Luís Bruno
email: lbruno@ipbeja.pt

João Pereira

Joaquim Jorge

A New Approach to Walking in Place

Keywords: Walking in place, virtual locomotion, virtual speed control, performance, motor control

Walking in Place (WIP) is an important locomotion technique used in virtual environments. This paper proposes a new approach to WIP, called Speed-Amplitude-Supported Walking-in-Place (SAS-WIP), which allows people, when walking along linear paths, to control their virtual speed based on footstep amplitude and speed metrics. We argue that our approach allows users to better control the virtual distance covered by the footsteps, achieve higher average speeds and experience less fatigue than when using state-of-the-art methods based on footstep frequency, called GUD-WIP. An in-depth user evaluation with twenty participants compared our approach to GUD-WIP on common travel tasks over a range of short, medium and long distances. We measured task performance using four distinct criteria: effectiveness, precision, efficiency and speed. The results show that SAS-WIP is both more efficient and faster than GUD-WIP when walking long distances while being more effective and precise over short distances. When asked their opinion via a post-test questionnaire, participants preferred SAS-WIP to GUD-WIP and reported experiencing less fatigue, having more fun and having a greater level of control when using our approach.

Introduction

Virtual locomotion is the most common task performed by users in virtual environments (VEs). Although in many cases virtual locomotion is not the primary user objective [START_REF] Bowman | 3D User Interfaces -Theory and Practice[END_REF], the motion supports numerous other tasks, such as manipulating objects, collecting information [START_REF] Chittaro | Navigation aids for multi-floor virtual buildings: A comparative evaluation of two approaches[END_REF] and surveying virtual environments.

Navigation control can rely on either indirect interaction [START_REF] Mine | Virtual Environment Interaction Techniques[END_REF] using physical controls (buttons or joystick) or direct interaction based on gestures expressed by the motion of body parts. Physical locomotion [START_REF] Bowman | 3D User Interfaces -Theory and Practice[END_REF] is a metaphor that groups different locomotion techniques, such as real walking and Walking in Place (WIP). WIP is a popular technique because it enables people to control virtual locomotion, thus mimicking real walking movements [START_REF] Terziman | Shake-your-head: Revisiting walkingin-place for desktop virtual reality[END_REF]. Unlike unrestricted real walking, the WIP technique allows users to operate in small areas of interaction. This technique also frees the subject's hands, making them available to perform other interaction tasks. One usage scenario is the architectural review task where reviewer moves through buildings or outside spaces using a WIP technique and points and writes notes using hands.

Different WIP systems have been devised with different inputs, outputs, control laws of the virtual displacement and simulated movements [START_REF] Terziman | Shake-your-head: Revisiting walkingin-place for desktop virtual reality[END_REF]. Different technologies have been used to detect steps in place: magnetic trackers [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF][START_REF] Slater | Taking steps: The influence of a walking technique on presence in virtual reality[END_REF][START_REF] Templeman | Virtual locomotion: Walking in place through virtual environments[END_REF], force sensors placed on shoe insoles [START_REF] Templeman | Virtual locomotion: Walking in place through virtual environments[END_REF], optical camera trackers [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF], Wiimote Nintendo TM accelerometers [START_REF] Shiratori | Accelerometer-based User Interfaces for the Control of a Physically Simulated Character[END_REF] and Wii Balance Boards [START_REF] Williams | Evaluation of Walking in Place on a Wii Balance Board to Explore a Virtual Environment[END_REF]. Several different body segment motions are tracked to generate virtual output, including the head [START_REF] Slater | Taking steps: The influence of a walking technique on presence in virtual reality[END_REF], knees [START_REF] Templeman | Virtual locomotion: Walking in place through virtual environments[END_REF][START_REF] Yan | New simple virtual walking methodwalking on the spot[END_REF] and shins [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF][START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF]. These evolutions in user input have enabled the improvement of footstep latency times (starting and stopping travel) [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF][START_REF] Yan | New simple virtual walking methodwalking on the spot[END_REF] and have assured the continuity and smoothness of the movement between and within steps [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF][START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF]. In addition, different types of virtual locomotion control laws are based on neural networks [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF], knee pattern recognition [START_REF] Templeman | Virtual locomotion: Walking in place through virtual environments[END_REF], signal processing [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF] and biomechanical state machines [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF]. In reference to viewpoint movement, the Slater WIP system [START_REF] Slater | Taking steps: The influence of a walking technique on presence in virtual reality[END_REF] updates a predefined virtual distance only when a footstep is detected. This type of displacement motion is discrete and confusing for users. Later systems [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF][START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF] updated the viewpoint in each frame and attempted to simulate a sinusoidal forward velocity curve.

GUD-WIP [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF] is a state-of-the-art WIP method that estimates walking speed based on the subject's height and mainly on the subject's step frequency. These metrics emanate from the gaiting biomechanics literature [START_REF] Dean | An analysis of the energy expenditure in level and grade walking[END_REF], which indicates that are positively correlated with footstep horizontal length [START_REF] Inman | Human walking[END_REF].

However the physical movements of gaiting in place are slightly different. The feet movements are predominantly vertical and the time to perform footsteps can be different to the horizontal footsteps. To control speed using the step frequency users should manage the time variable. Due to different biomechanics features the minimum time to perform vertical footsteps can be lower than to the horizontal footsteps. So, in the WIP method user can achieve unexpected higher speed values which cause difficulties to control forward walking.

Other important metric derived from WIP biomechanics features is the footstep amplitude. This metric describes the footstep vertical length and is defined as the distance between the foot off (and foot strike) positions and the maximum foot height achieved. With this metric user manages the vertical distance covered by the foot to control virtual speed. We argue that user manages better this distance metric than the time metric to control virtual speed. So, our hypothesis states that is more affordable and perceptible to user control the virtual speed using the amplitude than the frequency metric in common walking tasks.

We developed an exploratory study to understand how subjects establish a relationship between step vertical length and three simulated virtual rates of speed (i.e., slow, moderate and fast). Based on the quantitative results, we propose a new approach to walking in place, called SAS-WIP (Speed-Amplitude-Supported Walkingin-Place), which models the forward linear virtual speed as a function of two kinematic metrics: footstep amplitude and speed. Our approach follows the main goals of the recent WIP systems and adds a simple and perceptible technique for transforming step amplitude as a primary factor to generate virtual speed.

Few studies have attempted to compare different WIP approaches and other interfaces [START_REF] Terziman | Shake-your-head: Revisiting walkingin-place for desktop virtual reality[END_REF]. Furthermore, many evaluations resort to subjective questionnaires, from which results are difficult to reproduce. To test our hypothesis we developed a comparative study of our SAS-WIP approach and GUD-WIP [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF]. We measured the task performance of twenty participants in travel and stopping precision tasks through three different distances (i.e., short, medium and long). We assessed the user performance according different criteria: effectiveness, precision, speed and efficiency. We argue that SAS-WIP outperforms GUD-WIP with the following features:

Provides to users a better forecast of the virtual distance covered by a single step. Allows for higher average speeds to be achieved, especially when covering long distances. Requires less effort from users, resulting in less fatigue, especially when covering long distances.

The results show that the SAS-WIP interface exhibited greater effectiveness and precision than the GUD-WIP interface in stopping precision tasks for short distances. Additionally, SAS-WIP was both more efficient and faster than GUD-WIP for long distances.

The remainder of this paper is organized as follows. Related work is presented on the next section. Next, we describe the SAS-WIP approach, followed by the experiment description, the results and the discussion of the main outcomes. In the conclusion section, we introduce the guidelines and summarize our work.

