaching a traffic event (road works), one out of four vertical bar alternatives indicated the current distance. Subsequently, the associated horizontal bar type (decreasing or increasing) visualized the driver's spatial progress within the road works section. Our results indicate, that drivers prefer upwards-moving approach bars and progress bars decreasing from left to right. Eye-tracking data supports usage of decreasing bars instead of increasing bars. Accordingly, we elaborated an initial version of design guidelines for bars representing relative spatial information for local events. On this basis we implemented approach and progress indicators, which were adopted for numerous use-cases in a large field operational test for Vehicle-2-X Communication.

Introduction

During the last decades, awareness regarding intuitive driver interfaces has constantly increased. With a rising number of in-vehicle information systems (IVIS) and advanced driver assistant systems (ADAS) being integrated, coherent presentation of information is crucial for drivers to understand contents immediately. Although multimodal presentations open new options, common graphical displays will not become dispensable for automotive applications. Due to visual driver distraction while glancing at the display, it is necessary to reduce glancing times to a minimum by intuitive presentations. This is one fundamental design principle for interaction with IVIS [START_REF]Driver Focus-Telematics Working Group[END_REF]. One of the frequently required functionalities of in-car displays is spatial distance presentation, e.g. in today's navigation systems when a maneuver point comes up (e.g. turn right in 300m). In cases of events that can be pinpointed to a location, we use the term spatial approach visualization. Additionally, modern ADAS require visual presentation of spatial validity, e.g. visualizing the remaining section where a traffic sign is valid. In this case of traffic events having an areal extend, we name it spatial progress visualization. Although in general HCI progress is well researched [START_REF] Hamada | Color Effect on Subjective Perception of Progress Bar Speed[END_REF][START_REF] Harrison | Faster Progress Bars: Manipulating Perceived Duration with Visual Augmentations[END_REF][START_REF] Harrison | Rethinking the progress bar[END_REF][START_REF] Hsu | New Type of Auditory Progress Bar: Exploration, Design and Evaluation[END_REF][START_REF] Brewster | The design and evaluation of a vibrotactile progress bar[END_REF][START_REF] Myers | The importance of percent-done progress indicators for computer-human interfaces[END_REF], the automotive domain lacks both a common concept, as well as design guidelines for implementations of spatial approach and progress. We found a general tendency of numerical distance presentation (e.g. 300 meters) in combination with a graphical progress bar representing relative spatial information when investigating hitherto existing distance presentation in diverse system types. For presentation of spatial information, there exist many plausible variants, and unfortunately no design consensus could be observed. Nevertheless, three major design factors could be derived: First, there exist two general orientations of the bar (horizontal vs. vertical), second, the bar can be filled or emptied with some kind of filling (increase vs. decrease), and third, the movement direction can be manipulated (up vs. down or left vs. right respectively). Moreover, color-coding is sometimes used for urgency emphasis.

In this paper we examine these different design factors with the help of a user study. Our goal was to find parameter combinations, which are preferred by drivers, perceived fastest, and minimize glancing times. Accordingly, a suitable driving task was designed and implemented via the openDS driving simulator [START_REF] Math | OpenDS: A new open-source driving simulator for research[END_REF] and an eye tracker was used to measure glance durations while driving. Based on our results, we suggest general design guidelines for spatial approach and progress in the automotive domain. These guidelines have already been applied to a Human-Machine Interface used in a large field operational test with over 20 novel ADAS (sim TD ).

2

The sim TD HMI

The sim TD project 1 comprises one of the largest field operational tests for Vehicle-2-X Communication Networks (V2X) and its applications. In this domain, cars communicate with other entities using different wireless communication technologies where `X` denotes the communication partner. These are typically other cars, road infrastructure like signal lights, or traffic management authorities. sim TD investigates established V2X use-cases in real traffic scenarios around the Hessian city of Frankfurt/ Main, Germany with a fleet of 120 cars. One of the goals of the project is to evaluate the effect of V2X-based applications on traffic and driver. It is currently the largest field operational test in this domain. Drivers are not only advised "experts" to the sim TD system but also novices. Especially for this type of drivers it is of utmost im-portance that the Human-Machine Interface (HMI) of the system is intuitive, nondistractive and features a consistent integration of all use-cases. It consists of a main area where the most relevant information is displayed and an additional area in the upper part containing six slots for additional information of parallel applications. Using two virtual buttons, the driver can also access a map and an option screen (see Figure 1). The Automotive group at "double-blinded" is responsible for developing and evaluating the HMI concept in sim TD prior to the field operational test. The V2X use-cases evaluated can be classified into three categories: Safety, traffic efficiency, and value added services. Many events belonging to these use-cases are location-based, i.e. they pinpoint to a certain geographical position. Moreover, some events are valid for a certain road segment, e.g. road works. Table 1 lists a shortened selection of use-cases, which are investigated in sim TD . Altogether, there are over 25 use-cases tested within the project. We refer the interested reader to the full list of selected functions in [START_REF] Paßmann | simTD Project result: Selection of Functions[END_REF]. Especially warning functions, e.g. congestion warning, need to be displayed way before the actual event. Those approaches to upcoming events should be presented to drivers in a consistent way for all use-cases. Besides text-only information, the use of graphical information via progress bars can be found in several navigation systems. We analyze different realizations and have a look what can be learned from general HCI research in the following section.

Background

Since the beginning of HCI research, design and display of progress is considered important and useful. For example, Myers in [START_REF] Myers | The importance of percent-done progress indicators for computer-human interfaces[END_REF] concludes that especially for long-lasting tasks users prefer this kind of progress display. In traditional HCI, the design of progress was established even before graphical user interfaces were common. It was implemented using ASCII characters (cf. Figure 2a). . Modern versions of all major Desktops come with an animation of the bars' fill color either "pulsating" from left to right (Windows) or "ribbed" from right to left, e.g. in Mac, see Figure 2, (g). One might think this change was invented because of advances in technology such as increased computing power or in order to make the graphical user interface more appealing. But interestingly, according to [START_REF] Harrison | Faster Progress Bars: Manipulating Perceived Duration with Visual Augmentations[END_REF], this type of bars result in statistically significant shorter perceived duration times when compared to their static counterparts. Another approach is hiding the actual progress from the user and displaying another one, as investigated in [START_REF] Harrison | Rethinking the progress bar[END_REF]. Harrison et al. applied non-linear functions for progress bars and found that users have a strong preference for speed-ups towards the end. On the other side, according to their results, users tend to tolerate slow-downs at the beginning better than towards the end. For tasks with dynamic completion, like defragmenting a hard-drive, such a non-linear progress bar is considered useful. Such a progress bar, on the other hand, is not appropriate for linear tasks. Other approaches try to exploit colors or different kinds of modalities. For example in [START_REF] Hamada | Color Effect on Subjective Perception of Progress Bar Speed[END_REF], six combinations of colors are considered: blue/red for progress bar foreground color and cyan/orange/gray for background color. Furthermore, [START_REF] Brewster | The design and evaluation of a vibrotactile progress bar[END_REF] encode progress into a series of vibrotactile pulses while [START_REF] Hsu | New Type of Auditory Progress Bar: Exploration, Design and Evaluation[END_REF] use auditory cues. To conclude, so far, researchers above all tried to reduce the subjective perception of waiting time, either by visual means, or by applying nonlinear functions to progress bars.

However, these strategies should only be applied, when progress of the actual task is hidden from the user. In the automotive domain this decoupling between actual and displayed spatial information is neither given nor desired. Quite contrary, it is essential that both coincide as exactly as possible, e.g. during a spatial approach to a maneuver point. Besides that, transferred to our domain, Harrison et al. suggest in [4] to represent linear tasks by linear progress. Also, the use of animations in the automotive context might induce visual distraction.

Unlike in general HCI research, consistency with respect to design of spatial approach could not be observed between several manufacturers of navigation systems. Figure 3 depicts variants from different vendors. They vary in direction (decreasing vs. increasing) and movement (upwards vs. downwards). Even a single manufacturer employs increasing and decreasing bars in different models, see (f) and (g) in Figure 3. Falk (d) realizes segmented bars as arrows to indicate the filling direction. Several vendors provide no visual directional cue at all (NavigonMobile (f), TomTom (e)). This listing is not meant to be complete, but is supposed to reveal existing inconsistency found in different navigation systems as opposed to desktop progress bar design. Despite these differences, we could however observe several common criteria: All observed designs of spatial approach are realized as straight, vertical, and segmented bars. Minimum and maximum bar states could be inferred by a frame or similar means. Additional textual information (e.g. 500 meters) was presented in every design. We decided to reflect these shared criteria by setting them as preconditions in the design of experimental conditions. Accordingly, we used textual information in combination with a graphical bar representing relative spatial information in each condition. However, the observed inconsistencies in existing systems and a lack of literature on the issue in the automotive context lead to the conclusion that still many questions are unanswered when it comes to design of spatial approach and progress:

1. Do users find graphical approach and progress bars useful? 2. Which progress bar type is preferred (increasing vs. decreasing, upwards vs. downwards, use of colors, relevance of frame)? 3. Do different progress bar variants induce different visual distraction from the driving task (accessed by eye-tracking data or driving performance)?

The aim of the experiment presented here, was to answer the questions above and derive a universal guideline for designing consistent spatial approach and progress for in-vehicle information systems. It is important to notice that dynamic bars cannot be evaluated on static screenshots as it is for example impossible to infer movement direction or rate of changes. Furthermore, watching simply animated bars is not comparable with a driver (visually) engaged in a driving task using bar animations as relative spatial information for local events. On these grounds, we decided to conduct a driving simulator experiment with a mock spatial approach and progress scenario.

Experiment

Participants

24 participants participated in this experiment, 12 men and 12 women. Their age varied between 19 years and 59 years (mean = 37.0, SD = 15.1). All of them possessed a valid driver's license for at least two years. Half of the participants owned a navigation system and indicated to use it frequently, 2 participants indicated to use a navigation system occasionally, and 10 participants have only rarely used a navigation system before. Accordingly, no absolute novices took part. Participants were paid 10 Euros for approximately 1 hour of time.

Apparatus

The experiment setup consisted of a Logitech Momo steering wheel attached to a desk and two pedals on the floor, resembling the ergonomic condition of a driver in a car (cf. Figure 4). During the experiment, the participant's task was to steer and brake with these devices according to driving requirements. A computer flat screen (size 20 inch, resolution 1440 x 1050 pixel) presenting the driving simulation was set up on a desk with a distance of about 80 centimeters to the participant's head. White digits on the lower right side of the screen displayed the current vehicle speed. Their color turned into red, whenever drivers diverged from the currently prescribed maximum speed for more than 10 kilometers per hour. We used an open source driving simulation solution, which has recently been presented in [START_REF] Math | OpenDS: A new open-source driving simulator for research[END_REF]. For this experiment, several highway tracks were generated with each track including a road works section towards its end (see also Driving Task and Tracks section). In-vehicle information about an upcoming or current traffic situation was presented at the right hand side of the driver on a seven-inch MiMo screen. A Tobii eye tracker was installed below the small information screen for recording respective gazes onto this screen. A detailed description of the information screen and the presented bar variant combinations will be provided in the following section. shows a presentation while driving within a road works section.

Information Screen and Presented Bar Types

Besides the current speed limit in the upper left corner, the type of the upcoming traffic event (road works) is presented. Most important, numerical distance indicators combined with spatial bars were presented in all cases along the general lines of [START_REF]Road vehicles -Ergonomic aspects of in-vehicle presentation for transport information and control systems -Warning systems[END_REF]. For approaching road works, a vertical bar was used and while driving through a section of road works, a horizontal bar indicated the current progress. Hence, the alignment of a bar itself would provide information whether a driver is in front of or within a road works section. Looking only at a screenshot, one cannot infer how a bar is animated. For example, it is not possible to decide, whether the bar depicted in Figure 5 (left) is decreasing upwards or increasing downwards. This is one reason why it is important to use dynamically presented bars in an actual driving task to enable a comparison between the different bar types.

In Figure 6 we present an overview of the two experimental factors composition type and moving direction leading to four experimental conditions. Three snapshots illustrate the animation of each moving bar (from the leftmost to the rightmost). For each experimental condition a combination of an approach bar (either increase or decrease; either upwards or downwards) for approaching the road works was successively combined with its consistent progress bar (increase or decrease; all moving from left to right) within the road works. This resulted in four main experimental conditions (increasing upwards: inc_up; decreasing upwards; dec_up, increasing downwards: inc_dw; decreasing downwards: dec_dw; cf. Figure 6). As an informal pretest among eight colleagues had revealed, three bar types were favored or supported, whereas one variant (downward increase: inc_dw) was not appreciated by anybody. Furthermore, horizontal progress bars moving leftwards were evaluated as highly unsuitable. For reason of design completeness, we nevertheless kept the inc_dw condition, but we omitted the leftwards progress bars -also analogous to desktop solutions.

As participants might behave differently when for the very first time encountering a bar in the road works scenario, we considered that counterbalancing presentation order of conditions would not to be sufficient with respect to our research questions. Therefore, we decided to use the rather unpopular variant inc_dw as introductory condition. A second version of the inc_dw condition lacking the boundary line was generated and added as a fifth condition. The gray framing line is used for all four main bar variant conditions and indicates absolute minimum and maximum of bar states. Therefore, it might be a helpful reference for interpretation -especially for inexperienced users who do not know the bar range yet. To test this assumption, condition inc_dw and inc_dw_fl (fl = frameless) were alternately used between participants for either the introductory track (1) or the last track [START_REF] Hsu | New Type of Auditory Progress Bar: Exploration, Design and Evaluation[END_REF]. Accordingly, the inc_dw condition was experienced only half by novice participants and half by experienced participants. This kept the inc_dw condition comparable with the remaining three major experimental conditions (inc_up, dec_up, dec_dw), which were only assigned to middle tracks [START_REF] Hamada | Color Effect on Subjective Perception of Progress Bar Speed[END_REF][START_REF] Harrison | Faster Progress Bars: Manipulating Perceived Duration with Visual Augmentations[END_REF][START_REF] Harrison | Rethinking the progress bar[END_REF]. Order of five equivalent physical driving tracks (with slightly differing distances, see also Driving Task and Tracks section) remained constant. The assignment of the three middle experimental conditions to the fixed tracks [START_REF] Hamada | Color Effect on Subjective Perception of Progress Bar Speed[END_REF][START_REF] Harrison | Faster Progress Bars: Manipulating Perceived Duration with Visual Augmentations[END_REF][START_REF] Harrison | Rethinking the progress bar[END_REF] was counterbalanced between participants to control for learning or sequence effects as well.

For each bar type (except the inc_dw_fl condition), an animated color-coded version of the approach bars was additionally presented to participants (see Figure 7). We investigated, whether participants would prefer color-coding representing proximity and accordingly urgency over plain-colored bars.

Design

Two factors, movement direction (upwards, downwards) and composition type (increasing, decreasing) were manipulated within participants. A reference frame line as visual reference for minimum and maximum bar states was used for all four conditions. The inc_dw condition was additionally presented in a frameless version (inc_dw_fl). Half of the participants started with a inc_dw track and finished with a inc_dw_fl track, whereas for the remaining half of the participants the order was reversed.

Driving Task and Tracks

Participants were instructed to drive as they would in a real world scenario. They should conform to German traffic rules (speed limits, taking the right lane whenever possible, not exceeding the current lane). Besides, they should finish each track as fast as possible. Moreover, they were informed that the small screen would provide helpful information about the upcoming traffic situations. Participants had to perform five equivalent two-lane motorway tracks without any other vehicles present. An example containing concrete distances between the events is depicted in Figure 8. All experimental tracks were designed according to the same scheme, but each track was created with different distances. This should prevent learning effects about the exact event positions and times. After an acceleration phase without speed limit (acceleration to 140 km/h), a limit of 120 kilometers per hour requiring an immediate brake reaction was suddenly presented both on a road sign and on the small information screen. This speed limit was presented due to approaching road works. After a while, further speed limits showing 100 and 80 kilometers per hour were presented successively. When arriving at the road works it became obvious that the right lane was barred and participants had to change to the left lane. After passing the road works section at a speed of 80 kilometers per hour, the speed limit is revoked and participants should accelerate again. Shortly after, the track was finished. During each track, drivers were given notice of the road works on the small screen: Before arriving at the road works (from the first speed limit onwards), the remaining distance was shown by a numerical sign in combination with a vertical approach bar. Once the single lane within the construction work was entered, numbers combined with a horizontal progress bar displayed the remaining distance until the end of the road works segment. Having described participants, apparatus, experimental conditions, and the driving task, the next section will provide a description of the overall experimental procedure.

Procedure

After having been welcomed, participants filled out a first demographic questionnaire. The experimenter explained the driving task to the participant, and had her get familiar with the setup in a first practice drive on a straight road in the simulator. Subsequently, the participant completed a first baseline drive containing merely diverse speed limits but no road works or obstacles. Afterwards, the main experimental part was started, without the participant knowing anything about the upcoming road works in the first drive. The expectancy level certainly changed for the subsequent tracks, as both road works and speed limits reoccurred in each case. Nevertheless, exact locations were varied for each track, in order to prevent time-related expectancies and ignoring of the presented distance information. Within two breaks during each track and at the end of each track, participants had to fill out a questionnaire evaluating the current bar variant. Between plain-colored bars, color-coded bar variants were presented and evaluated as well. Following all experimental tracks, a second baseline drive had to be completed. After a final questionnaire participants were paid and said goodbye.