RELATED WORK

Virtual locomotion is a common task involved in controlling virtual environments. This motion can either be the primary task or a secondary goal that allows people to perform specific tasks, such as manipulating objects or collecting information [START_REF] Chittaro | Navigation aids for multi-floor virtual buildings: A comparative evaluation of two approaches[END_REF]. Different interaction techniques have been developed to support locomotion in VEs, and these techniques are typically used in complex navigation, including changes in direction and speed [START_REF] Terziman | Shake-your-head: Revisiting walkingin-place for desktop virtual reality[END_REF].

The WIP technique, which is considered a physical locomotion interface [START_REF] Bowman | 3D User Interfaces -Theory and Practice[END_REF], was designed to improve the sensation of walking while providing efficient navigation in VEs [START_REF] Slater | Taking steps: The influence of a walking technique on presence in virtual reality[END_REF]. This technique allows users to use small areas of interaction in the real world, unlike real walking, while freeing their hands for other interaction tasks. To this end, the users' bodies are tracked and analyzed to simulate virtual locomotion in the VE. The movements of different body parts are detected to steer virtual displacement via distinct approaches.

Slater developed the first WIP system for controlling virtual locomotion in VEs [START_REF] Slater | Taking steps: The influence of a walking technique on presence in virtual reality[END_REF]. This approach used a neural network to recognize patterns of head movement that correspond to walking-in-place actions. The starting and stopping step detection was not extremely precise, which confused users and caused them to overshoot their target stopping location. Moreover, the forward viewpoint movement is updated only one time for each step by one predefined length. User testing with this system revealed that WIP can increase presence compared to classical joystick-based interaction [START_REF] Usoh | Walking > walking-in-place > flying[END_REF], which is similar to the conclusion reached by Razzaque et al. [START_REF] Whitton | Comparing VE locomotion interfaces[END_REF]. In that study, it was suggested that WIP might also increase cybersickness [START_REF] Usoh | Walking > walking-in-place > flying[END_REF].

The Gaiter system [START_REF] Templeman | Virtual locomotion: Walking in place through virtual environments[END_REF] for military simulators is dependent on leg movement, and WIP is treated as a gesture that indicates that the user intends to take a virtual step. Gaiter uses the direction and the extent of knee movement to compute an implicit horizontal displacement of a body in the VE. However, to recognize a virtual step, the system must wait until the knee reaches the point of maximum extent, causing a halfstep latency [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF].

Another WIP system developed by Yan et al. [START_REF] Yan | New simple virtual walking methodwalking on the spot[END_REF] used the speed at which the knee lifts in stepping in place actions to determine the virtual locomotion speed. The authors related different variables of the real locomotion (forward velocity/leg lifting speed, leg lifting speed/step frequency) and the stepping in place (leg frequency/leg lifting speed). The authors report that this system is more responsive than Gaiter system without explicit proof.

The output speed of the LLCM-WIP [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF] system is dependent on a continuous signal of the foot heel position. This approach accomplishes the following goals: low starting and stopping latency, smooth locomotion between steps, continuous control of locomotion speed within each step and the incorporation of real-world turning and short-distance maneuvering into virtual locomotion [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF]. The output speed curve varies considerably when the position differentiation is 0 when maximum foot height is achieved and between steps.

The state of the art GUD-WIP model creates output speeds that better match those occurring during real walking and that better respond to variations in step frequency, including realistic starting and stopping control of virtual speed using the observed footstep frequency [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF]. The virtual speed of this implementation shows considerably less within-step fluctuation than LLCM-WIP. The virtual speed is dependent on a walking equation from the biomechanics literature, which relates to step frequency and subject height and relies on in-place step events. The user-study analysis presented that real walking and GUD-WIP each provide a consistent step frequency to resulting speed.

Although Wendt et al. hypothesized that consistency improves the user experience, which leads to improved usability [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF], we are not certain that users correctly interpret step frequency to forecast the virtual distance covered by a single step in precision targeting tasks (short distances). For certain types of applications, such as traveling long distances in VEs at high speed, step frequency usage could be inefficient and tiring. The stopping latency of the GUD WIP may also be unacceptable for certain applications, as mentioned by the authors.

The system of Wendt et al. was not evaluated by participants during VE travel tasks for comparison with other WIP systems or other navigation techniques to understand the task performance addressed and the user experience.

In this paper, we present a participant study to evaluate our SAS-WIP approach in comparison with GUD-WIP. The participants executed travel and target stopping tasks, and the performance is measured based on quantitative metrics. The user expe-rience is articulated with a subjective questionnaire. In this manner, it is be possible to understand the usability provided by each system related to those criteria.

OUR APPROACH

To test our hypothesis we developed an exploratory study to understand how subjects establish a relationship between step vertical length and three simulated virtual rates of speed (i.e., slow, moderate and fast). The quantitative results showed a correlation between the two variables; higher footsteps correspond to higher simulated speed. Another feature emerged during this analysis: foot speed. Subjects also related the simulated virtual speed to different levels of foot swing speed. This relationship also proved to be consistent.

Based on these results we develop the SAS-WIP model which primarily takes advantage of the foot height achieved during in-place steps and is complementary to the foot vertical speed. Our system addresses requirements similar to LLCM-WIP [START_REF] Feasel | LLCM-WIP: Low-latency, continuous-motion walkingin-place[END_REF] and GUD-WIP [START_REF] Wendt | GUD-WIP: Gait-understanding-driven walking-inplace[END_REF] as smooth between-step locomotion speed, continuous within-step speed control, real-world turning and maneuvering, low starting latency, low stopping latency, in-place step events detection and biomechanics-inspired state machine.

The next sections describe the development process, the features and the model algorithm of the SAS-WIP technique.

Exploratory Study

In the early stages of our research, we conducted informal observations of people walking in place. We asked subjects to perform sequences of in-place steps with the intention to represent different virtual speed levels (i.e., slow, moderate and fast). We only requested that participants relate foot height during steps to each virtual rate of speed.

Most subjects exhibited common behavior patterns. When asked to simulate slow locomotion, subjects raised their feet to the lowest maximum height at slow foot speed. For moderate locomotion, the footsteps were raised higher and faster than for slow walking. Maximum height and foot speed were observed when we instructed participants to walk briskly. These observations allowed us to identify a subjectconsistent pattern in the use of foot height to control virtual speed, which suggests that people perceive that this metric controls different levels of speed. Another metric in subject behavior was also revealed: vertical foot speed. To achieve faster virtual speed, the subjects raised the feet faster. Therefore, we considered it important to study this feature in greater detail.

We conducted a formal exploratory study to quantitatively confirm the results of those observations. We aimed to confirm that subjects consistently relate footstep amplitude and speed with the intended virtual speed. Five participants, four male and one female, aged 22 to 45, took part in the user study and were recruited from our local university campus. None of the participants had motor or physical impairments. The experiment was conducted in a laboratory equipped with an optical tracker sys-tem [START_REF] Optitrack | [END_REF] based on infra-red cameras, providing 6DOF tracking data at 100 Hz. To record the foot height and speed, one reflective marker was mounted on each heel. The heel position data for each foot is received as a continuous signal and allows for the calculation of the foot speed by vertical position differentiation. All participants were asked to walk in place inside a 3x3 meter square. At the beginning of the experiment, the participants were informed of the purpose of the experiment and were asked to perform three series of ten in-place steps for each level of simulated virtual locomotion speed: slow, moderate and fast. The position and timestamp data gathered allowed for the analysis of descriptive footstep amplitude and speed statistics and for the determination of the consistency between those metrics and the virtual speed levels perceived by the participants. As all the samples presented a normal distribution then we used an ANOVA to assess differences between the three speed levels on the two metrics within a significance of 0.05.

A one-way repeated-measures ANOVA revealed significant differences among the three speed levels (F2 =65.071, p<0.001) as a function of footstep amplitude sample. A Bonferroni post-hoc test revealed significant differences between all speed levels: slow and moderate (p=0.009), slow and fast (p=0.001), and moderate and fast (p=0.023).