Dependent Variables

Most importantly, we assessed user preferences regarding bar presentation in general and intuitiveness of the presented bar alternatives. We were moreover interested, whether users interpret the meaning of some bars more consistently than the meaning of others. We assessed this by simply asking participants where they located themselves and the road works on the respective bar type during a short (driving) pause. Furthermore, we evaluated participants' gaze behavior onto the information screen, as this might vary for the different bar types. Low gaze distraction is on the one hand itself favorable for the driving context, and on the other hand it might serve as an implicit measure of presentation intuitiveness. Even though our driving task was rather simple, we recorded driving performance to control for any unforeseen disturbances or effects. For all dependent variables we intended to test whether for each factor main effects and interactions could be found, finally leading to a recommendation, which bar types seem(s) most suitable.

Results

As pointed out earlier, we wanted to generate a dynamic situation in a driving context that allows above all a valid subjective evaluation of different graphical distance estimation support variants. Possible differences in driving performance or glance behavior should be investigated as further dependent variables. Accordingly, we start with a rather detailed analysis of user preferences measured at different times during the experiment with the help of tailored questionnaires.

User Preferences and Perceptions

For a start, we present several results from the concluding questionnaire at the very end of the experiment. We asked participants their general opinion about the usage of progress bars. 75 % of the participants appreciate progress bars as very useful or rather useful addition to numerical distance indicators, whereas only 25 % of the participants rated them average, little or not useful at all. Hence, for the majority of drivers using bar indicators is valuable. Equivalent answers were given for using a differing alignment (vertical vs. horizontal) for approach and progress information. This leads to the assumption that different alignments predefined for different types of spatial information were assessed as a valuable premise. We state that this supports differen-tiation between approach and progress at a glance. In terms of use-cases, participants prefer progress bars for traffic events like traffic jams (mean = 4.1), or obstacles (mean = 4.1), whereas these bars are less appreciated for events like speed limits (mean = 2.8). An alternative design draft containing small symbols indicating car and hazard position received average ratings (mean = 3.3), whereas modifying the small segments into arrows indicating the movement direction of the bar (analogous to the Falk screenshot in Figure 3d) received comparably positive ratings (mean = 3.7).

As a second step, we elaborate on the results for the vertical approach bar alternatives. During a short pause within each approach phase (see Figure 8), participants should mark on a printed screenshot of the respective bar where they located themselves, as well as the beginning of the road works. For upwards-moving bars we initially considered the road works to be represented by the upper frame line. For the downwards-moving bars, we initially considered road works to be represented at the bottom frame line. For all conditions, the driver's position was presumably represented within the frame line at the changeover from bar content to empty. Only if a participant indicated both positions accordingly her answer was coded as "correct". Participants could choose not to answer if they were unsure, but this was not counted as correct either. As becomes obvious in Figure 9, participants were quite sure about their answers for the upwards-moving bars leading to a high percentage of correct answers. Regardless of our initial hypothesis of "correct" positions, results reveal that participants immediately access concordant mental models for this type of visualization. On the contrary, major dissent occurred for the downwards-moving bars. We found a high proportion of at least partially incorrect answers and non-answers. This reveals an inconsistent interpretation (between subjects) and uncertainty for bars moving downwards. A Chi-Square test confirmed the improved performance levels for two upward compared with two downward approach bars, χ 2 (1) = 60.33, p < .05. Furthermore, participants were asked both during the test tracks as well as at the very end of the experiment, how intuitive they rated each graphical distance presenta-tion from 1 -not intuitive to 5 -very intuitive. Figure 10 shows the results for the later ratings using a stacked bar graph2 . A Repeated Measures MANOVA (i.e., we used the multivariate approach to the repeated measures analysis; see, e.g. [START_REF] Dien | Application of repeated-measures ANOVA to high-density ERP datasets: A review and tutorial[END_REF][START_REF] O'brien | MANOVA method for analyzing repeated measures designs: An extensive primer[END_REF] with the factors movement direction (upwards vs. downwards) and composition type (increase vs. decrease) was conducted for the four main bar types. Movement direction had a significant influence on ratings with upwards moving bars being rated higher than the downward ones, F(1,23) = 12.49, p < .01. However, neither composition type, F(1,23) = 2.34, p = .14 nor the interaction revealed significant effects, F(1,23) < 1, ns. Another comparison targets the factor reference frame line, which has been altered between bar inc_dw and inc_dw_fl. Even though the order (first vs. last track) was counterbalanced, ratings did not show any significant difference t(23) = -.77, ns. However, relatively low ratings for a bar increasing downwards (inc_dw) might have concealed further negative influence of a missing reference frame (floor effect). Anyway, we would suggest using a frame line, as it does not worsen results. According to the results above, upwards-moving bars (inc_up, dec_up) score higher than downwards-moving bars. For the two upwards bars, we furthermore wanted to investigate, whether additional colorcoding (see Figure 7) would influence subjective assessment. Accordingly, we conducted a Repeated Measures MANOVA on bar ratings, with the factors composition type (inc_up vs. dec_up) and color-coding (with vs. without). A significant main effect of color-coding, F(1,23) = 9.31, p < .01, indicates that participants clearly prefer color coding. Neither the other main effect of composition, F(1,23) = 2.46, p = .13, nor the interaction, F < 1, ns., led to significant results.

Beyond the four vertical approach bars, we present results for the corresponding two bars for progress within the road works (decreasing horizontal or increasing horizontal). During another short pause within the road works section (see Figure 8), participants should mark on a printed screenshot of the respective bar where they perceived themselves, as well as beginning and end of the road works. For both bar types we considered the beginning of the road works to be represented by the very left frame line and the end at the very right. Consistent with the approach bars, the driver was presumably represented within the frame line at the changeover from bar content to empty. If participants were unsure about their assessment, they could also give no answer. Results were coded as "correct" if all position indications were as we had expected. The percentage of correct answers about the car's and the road works' position was relatively high for the decreasing (83%) as well as for the increasing horizontal bar variant (88%). For the frameless variant (horizontally increasing), rather inconsistent interpretations were achieved (63%). To conclude, both framed progress bars were interpreted quite consistent with our initial assumption. These findings do at least not contradict our initial assumption that preselecting progress bars moving rightwards is feasible.

When having a look at the subjective ratings about the intuitiveness of progress bar types (cf. Figure 11), a significant tendency in favor of the decreasing progress bar (dec_ri), compared with the increasing progress bar (inc_ri) can be identified, t = 2.15, p < .05. A comparison of both increasing bar types revealed significantly better ratings for the version with frame line (inc_ri) compared with the frameless bar (inc_ri_fl), t(21) = 2.57, p < .05.

Gaze Behavior

Besides gathering subjective ratings, also participants' gaze behavior onto the small information screen was recorded and analyzed, as glance behavior can serve as a valuable indicator for visual distraction from the road. We recorded average glance duration, maximum glance duration, number of glances during each track, and the proportion of time that participants glanced onto the small screen while driving. Note that results only labeled according to the five vertical bar variants (see Table 2) but they also include the horizontal bar increasing or decreasing consistently in each track. First of all, we compared the initial and final baseline drives with an average of all four experimental conditions for all dependent variables. In the baseline drives merely the current speed limits were presented on the small screen according to the road signs in the simulator. Therefore, we expected lower values of gaze measurement for baseline tracks. This was confirmed for all collected parameters, F(1,13) > 12.79, p < .001, for 14 out of 24 participants. Consistent with the implication of this finding, the remaining 10 participants did not glance onto the small screen at all in at least one of the baseline conditions, and therefore had to be excluded from the comparison above.

Most important we wanted to have a closer look at the two experimental factors movement direction and composition type. Analogous to the self-reported metrics in user preferences section, we entered all gaze metrics (average glance duration, maximum glance duration, number of glances, gaze percentage) of the four main bar alternatives into a two-way Repeated Measures MANOVA. We did not receive any significant effects for the average glance duration, F(1,23) < 1.57, n.s., and for the maximum glance duration, F(1,23) < 1, n.s. This indicates that the duration of single glances does not vary between our variants. However, most interestingly the number of glances per track, F(1,23) = 14.05, p < .001, as well as the overall percentage of experimental time spent with looking at the small information screen, F(1,23) = 7.25, p < .05, showed a consistent main effect of composition type: Decreasing bars lead to less glances and less overall glancing time on the small information screen (cf. Figure 12). Neither significant main effects for movement direction, nor significant interactions were found. To sum up, participants rated the bars differently rather with respect to movement direction, but for gaze behavior the composition type seems to be more decisive.

Driving Performance

Even though the driving task was very simple and all the tracks were designed to be similar, we investigated driving performance to control whether any unforeseen disturbance or confound had influenced driving behavior. Reaction times upon speed limits, deviation from the current speed limit, and deviation from a predefined ideal line (drive in the middle of the respective lane, fast lane change when necessary) were logged. Thereby, Neither reaction times upon speed limits, nor deviation of the ideal line, nor deviation of the current speed maximum revealed any significant differences, all F's(1,23) < 1, ns. In summary, as expectable in this scenario no performance differences could be observed for the presented bar alternatives in the driving simulation scenario.

Discussion, Conclusion, and Future Work

Our results imply that participants appreciate the distinction of vertical approach bars and horizontal progress bars. For approach bars, upwards-moving bars are preferred over downwards-moving ones, whereas composition type ratings do not differ significantly. However, in gaze-based metrics decreasing bars induced less visual distraction from the primary driving task. This might occur because decreasing bar segments themselves contain all necessary spatial information without requiring a comparison with a further reference (e.g. frame line). Furthermore, for horizontal progress bars the decreasing bar compatibly received higher ratings as well. Moreover, color-coding leads to higher intuitiveness ratings, but obviously designers need to consider the trade-off between potential benefit and increased visual complexity in each individual case.

According to our findings, we suggest to use an approach bar decreasing in an upward direction. Spatial progress within a traffic event should consistently be presented via a horizontal bar decreasing to the right (see also Figure 13). On account of these guidelines, we designed graphical bars visualizing spatial approach and progress for the sim TD system that has been introduced earlier. Figure 14 depicts example screenshots containing a final solution for approach and progress bars. In the field operational test the bars are consistently used for all appropriate usecases, e.g. obstacle warnings, traffic jam ahead warning, road signs. Our results are a first step towards a consistent common solution for assisting the driver with graphical distance and progress information. Agreed design guidelines for human interface designers in the automotive domain would help users to immediately comprehend the presented spatial information in diverse in-vehicle information systems at a glance and with minimal cognitive effort. However, further research is needed to quantify benefits from the most suitable bar version compared with no graphical distance indicator. Even though we found out that the majority of drivers prefer bars in our study, we still lack knowledge, whether a well-designed bar should be prescribed for all appropriate systems. For this comparison of driver distraction we would recommend a more realistic driving environment providing more ecologic validity. Accordingly, differences in gaze behavior and driving performance would become more obvious -preparing the ground for investigation of additional moderators like gender or prior experience.

Figure 1 .

 1 Figure 1. The sim TD HMI depicting three virtual screens and symbol area.

Figure 2 .

 2 Figure 2. Design of progress in different Operating Systems (b)-(g) and in ASCII code (a). Nowadays, horizontal filling bars (from left to right) constitute a common implementation on Desktop Operating Systems and mobile devices. With Windows 2000 and Windows XP, segmented bars were invented but replaced by continuous bars in Windows Vista/7 (Figure 2, (c), (f), (d)). Modern versions of all major Desktops come with an animation of the bars' fill color either "pulsating" from left to right (Windows) or "ribbed" from right to left, e.g. in Mac, see Figure2, (g). One might think this change was invented because of advances in technology such as increased computing power or in order to make the graphical user interface more appealing. But interestingly, according to[START_REF] Harrison | Faster Progress Bars: Manipulating Perceived Duration with Visual Augmentations[END_REF], this type of bars result in statistically significant shorter perceived duration times when compared to their static counterparts. Another approach is hiding the actual progress from the user and displaying another one, as investigated in[START_REF] Harrison | Rethinking the progress bar[END_REF]. Harrison et al. applied non-linear functions for progress bars and found that users have a strong preference for speed-ups towards the end. On the other side, according to their results, users tend to tolerate slow-downs at the beginning better than towards the end. For tasks with dynamic completion, like defragmenting a hard-drive, such a non-linear progress bar is considered useful. Such a progress bar, on the other hand, is not appropriate for linear tasks. Other approaches try to exploit colors or different kinds of modalities. For example in[START_REF] Hamada | Color Effect on Subjective Perception of Progress Bar Speed[END_REF], six combinations of colors are considered: blue/red for progress bar foreground color and cyan/orange/gray for background color. Furthermore,[START_REF] Brewster | The design and evaluation of a vibrotactile progress bar[END_REF] encode progress into a series of vibrotactile pulses while[START_REF] Hsu | New Type of Auditory Progress Bar: Exploration, Design and Evaluation[END_REF] use auditory cues. To conclude, so far, researchers above all tried to reduce the subjective perception of waiting time, either by visual means, or by applying nonlinear functions to progress bars.However, these strategies should only be applied, when progress of the actual task is hidden from the user. In the automotive domain this decoupling between actual and displayed spatial information is neither given nor desired. Quite contrary, it is essential that both coincide as exactly as possible, e.g. during a spatial approach to a maneuver point. Besides that, transferred to our domain,Harrison et al. suggest in [4] to represent linear tasks by linear progress. Also, the use of animations in the automotive context might induce visual distraction.

Figure 3 .

 3 Figure 3. Design of spatial approach in different navigation systems. Legend: ''=Upwards; ''=Downwards; '>'=Increasing; '<'=Decreasing, ''=Feature not present

Figure 4 .

 4 Figure 4. Experimental Setup

Figure 5

 5 Figure 5 depicts an example of all items displayed on the information screen.

Figure 5 .

 5 Figure 5. Two screenshots of displays for the road works use-case. Distance is indicated in numerical form (in 400m/ 300m) accompanied by a graphical bar representing relative spatial information. The left side depicts a screenshot for approaching road works and the right sideshows a presentation while driving within a road works section.

Figure 6 .

 6 Figure 6. Three snapshots illustrate the animation of each bar variant. Overview of four mainexperimental conditions: For each track an approach bar (either increase or decrease; either upwards or downwards) was combined with its consistent progress bar (increase or decrease; all moving from left to right).

Figure 7 .

 7 Figure 7. Additional color-coded versions of approach bars presented to participants.

Figure 8 .

 8 Figure 8. Schematic overview of an experimental track containing speed limits, road works, and two pauses for questionnaires.

Figure 9

 9 Figure 9 Correct (red) versus at least partly incorrect (false or no answer; blue) answer percentage to the question: "Where are your car and the beginning of the road works located on this bar?". Results indicate consistency of bar assessment with xy.

Figure 10 .

 10 Figure 10. Stacked bar graph illustrating the percentage of participants rating each approach bar from 1 -not intuitive to 5 -very intuitive. Darker bars indicate higher intuitiveness ratings.

Figure 11 .

 11 Figure 11. Stacked bar graph illustrating the percentage of participants rating each progress bar from 1 -not intuitive to 5 -very intuitive. Darker bars indicate higher intuitiveness ratings.

Figure 12 .

 12 Figure 12. Number of glances and percentage of gazes onto the small information screen during a track. Decreasing bars lead to significantly lower visual distraction than increasing bars.

Figure 13 .

 13 Figure 13. Design guidelines for graphical bars representing spatial information when approaching local traffic events or for current progress within a traffic event section.

Figure 14 .

 14 Figure 14. Official sim TD screenshots of road works information containing a color-coded approach bar (left) and a single-colored horizontal progress bar (right).

Table 1 .

 1 Some selected sim TD use-cases involving approach and/or progress information.

	Category	Use-case
	Safety	Lost Cargo Warning
	Safety	Congestion Ahead Warning
	Safety	Animals on the Road Warning
	Safety	Approaching Emergency Vehicle Warning
	Safety	Road Weather Warning
	Efficiency	Traffic Sign Assistant
	Efficiency	Road Works Information System

Table 2 .

 2 Average fixation duration, number of glances, maximum glance duration, and percentage of time looking at the information screen for the five tracks.

http://www.simtd.de/

The later overall ratings did not differ compared with the earlier ratings, F(1,20) = 2.64, ns. The interaction between point of time and bar variants also missed the level of significance, F(4,17) = 2.42, p = .09. Accordingly, the pattern of ratings does not change significantly over time and it is sufficient to only present the later ratings in detail.

Acknowledgements

This work was funded within the project sim TD by the German Federal Ministries of Economics and Technology as well as Education and Research, and supported by the Federal Ministry of Transport, Building and Urban Development. EIT ITC Labs supported OpenDS via the activity Apps for Your Car (AIMS 13131) in the context of the project GetHomeSafe within the scope of the Seventh Framework Program of the European Commission.