These results suggest that participants expected that virtual speed changes consistently with the footstep amplitude. Fig. 1 shows that amplitude values are consistent with each simulated rate of speed.

We use mean speed to characterize foot motion during one step. This calculus is based on the vertical distance covered by the foot in the descendent and ascendant phases and the respective time periods as defined in Equation ( 1). The vertical distance value is the double of the amplitude value, ta is the ascendant time and td is the descendant time. The amplitude is the distance between the foot-off (and foot strike) detected position and the maximum foot height position.

Vel= 2*amplitude/(ta+td)

(

A one-way repeated-measures ANOVA revealed significant differences among the three speed levels (F 2 =154.981, p<0.001) as a function of footstep mean speed. A Bonferroni post-hoc test revealed differences between all cadences: slow and moderate (p=0.003), slow and fast (p<0.001), and moderate and fast (p=0.008).

These results show that participants naturally and consistently adapted their footstep speed to match each requested simulated speed. Fig. 1 confirms these results by demonstrating the variation of the vertical mean speed values of the five participants for each of the three simulated speed levels. Based on these results, we propose to compute the linear virtual speed in our SAS-WIP metaphor using the observed kinematics variables and their value ranges. The next section describes the primary decisions concerning SAS-WIP.

SAS-WIP Model Parameters

Our approach uses a biomechanics state machine with three states: ascending, descending and foot support. The main events detected are foot-off, foot max height and foot strike. Fig. 2 shows the foot states and the conditions that trigger each event. The start event (foot off) occurs when the foot vertical position (Pvert) exceeds 0.035 m (P1) and the vertical velocity (Vvert) exceeds 1.0 m/s (V1). The transition from ascending to descending state occurs when the maximum height is achieved (i.e., Vvert is less than 0.05 ms (V2)). The foot strike occurs when Pvert is lower than 0.035 m.

Fig. 2. The state transition diagram for an in-place step for a single foot

In the WIP starting phase, the virtual speed of the first step is updated at three moments, when the following events occur: foot off, max height and foot strike. Therefore, when the foot off of the first step is detected, the system assigns a value of 0.24 m/s to the initial virtual speed, which is the minimum value corresponding to the slow simulated speed achieved in the exploratory speed (see Table 1).

In rhythmic phase, the speed is updated at two moments for each step cycle during the following events: max height and foot strike. The first event occurs at the end of the ascending phase, and the second event occurs at the end of the descending phase. Only for these two moments, the speed is updated based on Equation (2), allowing for the maintenance of steady speed within and between steps.

Equation ( 2) to compute the virtual speed is supported by the kinematics results of the exploratory study. We separate the calculus into two conditions: (i) subjects that intend to achieve a slow speed or (ii) subjects that want faster speeds (moderate or fast).

To achieve slow virtual speed, the footstep amplitude should be lower than 0.13 m. This value corresponds to the sum of the foot mean amplitude (0.10 m) and the standard deviation (0.03 m) of the slow simulated speed (see Table 1). We observed that users control slow speeds better using only their footstep mean speed.

To achieve faster virtual speed, the main factor is footstep amplitude, which should be higher than the slow speed threshold (0.13 m). In this case, the amplitude is divided by that threshold to obtain a linear scale factor of the virtual speed. This decision was made according to our concept that users expect that virtual speed changes consistently with the step amplitude. To calculate the virtual speed, the amplitude scale is multiplied by the foot speed. This metric assures consistency in the updating of the virtual speed and allows users to allocate or not allocate more walking effort or energy.

(

) 2
Accurate stopping is important to users to better predict how to reach the desired locations. Two types of problems exist: false positives (the system stops without user intention) or stops long after the user orders a stop. In the stopping movement phase, we attempted to avoid these problems in the SAS-WIP system. The motion stops when one timeout occurs at the double support (when the feet are supported on the ground) state as described by Equation ( 3). This value is computed as a function of the mean foot speed of the last descending phase before the double support. This formula was computed from data gathered from the exploratory study. We investigated how long the double support was sustained after different footstep speed levels (from slowest to fastest) occurred. Time= (-0.2*foot_speed+0.41)*1000

(3)

We identified a trend in the foot speed variation from the slowest to the fastest motions before the user stop actions. When the foot speed is higher, the timeout is shorter. We opted to stop the virtual movement immediately after the timeout is achieved. GUD-WIP slows and smoothly stops the virtual speed. Our method decreases the stopping latency relatively to GUD-WIP, preventing unintentional motion stoppage.

Speed Features

Fig. 3 (right image) shows the virtual speed curve generated by the SAS-WIP algorithm from two independent series of in-place footsteps (blue and red lines). The first three steps are slows, the following three steps are moderate and the three final steps are fast. The "Virtual Speed" axis discriminates the output speed generated by SAS-WIP, and the "Position" axis represents the foot height achieved by each foot (left and right). In Fig. 3 (left image), it is possible to identify several features, such as the assignment of the initial speed (0.24 m/s) at foot off of the first step as well as the speed updates at the max height and foot strike events. Regarding the rhythmic phase of the slow steps, the curve shows that the output speed is steady because the foot speeds are similar. Regarding the moderate steps, the effect of amplitude is evident. The virtual speed curve grows consistently with footstep amplitude. The speed control is continuous and responsive to amplitude changes between-and within-step.

The stopping latency is exhibited in the last step of each sequence. There is an evident delay between the end of the last step and the virtual stopping. However, this timeout is detected immediately and is assigned a null speed in the virtual movement.

EXPERIMENT

To assess the performance afforded by SAS-WIP, we developed a participant study to compare our approach with GUD-WIP.

4.1

Tasks

The participants were asked to execute two different tasks: (i) travel through nine paths of different distances and (ii) stop in front of nine prescribed target locations. The sequence and the distances of the nine paths traveled are as follows: P1 (6.0 m), P2 (1.0 m), P3 (12.0 m), P4 (0.5 m), P5 (24.0 m), P6 (1.5 m), P7 (3.0 m), P8 (48.0 m) and P9 (2.0 m). These different paths covered different distances: short (0.5 m, 1.0 m, 1.5 m, 2.0 m and 3.0 m), medium (6.0 m and 12.0 m) and long (24.0 m and 48.0 m). The short distances are suited to test the precision and the effectiveness provided by the interfaces in the stopping task, and the medium and long distances can check the efficiency and the speed offered to participants by the locomotion interfaces.

In the stopping task, the participants attempt to stop as close as possible in front of a target without overshooting it. This task occurred at the end of the travel task. Each of the nine paths is bound by rectangular targets (length: 1 m, width: 0.1 m) positioned on the sidewalk floor (length: 100 m), as exhibited in Fig. 4. The participants control the viewpoint in this experiment by using the first person model and perceive the proximity to the target by measuring the distance between that object position and the bottom line of the viewport. Therefore, the participants should stop the viewpoint motion when the rectangle is as near as possible to the bottom line of the screen.

The task performance offered to participants by the interfaces is determined by the following criteria: effectiveness, precision, mean speed and efficiency.

Participants

Twenty participants, 15 male and five female, ranging in age from 20 to 33 years (mean=24.5 years) took part in the user study and were recruited from our local university campus. The majority of the participants had played videogames that use virtual navigation, and no one had motor or physical impairments. No participant had previously tested physical locomotion interfaces like the WIP systems.

Procedure

At the beginning of the experiment, the participants were told the purpose of the study, and the script was explained. A pre-test questionnaire was administered to gather demographic and navigation skills data for each participant.

Before testing each interface, participants practiced for at least 10 minutes in an environment similar to the formal tests, performing travel and stopping tasks. When ready, the participants began each interface trial, traveling all the nine paths and making stops before each target. When a system error occurred or a participant sensed a particular difficulty, the trial was repeated. Regarding the travel task, the participants were asked to complete each assigned path as fast as they could. At the end of each interface, the trial subjects completed a post-test questionnaire to respond to questions about their experience. Each participant experimented with both interfaces in an order determined using a Latin Square approach to avoid bias associated with the sequence of the trials. After all the participant trials were complete, a post-experiment questionnaire was administered in order to allow the participants to compare and rank the interfaces based on their subjective preferences.