Raghavendra Reddy Gudur

Alethea Blackler
email: a.blackler@qut.edu.au

Vesna Popovic
email: v.popovic@qut.edu.au

Doug Mahar
email: d.mahar@qut.edu.au

Ageing, Technology Anxiety and Intuitive Use of Complex Interfaces

Keywords: Prior-experience, Technology anxiety, self-efficacy, Intuitive interaction, Ageing, Complex Interfaces

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Many older people have difficulties in using contemporary consumer products that have complex interfaces and extensive functionalities. Past decades have seen a substantial increase in the use of technology in all aspects of daily living. Inability to use contemporary technologies such as computers, mobile devices, the Internet, and ever increasing self-care medical devices puts the older population at a disadvantage in terms of their ability to live and function independently [START_REF] Czaja | The impact of aging on access to technology[END_REF]. This research hypothesised that designing technological products that are more intuitive for older people to use can solve this problem. An intuitive interface requires minimal learning as it mostly relies on technology prior experience/familiarity of the users for effective interaction [START_REF] Blackler | Intuitive interaction with complex artefacts[END_REF][START_REF] Hurtienne | Design for intuitive use -Testing image schema theory for user interface design[END_REF]. However, gradual shift of consumer products from hardware-based to microprocessor controlled software-based technologies has brought a higher level of abstraction into interaction with products [START_REF] Hurtienne | Design for intuitive use -Testing image schema theory for user interface design[END_REF][START_REF] Rama | Technology generation and Age in using layered user interfaces[END_REF]. Older generations, who grew up with relatively older technological paradigms, have been left behind. Although the use of technologies such as computers and the Internet is increasing among older people, an age-based digital divide still exists [START_REF] Czaja | The impact of aging on access to technology[END_REF]. Research also suggests that older people encounter more difficulties when using interfaces that use complex (nested) interface structures compared with simple (flat) interface structures [START_REF] Rama | Technology generations handling complex user interfaces[END_REF][START_REF] Detweiler | The effects of display layout on keeping track of visuo-spatial information[END_REF]. Despite this a gradual move in product interface interaction towards touch-based input systems with small screens has necessitated extensive use of multi-layered interface structures.

Not being pace with contemporary technology or lack of technology prior knowledge could lead to low perceived technology self-efficacy, which in turn has the potential for causing technology anxiety. Both low domain-specific prior knowledge and technology anxiety can impede successful use of complex contemporary technological products [START_REF] Eisma | Mutual inspiration in the development of new technology for older people[END_REF]. In addition, some research [START_REF] Bastick | Intuition : evaluating the construct and Its impact on creative thinking[END_REF] also suggests that stress, anxiety and oppressive environments are not conducive to intuitive thinking. However, what is not well understood is the impact of these conditions on intuitive use of a product. This paper discusses a study that investigated the impact of anxiety, complexity in interface structure (nested versus flat) and age on intuitive interaction.

Intuitive interaction

Interaction design professionals often feel that they understand what 'intuitive interaction' is; however, very few really define it clearly [START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF][START_REF] Turner | Towards an account of intuitiveness[END_REF]. In the context of interaction design, Blackler [START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF] and Hurtienne [START_REF] Hurtienne | Prior Experience and Intuitive Use: Image Schemas in User Centred Design[END_REF], based on their literature reviews of the nature of intuition, suggest that -intuitive use of product interfaces involves non-conscious use of user's prior-knowledge related to the product in use. In other words, the user is familiar (based on their earlier encounter with similar products or other relevant experiences) with different features and functions of the product [START_REF] Hurtienne | Prior Experience and Intuitive Use: Image Schemas in User Centred Design[END_REF][START_REF] Blackler | Investigating users' intuitive interaction with complex artefacts[END_REF]. Intuitive use of an interface can be recognised by the following characteristics: It is fast and effortless, it is generally non-conscious and does not involve conscious reasoning [START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF].

Complexity in contemporary product interfaces

There is a gradual shift in most consumer products towards a touch-based interface paradigm. If this trend continues, it will not be long before most of the technology older people encounter in their day-to-day activities will be based on this interface paradigm. On a brighter note, recent research suggests that touch-based products are much easier to learn and older users could successfully use them regardless of their age-related cognitive or physical deficiencies [START_REF] Häikiö | Touch-based user interface for elderly users[END_REF][START_REF] Taveira | Review Study of Computer Input Devices and Older Users[END_REF][START_REF] Isomursu | Experiences from a Touch-Based Interaction and Digitally Enhanced Meal-Delivery Service for the Elderly[END_REF]. Interestingly, Umemuro's [START_REF] Umemuro | Lowering elderly Japanese users' resistance towards computers by using touchscreen technology[END_REF] research on older people's aptitudes to computer found that the anxiety factor declined significantly in the touch screen condition. However, further research needs to be done in this regard to clearly establish which types of input devices are optimal for older people [START_REF] Czaja | Information Technology and Older Adults, in Human Computer Interaction: Designing for Diverse Users and Domains[END_REF].

On the other hand, this shift to touch screen has also resulted in smaller screen sizes with little or no tactile feedback [START_REF] Ziefle | Information presentation in small screen devices: The trade-off between visual density and menu foresight[END_REF]. In addition, with increased functionality and small screen sizes, it has become necessary either to decrease the size of interface elements so as to fit them on the small screen, or to resort to some amount of nesting of the interface to fit all the functions of the device on the screen. Decreasing the size of text and icons will result in visibility and readability issues, especially for older people. Then again, a nested design, if its structure is too deep or complex, could increase the cognitive load on the users.

1.3

Breadth versus depth in interface design structure Fundamental characteristics of menu structures have been well researched over the past couple of decades. In particular, tradeoffs between breadth (the number of options in a level) and depth (the number of levels) in menu structures have been extensively empirically investigated and analysed; for example: [START_REF] Detweiler | The effects of display layout on keeping track of visuo-spatial information[END_REF][START_REF] Zaphiris | Age related differences and the depth vs. breadth tradeoff in hierarchical online information systems[END_REF][START_REF] Kiger | The depth/breadth trade-off in the design of menu-driven user interfaces[END_REF][START_REF] Geven | Depth and breadth away from the desktop: the optimal information hierarchy for mobile use[END_REF][START_REF] Landauer | Selection from alphabetic and numeric menu trees using a touch screen: breadth, depth, and width[END_REF][START_REF] Miller | Depth/breadth tradeoff in hierarchical computer menus[END_REF]. Most of these studies agree that broad menu structures result in shorter times and better accuracy.

A flat/broad interface is one that has only one level of menu with all the options arranged in a grid fashion, as shown on the left in Figure 1. A nested/multi-layered interface has more than one level of menu. In a nested or multi-layered interface, menu options for the second level onwards are only displayed when one of the menu options in the first level is activated, as shown on right of Figure 1. Menu structure on mobile devices presents a different set of problems. Their screen sizes are often smaller and they usually use touch or pen-based interfaces. A study of preferences for menu structure of mobile devices found that participants preferred a layered (nested) rather than broad menu structure [START_REF] Geven | Depth and breadth away from the desktop: the optimal information hierarchy for mobile use[END_REF]. More specifically, they preferred a structured hierarchy with four to eight items per level.

Research suggests that older people encounter more difficulties when using interfaces that use nested/multi-layered structures compared with flat/broad interface structure [START_REF] Rama | Technology generations handling complex user interfaces[END_REF][START_REF] Detweiler | The effects of display layout on keeping track of visuo-spatial information[END_REF][START_REF] Lim | Designing inclusive ICT products for older users: taking into account the technology generation effect[END_REF]. They are also much slower using nested interfaces and tend to prefer shallower interfaces that offer better spatial orientation [START_REF] Zaphiris | Age related differences and the depth vs. breadth tradeoff in hierarchical online information systems[END_REF]. A nested interface, by its very nature, offers very few cues to the spatial location of different nested functions. Therefore, these types of interfaces impose a greater demand on the user's working memory. In general, working memory function deteriorates with age [START_REF] Czaja | The impact of aging on access to technology[END_REF]. Research suggests that broad or flat structured interfaces rely much less on cognitive abilities and, hence, may be much more beneficial for older users [START_REF] Rama | Technology generation and Age in using layered user interfaces[END_REF][START_REF] Detweiler | The effects of display layout on keeping track of visuo-spatial information[END_REF].

Anxiety, stress and intuitive interaction

Although anxiety and stress are known to interfere with intuitive thinking [START_REF] Bastick | Intuition : evaluating the construct and Its impact on creative thinking[END_REF], there is no research available that clearly points to the impact of anxiety on intuitive interaction. Blackler's [START_REF] Blackler | Intuitive interaction with complex artefacts[END_REF][START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF] research investigating the hypothesis that intuitive use of prod-ucts is based on prior experience with similar products did attempt to use the presence of anxiety in the participants as an indication of non-intuitive use. However, it was noted in the study that the psychophysiological tools used to measure heart rate and electrodermal activity (to indicate when participants were anxious) did not provide useful data due to latency issues. On the other hand, it was noted that there were occasions when, towards the end of the session, it was observed that the participants were making mistakes with features they had used correctly earlier. While causes of these moments were not ascertained, it was noted that they could be a result of anxiety or fatigue interfering with intuition [START_REF] Blackler | Intuitive interaction with complex artefacts[END_REF][START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF]. Overall, the outcomes of these experiments were inconclusive in terms of establishing a clear relationship between anxiety and intuitive use.

Method

Experiment design

The objective of this experiment was to investigate the relationship between age, anxiety, and intuitive use of complex interface structure (nested versus flat). The following hypotheses were framed to investigation this relationship.

1. Nested interface structure will have an adverse effect on time to complete the task, intuitive uses and errors on older when compared with younger participants. 2. Participants who score low on the Technology Prior Experience Questionnaire will also score low on the Self-efficacy Questionnaire and report high anxiety on the State-Trait Anxiety Inventory (STAI) questionnaire. 3. Anxiety, induced by stressful conditions, will have an adverse impact on the time to complete the task, intuitive uses and errors in both younger and older participants. This experiment used a mixed-factorial design (Table 1). The Dependent Variables (DVs) for this experiment were time on task, percentage of errors and percentage of intuitive uses. Independent Variables (IVs) were, interface type, age and stress with their levels listed in Table 1. Interface type -flat or nested -was the repeated factor and low or high stress conditions were the between subjects factors.

Participants

Overall 54 participants (29 male, 25 female), in five age groups (17 to 34, n = 12; 35 to 49, n = 10; 50 to 64, n = 12; 65 to 72, n = 10; 73+ Years n = 10), between the ages 18 to 84 years (M = 54, SD 18) participated in this experiment. They were recruited from different sources to maintain a good sample of the general population. Individuals from organisations (for example, sports clubs, educational institutes, recreational facilities and retirement resorts) were asked if they could volunteer to take part in the study. All the participants were screened for visual acuity (corrected or uncorrected) using a Snellen chart. All participants were given a small gift as a token of appreciation (a $3AU worth scratch lottery ticket or coffee voucher) and were entered into a $200AU shopping voucher draw.

Apparatus and measures

Technology Prior Experience Questionnaire This was a two-part questionnaire to capture participants' exposure to, and knowledge of, technologies related to the mediator product used for this experiment.

Self-efficacy Questionnaire

This questionnaire was based on a well-tested instrument to measure perceived general self-efficacy (GSE) that was suggested by Schwarzer, and Jerusalem [START_REF] Schwarzer | Generalized Self-Efficacy scale, in Measures in health psychology: A user's portfolio, Causal and control beliefs[END_REF]. For the specific self-efficacy (SSE), a short questionnaire, as suggested by Cassidy and Eachus [START_REF] Cassidy | Developing the Computer User Self-Efficacy (CUSE) Scale: Investigating the Relationship Between Computer Self-Efficacy, Gender, and Experience with Computers[END_REF] was used , SSE was specifically designed to measure perceived selfefficacy on using domain-specific technology.

STAI Questionnaire

The State-Trait Anxiety Inventory (STAI) is a self-evaluation questionnaire administered to measure level of state and trait anxiety [START_REF] Spielberger | Understanding stress and anxiety. Life cycle series1979[END_REF]. A short form six-question questionnaire was used to measure current state of anxiety [START_REF] Marteau | The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI)[END_REF] Mediator product A touch-based device (iPad) was used as the mediator platform for the experiment. This decision was based on observing the increasing use of touch-based interfaces for consumer products. The mediator interface for this experiment was designed based on research [START_REF] Reddy | Ageing and use of complex product interfaces[END_REF][START_REF] Reddy | The effects of cognitive ageing on use of complex interfaces[END_REF] that suggested that there was a significant negative correlation between visuospatial sketchpad capacity and time to complete the task and the number of errors. The number of controls in the mediator product for this experiment was kept at 5, to keep it within the visuospatial sketchpad capacity of an average older participant [START_REF] Charness | Visual Short-term Memory and Aging in Chess Players[END_REF][START_REF] Cowan | The magical number 4 in short-term memory: A reconsideration of mental storage capacity[END_REF] . Interface structure was kept at an optimal two levels [START_REF] Miller | Depth/breadth tradeoff in hierarchical computer menus[END_REF]. At the same time, the product interface was designed so that, to avoid floor or ceiling effects, it was not too easy for the younger and not too difficult for the older participants. The design of the interface and the task difficulty was established based on the outcome of two pilot studies. Overall 12 participants participated in the pilot studies in three age groups young (20-39 years), middle-aged (40-60 years) and old (61+).

The tasks for this experiment were designed to emulate a real-life, meaningful situation, as Hawthorn [START_REF] Hawthorn | Interface design and engagement with older people[END_REF] suggests that involving the participants in the task is very important to gain meaningful data for the experiment. The task scenarios were based on the real life situation of pet sitting. Moreover, they provided enough interest for both younger and older participants during the pilot study. The tasks that participants were asked to perform were scripted to make sure all participants went through the same number of steps to complete them successfully.

Figure 2 illustrates the layout of the screen with different elements. Interface for the task was displayed only in black, white and greyscale. This was done to control any colour perception issues that the participants might have. The low stress condition was induced through positive feedback provided through a bar located on the top centre of the screen. Similarly, high stress condition was induced through negative feedback provide through the feedback bar. Feedback was not tied to the actual performance of the participant. Depending on the condition (low or high stress) the performance bar changed its status (towards excellent or poor) at certain pre-determined milestones of the task. In the low stress condition, the feedback given was always positive, no matter what the actual performance of the participant was. Similarly, on the high stress condition, the feedback was negative no matter what the actual performance was.

Task sheet

Two task sheets were prepared that described the two task scenarios, Play and Walk, for the participants. They were asked to read the task scenario before they were asked to start on the tasks.

Procedure

The Experiment was conducted in a controlled laboratory setting. The whole experiment was scripted to make sure all the participants followed similar patterns of events, timing, sequence of tasks and instructions from the experimenter. Participants within each age group were randomly assigned to one of the stress conditions, and each completed two task scenarios (Play and Walk), one each on a flat and nested interfaces. To avoid sequencing effects, the tasks were counter-balanced by alternating the sequence of interfaces (flat and nested) and the two task scenarios (Play and Walk) on the device. Two cameras were used to record the experiment for later analysis using the Observer software. One camera was positioned to record participants' facial expressions and body language; the second camera was positioned to record tasks performed by the participant on the screen. Once a participant was allocated to a group, they were introduced to the lab environment and experimental setup, and were screened for visual acuity using the Snellen chart. They were provided with the information package and consent form. Participants were also informed that they could stop the experiment at any time and could request the deletion of all the record of their participation.

Participants were then provided with the Technology Prior Experience Questionnaire followed by the combined General and Specific Self-efficacy Questionnaire. As not all participants were familiar with the mediator platform (iPad), they were asked to use a simple calculator application on the platform to get a feel for the device. This was also done to ensure that the seating and the position of the device was comfortable for the participants, as it was observed during the pilot study that some of the older participants were bothered by the glare that could occur when the device was placed at certain angles and positions.

Once the participants were comfortable with the setup, they were provided with the STAI Questionnaire and were asked to complete it based on their current state. Then after this they were given the Task 1 scenario sheet, and after making sure they understood what was expected of them, they were asked to start on the task.

After completing Task 1, they were provided with the STAI Questionnaire for the second time to record their current state of anxiety. Once they completed the STAI, they were provided with the Task 2 scenario sheet. After making sure they understood what was expected of them they were asked to start on the Task 2. Soon after completing the Task 2 they were asked to complete the STAI Questionnaire for the third and final time.

After the participants completed the third STAI Questionnaire, they were debriefed. This was especially needed for the participants in the high stress condition, as some tended to get a bit upset with their performance. It was explained to them that the performance bar was rigged and was not a reflection of their actual abilities or performance.

Data analysis

The dependent variables time on task, percentage of intuitive uses, and errors were central to this experiment. Time on task is an important indicator of intuitive use. As 'Intuitive interaction' is defined as fast and generally non-conscious [START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF]. Time on task and errors were relatively easy variables to measure. On the other hand, collecting data on actions that are non-conscious was much more challenging. Data on intuitive use was acquired based on observations using Noldus Observer software. Section 3.2 describes heuristics for coding different IVs. The data from the Observer application, the questionnaires and the CogLab application were later exported into statistical analysis software (SPSS) for analysis.

Coding heuristics

The coding heuristics were based on earlier studies of intuitive interaction [START_REF] Blackler | Investigating users' intuitive interaction with complex artefacts[END_REF]. There are altogether seven codes used for coding the tasks in this experiment. Data from four of these codes are presented in this paper. Time on task: This code records a start and finish time and calculates time on task.