Apparatus

This experiment was developed in the same laboratory where the exploratory study occurred. The output of the virtual environment was displayed on a large-scale screen (4x2.25 square meters) as shown in Fig. 4 (bottom picture). The participants are positioned approximately 2.0 m from the screen, within a 3x3 meter square area. The foot motion capture was supported by an optical tracker system [START_REF] Optitrack | [END_REF], which determines the heel position for each foot.

Ensure equity with GUD-WIP

To ensure a fair comparison between our approach and GUD-WIP, we implemented several adjustments to this system. Because the GUD-WIP stopping latency is slow, we adapted the SAS-WIP stopping mechanism, which described by Equation (3). Therefore, the effectiveness and precision of the two interfaces were only affected by the interface features provided to the participants.

To compare the speed provided by each interface, it is necessary to assure that their value ranges are similar. Theoretically, the two WIP interfaces allow similar speed levels for the slowest and fastest cases, as exhibited in Table 2. 1 for the three speed levels. The GUD-WIP frequency values are based on reference values from the literature [START_REF] Dean | An analysis of the energy expenditure in level and grade walking[END_REF] that are representative of the range speed. The comparative virtual speed values suggest that the two interfaces can provide similar speed levels. Therefore, the speed achieved by the participants should depend on the affordance provided by each interface.

Design and Data Analysis

We used a within-subjects design where each participant tested all the three interfaces. The nine paths were grouped in three different distance classes: short, medium and long, as explained previously. Therefore, the analyzed data consisted of 20 participants x 3 distance classes x 3 interfaces, which equals 180 paths/targets.

The quantitative results are dependent on the following task metrics: target overshoots frequency, final distance to target, mean speed and number of steps. The data samples of the overshoot frequency and the final distance to target did not present a normal distribution, so we used the non-parametric Wilcoxon test to determine the pairwise differences among the interfaces.

Regarding the "mean speed" and "number of steps" metrics, a two-way repeatedmeasures ANOVA (interfaces x paths) was applied. In this case, the Greenhouse-Geisser's sphericity corrections were applied whenever Mauchly's test of sphericity showed a significant effect. In both metrics, the paired t-test was used to determine the pairwise differences among the two interfaces.

The subjective questionnaire results were analyzed using the Friedman nonparametric test on the different six criteria (i.e., Fun, Ease of use, Fatigue, Precision, Naturalness and Global Appreciation) and the Wilcoxon post-hoc test.

The level of significance used for all of the hypothesis tests was α=0.05.

RESULTS AND DISCUSSION

The quantitative data gathered from the trials were statically analyzed to understand the comparative performance provided by the two interfaces. In this section, we explain the effects of the interfaces on the metrics, which are the overshoot frequency, final distance to target, mean speed and the number of steps. In this section, results obtained from the subjective questionnaire post-test are also described.

Effectiveness

The target overshoot data were recorded during the stopping tasks. One overshoot represented an error by the participant. The performance effectiveness criterion is related to the overshoot frequency. Fewer overshoots indicates greater effectiveness.

Table 3 shows the mean overshoot frequency for the two interfaces grouped by distance classes. The SAS-WIP interface allows, on average, a lower overshoot frequency than GUD-WIP for the three distance classes.

Relative to short distances, the test showed significant differences between SAS-WIP and GUD-WIP (p=0.004). For medium and long distances, significant differences between SAS-WIP and GUD-WIP (p=0.080, p=0.856, respectively) were not found. We contend that short distances are suited to assess the effectiveness provided by the interfaces because users have less time and space to forecast the virtual distance covered by each footstep, in contrast with longer distances. We argue that smaller distances allow for better understanding of which interface provides the best effectiveness control. These arguments and the results suggest that SAS-WIP provides more effectiveness to users than GUD-WIP. In contrast, for medium and long distances, SAS-WIP and GUD-WIP assured similar effectiveness in those tasks. In these cases, the distances do not affect the interface results because participants had greater time and space to predict and adjust the distances covered to stop at the desired point.

Precision

During the stopping tasks, the participants were instructed to stop as near as possible to the target without overshooting the target. From the trials, the distance between the stop position and the target position was recorded for all targets that were not exceeded. The exceeded cases are analyzed on frequency overshoot metric. This metric was named "final distance to the target" and determines the precision level that an interface provides to participants. A smaller final distance to the target indicates greater precision.

Table 3 shows the mean final distance to the target provided by the interfaces grouped by the three distance classes. On average, the SAS-WIP interface provided the lowest final distance to the target. Regarding short and medium distances, the test showed significant differences between GUD-WIP and SAS-WIP (p=0.002, p=0.015) in contrast to the long distances, where differences between the two interfaces were not revealed (p=0.466).

Similarly to effectiveness, we consider that smaller distances allow for better understanding of which interface provides the finest action control and consequently the best precision. From these arguments and from the short distance results, we can conclude that SAS-WIP provides more precision to users than GUD-WIP in stopping tasks. This last conclusion was complemented by the medium distance results. The long distance precision results were consistent with the long distance effectiveness results.

Mean Speed

The time and the distance traveled across the different paths were recorded to calculate the mean speed for each path. Greater mean speed indicates faster participant travel. This metric provides how fast the user can walk using each interface.

Table 3 shows the averages of the mean speed provided by the interfaces for the three distance classes. For medium and long distances, on average, SAS-WIP is faster than GUD-WIP, although for short distances, the two WIP interfaces are similar.

The two-way repeated-measures ANOVA revealed the significant effect of the interface (F 1.0 , 16.0 =8.067, p=0.012) and of the distance (F 2.0 , 32.0 =216.722, p<0.001) on the mean speed. Interactions between interfaces and distance classes were found (F 2.0,32 =7.600, p=0.002).

For short distances, the paired t-test did not reveal differences between the two interfaces regarding the mean speed (t 17 =-0.148, p=0.884). Concerning medium and long distances, the paired t-test revealed significant differences between the interfaces on the mean speed (t 17 =3.104, p=0.006; t 16 =2.805, p=0.013, respectively).

For medium and long distances, the SAS-WIP approach induces participants to travel faster than GUD-WIP, but for short distances, the two WIP interfaces are similar. The speed in performing paths is more important and critical to longer distances. In this manner, the results show that SAS-WIP provides to participants a higher average speed than GUD-WIP. This conclusion suggests that participants more consistently map the virtual speed values to the footstep amplitude than to the footstep frequency.

The tests allow for the identification of several errors committed by participants when they want to achieve faster speeds with GUD-WIP. When the step frequency is high (e.g., 20 Hz) because the footstep period is very short (e.g., 50 ms), the speed achieved is "supersonic" (e.g., 169.91 m/s). This problem occurs when participants increase the step cadence, shortening times between footsteps to travel faster (sometimes the viewpoint moves out of the VE scenario).

Efficiency

No references were found in the gaiting literature about what is the footstep factor which causes more energy expenditure: frequency or amplitude. So, we chose the number of steps as the metric that indicates the level of effort allocated to this task because is the common element for the two interfaces. So in our concept, this metric provides a measure of efficiency provided by the WIP interfaces. Fewer steps to travel the paths indicate greater efficiency provided by the interface. During the travel task across the different distances, the number of footsteps performed was recorded.

Table 3 shows the mean number of footsteps provided by the interfaces grouped by distance classes. For the medium and long distances, on average, the GUD-WIP inter-face induced a greater number of steps than did SAS-WIP. For short distances, GUD-WIP provided a lower number of steps than did SAS-WIP.

The two-way repeated-measures ANOVA revealed the significant effect of the interface (F 1.0 , 14.0 =38.522, p<0.001) and the distance (F 2.0 , 28.0 =160.112, p<0.001) on the number of footsteps performed. Interactions between interfaces and distance classes were identified (F 1.103 , 15.446 =32.855, p<0.001).

For the two WIP interfaces, regardless of the distances, the effect of the interface was significant (F 1.0 =38.522, p<0.001). The post-hoc test revealed significant differences between SAS-WIP and GUD-WIP (p<0.001). In contrast, accounting for the three distance classes, the distance had a significant effect (F 1.502 =160.112, p<0.001) on the number of steps, as we expected. The post-hoc test revealed significant differences between SAS-WIP and GUD-WIP (p<0.001). For short distances, the paired ttest revealed significant differences between the two interfaces (t 14 =3.450, p=0.004) regarding the number of steps. Concerning medium distances, the paired t-test revealed a significant difference between the two interfaces (t 14 =-2.419, p=0.030) regarding the number of steps. For long distances, the paired t-test revealed a significant difference between the interfaces (t 14 =-6.095, p<0.001).

For short distances, GUD-WIP is more efficient than the SAS-WIP approach. When the user intends to move slower with GUD-WIP, the footsteps should be slower so the user performs fewer footsteps during the displacement. In contrast, with SAS-WIP, the user should raise the foot to a short height to move slower, so the footsteps are faster, allowing a greater number of steps.

However, for medium and long distances, where the efficiency is a more important performance criterion, SAS-WIP requires fewer footsteps than GUD-WIP. The results suggest that SAS-WIP requires less effort from participants and causes less fatigue than GUD-WIP, especially when covering long distances, as we expected.

User Subjective Perception

After the participants finished the experiment, they completed a questionnaire in which they had to rank the interfaces (SAS-WIP and GUD-WIP) by their preference in the context of six subjective criteria: (a) Fun, (b) Easiness of Use, (c) Fatigue, (d) Precision of Control, (e) Naturalness and (f) Global Appreciation.

To rank the interfaces for each criterion, the participants provided scores from 1 (worst preference) to 3 (best preference). Fig. 5 shows the preference results for each subjective criterion. In the fatigue case, grade 3 indicates that the interface provides the least fatigue. We found significant differences between the SAS-WIP and GUD-WIP for the following criterion: Fun (p=0.012), Ease of Use (p<0.001), Fatigue (p=0.002), Precision (p<0.001) and Global Appreciation (p=0.02). Only for Naturalness, we didn't found significant difference between the two WIP interfaces.

The questionnaire results showed that SAS-WIP was ranked as the most appreciated interface (global appreciation) and most fun. Many participants commented during the experiment that they were greatly satisfied with the controllability provided by SAS-WIP and that they found it fun to move in the VE induced by its physical foot movements. SAS-WIP is perceived as less tiring than GUD-WIP.

The last result is consistent with the efficiency and the mean speed results achieved in this paper, which suggest that SAS-WIP induces less effort in participants and less fatigue than GUD-WIP. Several participants explicitly commented that SAS-WIP is less tiring than GUD-WIP, particularly for long distances.

CONCLUSIONS AND FUTURE WORK

This paper introduced SAS-WIP (Speed-Amplitude-Supported Walking-in-Place), which is a new approach to WIP that allows for the control of linear virtual locomotion based on footstep amplitude and speed. We identified the features and kinematic variables that best describe the specific foot motions of the SAS-WIP approach via an exploratory study with five participants.

This paper provides a comparative study between two WIP alternatives concepts dependent on different metrics. The study explores common locomotion tasks, such as travel between targets and stopping before objects, and allows for the measurement of the performance provided and the user experience offered. We argued that users can better forecast the virtual distance covered by a single step dependent on the footstep amplitude and speed than on the footstep frequency. The effectiveness and precision performance results obtained in our experiment for the stooping task suggest the confirmation of this statement (see Table 4). Even for medium and long distances, the speed control of one step is more perceptible for subjects (see Table 4).

Although theoretically, the speed equation dependent on the footstep frequency allows for greater speed, the results suggest that the approach dependent primarily on the footstep amplitude achieved a greater mean speed, particularly for medium and long distances.

Supported by the performance results and participant opinions, the effort allocated by users to cover medium and long distances using SAS-WIP seems to be smaller than that provided by those using GUD-WIP. This result suggests that SAS-WIP is more efficient, assuring less fatigue and possibly less energy expenditure.

The SAS-WIP system seems to be a promising WIP method, assuring controllable and comfortable interaction and good user perception, and should be the object of improvements and evaluations with other tasks and environments. However, our WIP approach has one open question related to the effect of the subject height in the virtual speed computed. One participant expressed this concern because of her small stature. We plan to develop a detailed study to understand this effect and how this personalized metric can be implemented in the motion control law.

A challenging task to measure the controllability provided by SAS-WIP is the pursuit of moving objects. We plan to develop a study for measuring the level of proximity to objects provided by our approach in different locomotion contexts.

In the future, we plan to evaluate the effect of the proprioception afforded by our approach on the perception of spatial dimensions in estimating distances in virtual environments. Additionally, we intend to add locomotion direction control (steering) dependent entirely on foot motions to SAS-WIP.

Fig. 1 .

 1 Fig. 1. The footstep amplitude and vertical clustered for the five participants

Fig. 3 .

 3 Fig. 3. Virtual speed is updated at the starting and rhythmic phases (left image); virtual speed curve generated by SAS-WIP (right image)

Fig. 4 .

 4 Fig. 4.The 3D virtual environment and the experiment apparatus (from left to right)

Fig. 5 .

 5 Fig. 5. The results of the subjective questionnaire for each criterion (a) -Fun, (b) -Ease of Use, (c) -Fatigue, (d) -Precision, (e) -Naturalness and (f) -Global Appreciation

Table 4 .

 4 Comparing the performance criteria provided by the interfaces ("~": Not Different, ">": Better than, "<": Worse than)

Table 1 .

 1 Descriptive stats of kinematic variables

	Virtual		Amplitude (m)		Mean Foot Speed (m/s)
	Speed	Min.	Mean St. Dev.	Max	Min. Mean St. Dev. Max
	Slow	0.05	0.10	0.03	0.14	0.24	0.40	0.08	0.59
	Moderate	0.08	0.20	0.03	0.30	0.38	0.70	0.09	0.84
	Fast	0.22	0.31	0.06	0.46	0.68	0.99	0.09	1.13

Table 1

 1

shows the descriptive statistics of the footstep amplitude and mean speed metrics in greater detail. One can see that the amplitude values related to slow, moderate and fast speeds are grouped at approximately 0.10 m, 0.20 m and 0.31 m, respectively. Regarding the mean foot speed, their values are grouped at approximately 0.40 m/s, 0.70 m/s and 0.99 m/s for the same speed levels, respectively.

Table 2 .

 2 Comparison of 3 representative speed levels generated by SAS-WIP and GUD-WIP

		GUD-WIP		SAS-WIP
	Freq. (hz) Virtual Speed(m/s) Ampl.(m) Foot Speed (m/s) Virtual Speed (m/s)
	2.50	2.54	0.31	0.99	2.36
	1.75	1.24	0.20	0.70	1.07
	1.00	0.41	0.10	0.40	0.40

Table 2

 2 compares three representative speed levels for each interface, corresponding to slow, medium and fast values. In this comparison, the GUD-WIP virtual speed is computed with a constant height of 1.72m. The variable values of SAS-WIP are obtained from the mean values of Table

Table 3 .