Intuitive use: As intuitive process does not involve conscious reasoning, it is one of the most difficult codes to operationalise [START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF][START_REF] Hurtienne | Image schemas and Design for intuitive use[END_REF]. Blackler [START_REF] Blackler | Investigating users' intuitive interaction with complex artefacts[END_REF], based on her work on the nature of intuitive interaction, suggests that an intuitive use can be recognised by five indicators: 1) Lack of evidence of conscious reasoning (An intuitive use is often based on very fast decision making with no evidence of reasoning. There is often a lack of verbalisation and, at times, verbalisation follows an action.); 2) Expectation (Participants with very specific prior knowledge about the event are certain about the outcome of their actions.); 3) Subjective certainty of correctness (Confidence of participants executing an event.); 4) Response latency (When a participant executes an event quickly without hesitation.); and, 5) Prior knowledge (When participants indicate their earlier encounter with a similar event). All of this information was extracted from audio video recording of the tasks. An event was coded as intuitive use only when a participant showed two or more of the above indicators [START_REF] Blackler | Intuitive Interaction with Complex Artefacts: Empirically-based research[END_REF]. The most certain way of recognising an intuitive use is when an event is executed quickly without hesitation, and when verbalisation follows the action. However, in a study such as this where participants with very diverse sensorimotor and cognitive abilities are involved, it becomes difficult to establish a baseline for both of these indicators. It is well established that ageing slows down motor responses and this slow-down is not linear [START_REF] Fisk | Designing for Older Adults : Principles and Creative Human Factors Approaches[END_REF]. In other words, two people might share the same chronological age but may have very different reaction times. These issues were resolved by establishing a baseline response time for each participant. Each participant's observational data was coded multiple times until the differences between correct non-intuitive use and intuitive use was clearly recognisable. To further minimise coding errors, two independent experienced raters coded the audiovisual recording. An inter-rater reliability analysis using the Kappa statistic was performed to determine consistency of coding for intuitive use between the two raters. The inter-rater reliability for coding intuitive uses by the two raters was found to be Kappa = .77 (p < 001). Kappa values between 0.60 to 0.79 are considered to indicate substantial agreement between the raters [START_REF] Landis | The measurement of observer agreement for categorical data[END_REF]. Correct non-intuitive use: Participant completed an event successfully with the use of reasoning or when they had learnt from earlier error. Use of reasoning is indicated by: hesitation in action, latency between the exposure to the event and response, and verbalisation preceding the response. Error: This code was used when a participant was unable to complete an activity. Indicated by their using a wrong function or overlooking it.

Results

The results are organised into two sections: one will look at the effects of different measures used in this experiment; and other presents the relationship between the type of interface, anxiety and DVs.

Effects of different conditions and measures used in the experiment

Technology prior experience

Older people were much more diverse in their capabilities and exposure to the technology. Younger people tended to score much higher on technology prior experience score and were also much more homogenous in their capabilities,

Effect of stress condition on reported state anxiety

Before proceeding to analyse the rest of the data it was important to check if the method used for inducing stress had the planned effect on the participants. A two-way ANOVA with 5 age groups and two stress conditions as it factors revealed that there was a significant effect of age [F(4,44) = 3.73, p = .011, η p 2 = .25] and stress condition [F(1,44) = 6.45, p = .015, η p 2 = .13] on the anxiety reported on STAI Questionnaire. There was no significant interaction between the age and stress conditions [F(4,44) = 0.75, p = .561, η p 2 = .064]. A TukeyHSD post-hoc test revealed a significant difference between age groups 35 to 49 and 73+ years (p = .016). Except for the 35 to 49 years age group, all other groups reported more anxiety on high stress condition compared with low stress condition. Overall, the method used for inducing stress appears to have worked to a large extent..

Perceived self-efficacy, technology prior experience and anxiety

As anticipated, participants with low technology prior experience (TP) had reported low perceived specific self-efficacy (SSE) and high anxiety on the STAI. Although there was a significant correlation between perceived general self-efficacy (GSE) and technology prior experience (TP), [r (52) = .453, p = .001] between GSE and SSE [r (52) = .526, p < .001] and between SSE and TP [r (52) = . 650, p < .001] there was no significant correlation between the perceived GSE and anxiety [r (52) = -.172, p = .212] or between TP and anxiety [r (52) = -.198, p = .151]. On the other hand, SSE had a significant correlation with reported anxiety [r (52) = -.269, p = .049].

Overall, TP has a significant positive correlation with both the SSE and GSE and, SSE has a significant negative correlation with the reported anxiety. In other words, participants with more TP reported higher SSE and participants with higher SSE reported lower anxiety on the STAI.

Interface structure

It is also important to stress that the nested interface requires a higher number of responses (M = 22, SD = 7.3, N= 54) from participants, when compared to the flat interface (M = 16, SD = 4.5, N= 54). The nature of the nested interface is such that the controls are accessed through two pop-up menus and the additional action of using this two menu controls increases the number of responses needed to complete a task.

Age, Type of Interface, Anxiety and DVs

All ANOVA analyses reported in this section are three-way, interface type (flat, nested) x age (18 to 34 years, 35 to 49 years, 50 to 64 years, 65 to 72 years and 75+ years)

x stress condition (Low, High), mixed factorial design, with interface type (flat and nested) as the repeated measure factor.

Time to complete the task

Due to the violation of homogeneity revealed by Levene's test, for flat interface [F(4,49) = 12.72, p < .001], and nested interface [F(4,49) = 12.09, p < .001], a strict alpha of .025 was used for this analysis [START_REF] Keppel | Design and Analysis: a Researcher's Handbook[END_REF]. A three-way mixed ANOVA showed that there was a significant effect of interface type on time to complete the task [F(1,44) = 24.53, p < .001, η p 2 = .36] (Figure 5). Participants took significantly more time to complete the task on the nested interface (M =134 seconds SD = 71) when compared with the flat interface (M =102 seconds SD = 40).

Age also had a significant effect on time to complete the tasks: F(4,44) = 26.69, p < .001, η p 2 = .71. TukeyHSD post-hoc test revealed that the 73+ years age group took significantly more time to complete the task when compared with the four younger age groups (p < .001). The age group 65 to 72 years also took significantly more time than the youngest (17 to 34 years) age group (p = .003). There were no significant differences between the three younger age groups.

There was a significant interface type by age interaction: F(4,44) = 3.63, p = .012, η p 2 = .25. This shows that the time to complete the task in age groups differed between the flat and nested interfaces. To break down these interactions, contrasts were performed comparing different age groups between nested and flat interfaces. Type of interface had a significant effect on the 73+ (P < .001) and 65 to 72 year age group (p .013). Both of these age groups took more time to complete the task on the nested interface when compared to There was also a significant interface type by stress condition interaction: F(1,44) = 5.68, p = .021, η p 2 = .12. This indicates that the time to complete the task on both interfaces differed between stress conditions. Contrasts revealed that the time to complete the task on the nested interface differed significantly between High and Low stress conditions (p = .012). On the flat interface there was no significant time difference between the Low (M = 101 seconds, SD = 47) and High stress (M = 103 seconds, SD = 33) conditions. Interestingly, on nested interface, participants took significantly less time in High stress condition (M = 120 seconds, SD = 48) when compared with Low stress condition (M = 149 seconds, SE = 86).

There was also a non-significant (due to violation of homogeneity) interaction between age and stress condition: F(1,41) = 2.78, p = .032, η p 2 = .21. Although the interaction was not significant, it is mentioned here as the effect size is very large. This interaction indicates that the effect of stress differed between the age groups. Figure 5 shows these differences clearly. On both stress conditions (Low Figure 5A, High Figure 5B) all age groups took most time to complete the task on the nested interface. As can be seen, the oldest age groups took a lot more time to complete the tasks on both interface types; also, the time differences increased between the nested and flat interfaces. Differences between the other three age groups were not significant.

Interestingly, in the high stress condition, the younger age group (17 to 34 years) took less time on the nested interface compared to the flat interface under High stress condition. Moreover, older age groups took a lot less time to complete the task on both interfaces under High stress condition compared with Low stress condition. Age had a significant effect on percentage of intuitive uses: F(4,44) = 5.33, p = .001, η p 2 = .33 (Figure 6). TukeyHSD revealed that the age effect was significant between the age groups 17 to 34 and 65 to 72 years (p = .008), 35 to 49 and 65 to 72 years (p = .004), 35 to 49 and 73+ years (p = .048). There was no significant difference among the older three age groups.

There was also a significant three-way interaction between interface type by age by stress condition: F(4,44) = 2.97, p = .029, η p 2 = .21. This indicates that the interface type by age interaction was different for Low and High stress conditions. Contrasts were performed to reveal the age by interface type interaction under Low and High stress conditions. These revealed that in Low stress condition (Figure 6A) age had a significant effect on both flat , F(4,22) = 3.38, p = .027, η p 2 = .38, and nested, F(4,22) = 6.36, p = .001, η p 2 = .54, interfaces. A pairwise comparison using Bonferroni correction revealed a significant difference between the age group 35 to 49 and 65 to 72 years on flat interface, p = .031. There was a significant difference between the 35 to 49 and 50 to 64 (p = .013) age groups, and between the 65 to 72 (p = .003), and 73+ (p = .003), age groups on nested interface. Basically, the 35 to 49 age group's behaviour was a mirror image of the rest of the age groups (Figure 6A).

In High stress condition (Figure 6B) the effect of age was significant for nested interface [F(4,22) = 3.85, p = .016, η p 2 = .41]. A pairwise comparison using Bonferroni correction revealed no difference between age groups on the flat interface (Figure 6A). However, there were significant differences between the 17 to 34 and 65 to 72 years (p = .025) age groups, and between the 17 to 34 and 73+ (p = .049) years age groups on Nested interface. Interestingly, similar to the results for time on task, older age groups did better under stressful conditions. The 50+ age groups used both types of interfaces more intuitively under the High stress condition.