 3 Mean and interval confidence values of the metrics: A-Frequency Overshoots, B-Distance to Target (m), C-Mean Speed (m/s) and D-Number of Steps

				SAS-WIP					GUD-WIP
		Short		Med.	Long	Short	Med.	Long
		µ CI	µ	CI	µ	CI	µ	CI	µ CI	µ	CI
	A	0.58	0.25 0.48	0.21	0.48	0.25	0.80	0.35	0.65	0.28	0.60	0.26
	B	0.05	0.02 0.08	0.03	0.12	0.12	0.14	0.14	0.14	0.14	0.14	0.14
	C	0.50	0.06 1.21	0.23	2.64	0.39	0.50	0.07	0.88	0.11	1.93	0.23
	D	4.63	0.62 11.6	1.87	19.3	1.87	3.73	0.49	14.2	1.53	37.0	4.75

Acknowledgements

. This work was supported by FCT (INESC-ID multiannual funding) under the project PEst-OE/EEI/LA0021/2011 and by the project CEDAR PTDC/EIA-EIA/116070/2009.


Marios Belk
email: belk@cs.ucy.ac.cy

Christos Fidas
email: christos.fidas@cs.ucy.ac.cy

Panagiotis Germanakos

George Samaras
email: cssamara@cs.ucy.ac.cy

Security for Diversity: Studying the Effects of Verbal and Imagery Processes on User Authentication Mechanisms

Keywords: User Authentication, Cognitive Factors, Efficiency, Effectiveness, Preference, Usable Security, Diversity, User Study

Stimulated by a large number of different theories on human cognition, suggesting that individuals have different habitual approaches in retrieving, recalling, processing and storing verbal and graphical information, this paper investigates the effect of such processes with regard to user performance and preference toward two variations of knowledge-based authentication mechanisms. In particular, a text-based password authentication mechanism and a recognition-based graphical authentication mechanism were deployed in the frame of an ecological valid user study, to investigate the effect of specific cognitive factors of users toward efficiency, effectiveness and preference of authentication tasks. A total of 145 users participated during a five-month period between February and June 2012. This recent study provides interesting insights for the design and deployment of adaptive authentication mechanisms based on cognitive factors of users. The results and implications of this paper are valuable in understanding and modeling user interactions with regard to authentication mechanisms.

Introduction

The World Wide Web has become gradually a platform for deployment of complex applications of increased interactivity, as it takes the form of a medium used for complex and important tasks including commercial and governmental transactions, collaborative work, learning and information retrieval. Within this realm, security and privacy issues of interactive systems are considered of paramount importance as it is known that the consequences of a security breach can harm the credibility and legal liability of an organization, decreases users' trust and acceptance, while it exponentially increases maintenance and support costs. In this context, one of the most important and challenging issues is to support users, engaged on tasks related to authentication, through usable computer-human interface designs [START_REF] Shay | Correct Horse Battery Staple: Exploring the Usability of System-assigned Passphrases[END_REF][START_REF] Komanduri | Of Passwords and People: Measuring the Effect of Password-composition Policies[END_REF][START_REF] Inglesant | The True Cost of Unusable Password Policies: Password use in the Wild[END_REF][START_REF] Florencio | Large-scale Study of Web Password Habits[END_REF][START_REF] Adams | Users are not the Enemy: Why Users Compromise Security Mechanisms and How to Take Remedial Measures[END_REF][START_REF] Shay | Encountering Stronger Password Requirements: User Attitudes and Behaviors[END_REF][START_REF] Bonneau | The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes[END_REF][START_REF] Leblanc | Guessing Click-based Graphical Passwords by Eye Tracking[END_REF].

Nowadays, user authentication over the Internet is achieved primarily with the use of text-based passwords. It is estimated that more than 80% of US and UK companies apply some form of text-based password authentication; in many cases it is their solely method for user authentication [START_REF] Zhang | Improving Multiple-password Recall: An Empirical Study[END_REF]. Despite this familiarity factor of users with regard to text-based authentication mechanisms, a considerable amount of research has focused on the design and implementation of graphical authentication schemes. Graphical authentication mechanisms claim to preserve security and improve usability and memorability, as they leverage the vast capacity and capabilities of the human visual memory system [START_REF] Angeli | Is a Picture Really Worth a Thousand Words? Exploring the Feasibility of Graphical Authentication Systems[END_REF][START_REF] Everitt | Comprehensive Study of Frequency, Interference, and Training of Multiple Graphical Passwords[END_REF][START_REF] Tullis | Can Users Remember their Pictorial Passwords Six Years Later[END_REF]. Principally, graphical authentication mechanisms require from a user to enter an authentication key represented by images in a specific sequence. A recent comprehensive review of graphical authentication schemes [START_REF] Biddle | Graphical Passwords: Learning from the First Twelve Years[END_REF] revealed that numerous ideas have been proposed over the last decade which focus on graphical authentication, and how to address the security and usability issues of textbased passwords, however, few schemes exist that deliver on the original promise of addressing the known problems with text-based passwords since many of the same problems continue to exist. For example, in an early study of Brostoff and Sasse [START_REF] Brostoff | Are Passfaces More Usable than Passwords: A Field Trial Investigation[END_REF], which compared the usability of a text-based password and a recognition-based, graphical authentication mechanism (Passfaces [START_REF]The science behind Passfaces[END_REF]), results demonstrated that although fewer login errors were recorded in graphical authentication than in text-based password interactions, they tended to log in less frequently because the login process was not as efficient as through the text-based password mechanism. A more recent study of Everitt et al. [START_REF] Everitt | A Comprehensive Study of Frequency, Interference, and Training of Multiple Graphical Passwords[END_REF] revealed a dramatic decline in memorability and performance when using multiple graphical authentication keys. Also, from a security perspective, the greatest concern of graphical authentication mechanisms is guessing attacks since usable graphical authentication keys usually have a weaker level of security strength than traditional text-based passwords. A study of Stobert et al. [START_REF] Stobert | Exploring Usability Effects of Increasing Security in Click-based Graphical Passwords[END_REF] indicated that increasing the required authentication key length in graphical authentication mechanisms negatively affects the memorability of the authentication key, and thus, the overall usability of the authentication process.

Graphical authentication schemes can be classified into the following three categories: Recall-based authentication mechanisms require that users remember information and reproduce a secret drawing on a static image as their authentication key. Examples include Draw-a-Secret (DAS) [START_REF] Jermyn | The Design and Analysis of Graphical Passwords[END_REF], its variations [START_REF] Biddle | Graphical Passwords: Learning from the First Twelve Years[END_REF], YAGP (Yet Another Graphical Password) [START_REF] Gao | YAGP: Yet Another Graphical Password Strategy[END_REF], and Pass-Go [START_REF] Tao | Pass-Go: A Proposal to Improve the Usability of Graphical Passwords[END_REF]. Cued-recall authentication mechanisms require users to identify specific locations on a static image and are intended to reduce the memory load on users, since specific cues are utilized in order to assist the recall of information. Examples include PassPoints [START_REF] Wiedenbeck | Authentication Using Graphical Passwords: Effects of Tolerance and Image Choice[END_REF], Persuasive Cued Click Points [START_REF] Chiasson | Influencing Users Towards Better Passwords: Persuasive Cued Click-Points[END_REF] and gaze-based authentication [START_REF] Bulling | Increasing the Security of Gaze-based Cued-recall Graphical Passwords using Saliency Masks[END_REF]. Recognition-based authentication mechanisms require that users create an authentication key by selecting and memorizing specific images, and then recognize the images among decoys to authenticate. Examples include Passfaces [START_REF]The science behind Passfaces[END_REF], ImagePass [START_REF] Mihajlov | On Designing Usable and Secure Recognition-based Graphical Authentication Mechanisms[END_REF] and Tiles [START_REF] Nicholson | Security Assessment of Tiles: a New Portfolio-based Graphical Authentication System[END_REF].

A variety of studies have been reported that underpin the necessity for increasing usability of authentication mechanisms [START_REF] Shay | Correct Horse Battery Staple: Exploring the Usability of System-assigned Passphrases[END_REF][START_REF] Komanduri | Of Passwords and People: Measuring the Effect of Password-composition Policies[END_REF][START_REF] Inglesant | The True Cost of Unusable Password Policies: Password use in the Wild[END_REF][START_REF] Florencio | Large-scale Study of Web Password Habits[END_REF][START_REF] Adams | Users are not the Enemy: Why Users Compromise Security Mechanisms and How to Take Remedial Measures[END_REF][START_REF] Shay | Encountering Stronger Password Requirements: User Attitudes and Behaviors[END_REF][START_REF] Bonneau | The Quest to Replace Passwords: A Framework for Comparative Evaluation of Web Authentication Schemes[END_REF][START_REF] Leblanc | Guessing Click-based Graphical Passwords by Eye Tracking[END_REF]. The literature reveals many proposals, such as educating and influencing users to create more secure authentication keys [START_REF] Forget | Improving Text Passwords Through Persuasion[END_REF], improving existing recall-based password approaches with recognition of text [START_REF] Wright | Do You See Your Password?: Applying Recognition to Textual Passwords[END_REF], enforcing the creation of secure authentication keys through policies [START_REF] Komanduri | Of Passwords and People: Measuring the Effect of Password-composition Policies[END_REF][START_REF] Inglesant | The True Cost of Unusable Password Policies: Password use in the Wild[END_REF], automatically generating secure authentication keys and mnemonic passphrases [START_REF] Kuo | Human Selection of Mnemonic Phrase-based Passwords[END_REF]. Furthermore, password managers [START_REF] Halderman | Convenient Method for Securely Managing Passwords[END_REF][START_REF] Chiasson | Usability Study and Critique of Two Password Managers[END_REF] have been proposed to minimize users' cognitive load. Nevertheless, ineffective practice of usability in user authentication does not naturally embed the users' characteristics in the design process, and usually adopts a "one-size-fits-all" approach when concerning user authentication designs, ignoring the fact that different users have different characteristics and develop different structural and functional mental models, and thus need individual scaffolding. In this respect, supporting usability of user authentication mechanisms with useradaptive technologies [START_REF] Brusilovsky | The Adaptive Web: Methods and Strategies of Web Personalization[END_REF] is based on the promise that understanding and modeling human behavior in terms of structural and functional user requirements related to such security tasks can provide an alternative to the "one-size-fits-all" approach with the aim to improve the system's usability and provide a positive user experience.

Consequently, a first step toward designing an adaptive user authentication mechanism is to identify which individual characteristics (e.g., knowledge, previous experience, lingual characteristics, cognitive characteristics, etc.) are considered important for adapting such mechanisms. Bearing in mind that human computer interactions with regard to authentication mechanisms are in principal cognitive tasks that embrace to recall and/or recognize, process and store information, we argue that these interactions should be analyzed in more detail under the light of cognitive theories. Accordingly, the purpose of this paper is to investigate whether there is an influence of specific individual characteristics of users targeting on cognitive factors, toward efficiency, effectiveness and user preference of authentication mechanisms.

The rest of the paper is structured as follows: next we present the underlying theory of this work. Furthermore, we describe the context of an empirical study, methods, and developed hypotheses. Thereafter, we analyze and discuss our findings. Finally, we summarize our paper and outline the implications of the reported research.

Theoretical Background

One of the most widely accepted theory of human cognition is the Dual Coding Theory [START_REF] Paivio | Mind and Its Evolution: A Dual Coding Theoretical Approach[END_REF][START_REF] Paivio | Picture Superiority in Free Recall: Imagery or Dual Coding?[END_REF]. It suggests that visual and verbal information is processed and represented differently and along two distinct cognitive sub-systems in the human mind; the visual and verbal cognitive sub-systems. Each sub-system creates separate representations for information processed which are used to organize incoming information that can be acted upon, stored, and retrieved for subsequent use. Many psychology studies have reported that pictures are better recognized and recalled by the human brain than textual information, referred as the picture superiority effect [START_REF] Anderson | Cognitive Psychology and its Implications: Seventh Edition[END_REF][START_REF] Ally | The Worth of Pictures: Using High Density Event Related Potentials to Understand the Memorial Power of Pictures and the Dynamics of Recognition Memory[END_REF][START_REF] Brady | Visual Long-term Memory has a Massive Storage Capacity for Object Details[END_REF]]. Paivio's Dual Coding Theory explains the picture superiority effect that pictures are more perceptually rich than words which lends them an advantage in information processing. The picture superiority effect might be explained by the fact that pictures are mentally represented along with the features being observed, whereas text is visually sparse and represented symbolically, where symbols might have a different meaning depending on the form of the text, which requires an additional processing for the verbal sub-system. For example, 'X' may represent the Roman numeral 10, or the multiplication symbol [START_REF] Biddle | Graphical Passwords: Learning from the First Twelve Years[END_REF].

The advantage of the picture superiority effect has led to the design of graphical authentication schemes as a viable alternative to text-based authentication mechanisms aiming to increase efficiency and effectiveness of interactions. In graphical authentication mechanisms, human memory is leveraged for visual information in hope of a reduced memory burden that will facilitate the selection and use of more secure authentication keys [START_REF] Biddle | Graphical Passwords: Learning from the First Twelve Years[END_REF]. Given also that the current user authentication paradigm expands from login on single desktop computers, to login on smart devices, the added difficultly of typing text-based passwords on small screens has amplified the attention to graphical authentication mechanisms as an alternative to traditional textbased password schemes.

However, several research findings claim that the picture superiority effect does not always hold and is affected by various factors [START_REF] Brady | Visual Long-term Memory has a Massive Storage Capacity for Object Details[END_REF][START_REF] Oates | Memory for Pictures: Sometimes a Picture is not Worth a Single Word. Successful Remembering and Successful Forgetting: A Festschrift in Honor of Robert A. Bjork[END_REF][START_REF] Robertson | Insights from Child Development on the Relationship between Episodic and Semantic Memory[END_REF]. Oates and Reder [START_REF] Oates | Memory for Pictures: Sometimes a Picture is not Worth a Single Word. Successful Remembering and Successful Forgetting: A Festschrift in Honor of Robert A. Bjork[END_REF] claim that the picture superiority effect only occurs when a picture affords a meaningful textual label that discriminates it from other pictures. Results of these studies reveal that abstract pictures are not memorable as single words since the visual stimuli is difficult to identify, and hence, a generation of a consistent textual label is not easy or possible. Robertson and Köhler [START_REF] Robertson | Insights from Child Development on the Relationship between Episodic and Semantic Memory[END_REF] have further provided evidence that the ability to label a picture affects its processing and memory. Another recent study of Mihajlov and Jerman-Blazic [START_REF] Mihajlov | On Designing Usable and Secure Recognition-based Graphical Authentication Mechanisms[END_REF], that assessed the memorability rates of abstract, face and single-object images in recognition-based, graphical authentication mechanisms, supports that single-object images have the highest memorability rates in graphical authentication mechanisms, since single-object images can be easily labeled (e.g., "picture of a football"), and thus processed and remembered.