Percentage of errors

Age had a significant effect on the percentage of errors: F(4,43) = 3.11, p = .025, η p 2 = .22. Overall, older age groups made more errors on both types of interfaces when compared with the younger age groups. However, the type of interface had no effect on errors made (Figure 7).

There was a significant interaction between age and stress condition: F(4,43) = 2.64, p = .047, η p 2 = .20. This indicates that the percentage of errors made between age groups differed between the Low and High stress conditions. Contrasts revealed that in Low stress condition (Figure 7A), age had a significant effect on use of the flat interface: F(4,43) = 4.94, p = .002, η p 2 = .31. A pairwise comparison using Bonferroni correction revealed that significant differences were only observed between the 35 to 49 and 65 to 72 years age groups with the flat interface (p = .001).

Under Low stress condition on the flat interface, the 65 to 72 years (M = 17, SD = 7) age group made substantially more errors compared to the 35 to 49 year (M = 3, SD = 2) age group. Similar to other DVs, older age groups appear to have done better under High stress condition when compared with Low stress condition.

Discussion

This experiment was designed to investigate the relationships between age, anxiety, and intuitive use of complex interface structure (nested versus flat).

As hypothesised, people who scored low on Technology prior experience (TP) and Specific Self-efficacy (SSE) reported higher anxiety. This supports the research that suggests a relationship between age, cognitive decline, technology adoptions, selfefficacy and technology anxiety [START_REF] Czaja | Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE)[END_REF][START_REF] Bandura | Self-Efficacy: The Exercise of Control[END_REF]. Similarly, all age groups took significantly more time to complete the tasks on the nested interface when compared with the flat interface, probably because the nested interface needed more actions to complete the task. However, these differences between nested and flat interface use increased with age. Age also had a significant effect on time to complete the tasks on both types of interface. All age groups used the flat interface more intuitively compared with the nested interface. This finding supports existing data that suggests that older people find nested interfaces more difficult to use [START_REF] Rama | Technology generations handling complex user interfaces[END_REF][START_REF] Detweiler | The effects of display layout on keeping track of visuo-spatial information[END_REF].

Contrary to what was hypothesised, although older age groups made more errors, there were no significant differences in error rates between use of the nested and flat interface types. Similarly, anxiety had different effects on the younger and older age groups. Older people (65+) reported more anxiety than both the younger age groups. This supports Eisma et al.'s [START_REF] Eisma | Mutual inspiration in the development of new technology for older people[END_REF] research which suggests that older people may experience more anxiety when they interact with new technologies. However, surprisingly, the performance of the two older age groups was better in the high stress condition. These groups completed the tasks faster and used the interfaces more intuitively under the high stress condition.

This supports Attentional control theory [START_REF] Eysenck | Anxiety and cognitive performance: Attentional control theory[END_REF], which suggests that anxiety affects processing efficiency more than performance effectiveness. In other words, highly anxious individuals, under stressful conditions, trade time for accuracy in achieving their goal. They also use increased effort and more working memory resources. However, if the task does not overwhelm available resources, increased effort on the working memory resources actually enhances the performance. Since the experiment tasks were designed to be only moderately difficult, high stress condition did not induce high enough levels of anxiety to have a catastrophic effect on the older age groups' performance. This could be explained by the inverted U-hypothesis of anxiety-performance, which states that, for any given task optimal performance is achieved at some intermediate level (the peak of the inverted U) of arousal. Performance starts deteriorating as the level of arousal increases or decreases from its optimal level. This could be the most probable reason behind the performance increase in the older age groups under high stress condition.

The behaviour of the 35 to 49 years age group under stressful condition was different to that of the other age groups. Unlike other age groups, they reported low anxiety on high stress condition and high anxiety on low stress condition. However, while they performed better in a high anxiety state as older age groups did, their performance was opposite to that of the older age groups since they experience high anxiety under low stress condition. This behaviour did pose a few challenges in interpreting the results. For example, ANOVA showed that age had a significant effect on number of errors. However, contrasts showed that the difference was significant only between the 35 to 49 and 65 to 72 year age groups. The probable cause of this difference was that the 35 to 49 years age group made a lot fewer errors on low stress condition, whereas the other age groups did not differ that much. One of the possible reasons for the peculiar behaviour could be, as noted by Kosnik, Winslow [START_REF] Kosnik | Visual changes in daily life throughout adulthood[END_REF] study of the perception of vision related problems through adulthood, middle-aged people are more concerned about age-related changes that start becoming noticeable at this age. This behaviour of the middle age group needs further investigation.

The findings from this experiment also support Processing-speed Theory [START_REF] Salthouse | Major Issues in Cognitive Aging[END_REF], which suggests that older people tend to trade speed for accuracy. Although older people took more time to complete the tasks compared to younger people, overall they did not make significantly more errors than younger groups on both types of interfaces. However, as discussed earlier, middle-age groups differed significantly from the 65 to 72 year age group.

Overall, older people scored lower on technology prior experience, took more time to complete the tasks and used the interfaces less intuitively. However, the number of errors is one of the most crucial indicators for successful use of a product interface. This data suggests that when the interface is designed with consideration of the cognitive limitations of older people, the differences in its use among age groups can be minimal. Apart from the oldest age group (73+) the differences in terms of intuitive use of the interface were not significant. This supports research which suggests that working memory deficiencies in ageing are mediated by coping mechanisms adopted by older individuals, especially when the task is simple [START_REF] Brébion | Working memory and aging: Deficit or strategy differences?[END_REF]. Surprisingly, although most of the older participants had never used an iPad (mediator product for this experiment) they were at ease in using it. This supports recent research on touch-based products that has made similar observation [START_REF] Häikiö | Touch-based user interface for elderly users[END_REF][START_REF] Isomursu | Experiences from a Touch-Based Interaction and Digitally Enhanced Meal-Delivery Service for the Elderly[END_REF].

Conclusion

This experiment was designed to investigate if complexity of interface, in terms of its structure, has any impact on older users under high and low Stress conditions. The tasks used in this experiment were designed to consider older people's cognitive limitations. The outcome showed that, as expected, older people took more time on the nested interface compared to the flat interface. However, the type of interface structure had no significant effect on errors made. In addition, the age differences in terms of errors made were also minimal, except for the oldest age group (73+), who were significantly slower and used the interfaces less intuitively. Furthermore, contrary to what was hypothesised, older age groups, although they reported higher anxiety levels, did better under high stress conditions. This research is significant as the findings from this study demonstrated that, age differences are minimal when complex interfaces are designed to accommodate age related cognitive limitations (Section 2.3), Technology anxiety played positive role in use of interfaces in older people and, older people are at ease in using products with touch based interface. Based on these findings and related research, we are currently developing an adaptable interface framework for inclusive design that takes advantage of flexibility afforded by touch-based interfaces.

Figure 1 :

 1 Figure 1: Broad/flat interface versus nested/multi-layered 2-choices and 3-levels menu system

Figure 2 :

 2 Figure 2: Screen layout: a) performance feedback bar b) message window and c) interface

Figure 3

 3 Figure3illustrates the two repeated measures, flat and nested conditions. Each condition has 5 menu options. In the flat condition, all the five menu items were presented in a row at the bottom; in the nested condition, they were nested under two pop-up menus, one located on the right and the other one on the left bottom corner.

Figure 3 :

 3 Figure 3: Flat and nested interface

Figure 4 :

 4 Figure 4: Positive feedback and negative feedback display bar on the top of the task window

  17 to 34 years (M= 46 , SD = 4 , N = 12), 35 to 49 years (M= 43 , SD = 6.3 , N = 10), 50 to 64 years (M= 36.4 , SD = 8 , N = 12), 65 to 72 years (M = 36.6 , SD = 7.2 , N = 10), 73+ years (M = 34.4 , SD = 14.8 , N = 10).

  the flat: 65 to 72 years (flat M = 106 seconds, SE = 10, nested M = 149 seconds, SE = 15) and 73+ years (flat M = 154 seconds, SE = 10, nested M = 234 seconds, SE = 15).

Figure 5 :

 5 Figure 5: Time to complete the task on flat and nested interfaces under A) Low stress and B) High stress condition.

Figure 6 :

 6 Figure 6: Percentage of intuitive uses, interface type and age under A) Low stress B) High stress conditions.

Figure 7 :

 7 Figure 7: Percentage of errors, interface type and age under A) Low stress and B) High stress conditions

Table 1 :

 1 Experiment design and Independent Variables

	Independent variables	Levels of Independent Variables
	Interface type	Repeated measures (interface structure type)
		Flat interface	Nested interface
	Age	Young (17 to 34 years)
		Older young (35 to 49 years)
		Younger old (50 to 64 years)
		Old (65 to 72 years)
		Older old (73+ years)
	Anxiety	Low stress condition
		High stress condition