As an effort to explain the aforementioned empirically observed differences in users' mental representation and processing of information, many researchers have developed theories of individual differences in cognitive style from the perspective of dual coding theory [START_REF] Riding | Cognitive Styles -An Overview and Integration[END_REF], and consequently, argue that individuals have differences in the way they process and remember information. In particular, individuals may process and organize verbal information more efficiently than visual information, whilst others the opposite [START_REF] Riding | Cognitive Styles -An Overview and Integration[END_REF][START_REF] Peterson | Researching the Psychology of Cognitive Style and Learning Style: Is There Really a Future?[END_REF][START_REF] Blazhenkova | The New Object-Spatial-Verbal Cognitive Style Model: Theory and Measurement[END_REF][START_REF] Peterson | A New Reliable Measure of Verbal-Imagery Cognitive Style[END_REF][START_REF] Riding | Cognitive Styles Analysis. Learning and Training Technology[END_REF][START_REF] Kozhevnikov | Cognitive Styles in the Context of Modern Psychology: Toward an Integrated Framework of Cognitive Style[END_REF]. Within this context, a number of psychometric tests and questionnaires have been developed that elicit verbal-imagery cognitive style, mainly through self-reported experiences and preferences, and response times on verbal vs. visual aptitude tasks (OSIVQ questionnaire: [START_REF] Blazhenkova | The New Object-Spatial-Verbal Cognitive Style Model: Theory and Measurement[END_REF]; VICS test: [START_REF] Peterson | A New Reliable Measure of Verbal-Imagery Cognitive Style[END_REF]; CSA test [START_REF] Riding | Cognitive Styles Analysis. Learning and Training Technology[END_REF]; see also [START_REF] Riding | Cognitive Styles -An Overview and Integration[END_REF][START_REF] Kozhevnikov | Cognitive Styles in the Context of Modern Psychology: Toward an Integrated Framework of Cognitive Style[END_REF] for a review on older questionnaires and psychometric tests). Selfreported questionnaires usually ask the participants to rate their preference toward a verbal versus visual mode of processing. Example ratings would be "I have a photographic memory" or "My verbal skills are excellent" [START_REF] Blazhenkova | The New Object-Spatial-Verbal Cognitive Style Model: Theory and Measurement[END_REF]. For the reason that questionnaires showed relatively low internal reliability and poor predictive validity [START_REF] Peterson | A New Reliable Measure of Verbal-Imagery Cognitive Style[END_REF][START_REF] Mcavinue | Measuring Visual Imagery Ability: A Review[END_REF], objective measures through the development of psychometric tools have emerged, such as response time in solving cognitive tasks that require verbal or visual processing. In particular, psychometric tools have been proposed that typically require from the participant to provide an answer to text-based or image-based statements. Depending on the response time of each answer, the ratio of means or medians be-tween the verbal and visual statements is computed and further used to classify the participant to a particular group; Verbal or Imager group.

To this end, with the aim to investigate the relation among individual differences in cognitive style, and user authentication mechanisms in terms of efficiency, effectiveness and preference, we next describe an empirical study which entailed a psychometric-based survey for extracting users' cognitive styles, combined with a real usage scenario with two variations of user authentication mechanisms; text-based password and recognition-based, graphical authentication mechanism. Such an endeavor is considered valuable as it entails investigating and modeling human behavior in the context of user authentication tasks. Understanding structural and functional user mental models assists the deployment of usable computer-human interaction designs and workflows, whilst minimizing user cognitive loads, perceptual and learning efforts and erroneous interactions with regard to authentication mechanisms

3

Method of Study

Procedure

A Web-based environment was developed within the frame of various university courses. During each course enrolment process, students were required to create their authentication key that was used for accessing the courses' material (i.e., course slides, homework exercises) and for viewing their grades. The experimental procedure has been divided in three phases:

 Phase A: The type of authentication (text-based password or graphical mechanism) was randomly provided during the enrolment process. At the end of the enrollment process the sample was divided of half of the students having enrolled with a text-based password and the other half having enrolled with a graphical authentication mechanism. The users' interactions during this phase were recorded for a period of one and a half month.  Phase B: After Phase A, the system altered the students' authentication type; students that had enrolled with a text-based password in Phase A were prompted to create a new graphical authentication key and vice versa. The new authentication key would be used for the same period as in Phase A (one and half month). The main aim of Phase B was to engage the whole sample on both authentication mechanisms for the same period of time.  Phase C: After Phase B, the system gave the users the option to choose the preferred authentication type to access the system until the end of the study. In particular, the students were first asked to choose between the two variations of authentication (i.e., text-based password or graphical) for which they had already used in Phase A or Phase B during the semester, and then entered the preferred authentication key. Aiming to avoid the effect of users' familiarity with the previously (Phase B) used authentication mechanism, with regard to preference tication mech e recognitionnd Jerman-Bla nism was base are better me dual coding th ger cognitive s rs could freely random subse se the user wa nt random im the sequence the graphical ontaining the were perman decoy images uld be easily essions [START_REF] Mihajlov | On Designing Usable and Secure Recognition-based Graphical Authentication Mechanisms[END_REF]. D em-selected de ion grid were had to select nism involved y the user. A nd upper-case . An additiona users forgot t nd a hyperlink sed password. entication mech anism that inv -based, graph azic [START_REF] Mihajlov | On Designing Usable and Secure Recognition-based Graphical Authentication Mechanisms[END_REF]. The ed on the fact emorized and heory [START_REF] Paivio | Mind and Its Evolution: A Dual Coding Theoretical Approach[END_REF][START_REF] Paivio | Picture Superiority in Free Recall: Imagery or Dual Coding?[END_REF] styles were de y select betwe et of thirty im as not satisfie mage subset w (in this case authenticatio user-selected ently attached were to chan revealed by e During authen ecoy images w e randomly po t their images d alphanumeri minimum of e letters, and al option for r their authentic k was sent to t .

anism used in t volved single hical authentic e choice of th t that its theor recognized b ], which is con eveloped [START_REF] Riding | Cognitive Styles -An Overview and Integration[END_REF]. een eight to tw mages that wer d with the pre was available.

the key lengt n key was cre authenticatio d to the userna nge every auth eliminating th ntication, a 4 were presented ositioned in e s in the specif ic and special eight characte special charac resetting the t cation key. In their email th the study.

-object image cation mechan his particular retical assump by the human nsidered the b . During the a welve images re retrieved fro esented choic Repetitions th could be lo eated, a fixed on images an ame in order t hentication se he non-repeate x 4 grid cont d (Figure 2). T each authentic fic sequence, a keyboard ers includcters were text-based that case, at led to a es was denism prographical ption (i.e., n mind) is basis upon authentica-, in a speom a large ces, an opof images onger than image set d systemto increase ession, the ed images aining the The image cation sesas entered in the enrolment process in order to get permission for accessing the system. An additional option for resetting the authentication key was also available which was similar to the text-based password reset process.

Hypotheses

The following hypotheses were formulated for the purpose of our research:

 H 1 .

Participants

A total of 145 people participated in the study between February and June 2012. Participants varied from the age of 17 to the age of 26, with a mean age of 22 and were undergraduate students of Computer Science, Electrical Engineering, Psychology and Social Science departments of the University of Cyprus. participants' native language was Greek, and had learned English as a second language. A total of 2605 authentications have been recorded during the five-month period, with an average of 19.79 (SD 11.62) logins per participant.

Data Captured

Both client-side and server-side scripts were developed to monitor the users' behaviour during interaction with the authentication mechanisms. The following data was captured:

 Performance (Efficiency and Effectiveness): The total time (efficiency) and total number of attempts (effectiveness) required for successful authentication was monitored on the client-side utilizing a browser-based logging facility that started recording time as soon users entered their username for identification, until they successfully completed the authentication process. Additional performance data included total number of authentication key resets.  Preference: The users' choice between the two variations of authentication during the last one and a half month of the study (Phase C) was used to draw conclusions about their preference toward a particular type of authentication.

Compl particular group ses

An

For our a Verbal (N pants that Efficienc variance ers' cogn password Figure 3 cation typ group (V ical).

Fi

The an complish tion type class perf tication m significan p=0.785) A further comparison between the cognitive style groups revealed that Imagers performed significantly faster in graphical authentication than Verbals (F(1,514)=8.292, p=0.004). However, performance in text-based password mechanisms revealed no significant differences in time since both cognitive style groups performed similarly (F(1,381)=0.838, p=0.361), with Verbals being slightly faster. Given that both types of user groups were more familiar and experienced with textbased passwords, this might explain the fact that no significant differences have been observed in the time to authenticate through the text-based password mechanism. On the other hand, since the familiarity factor has not affected the graphical authentication mechanism, results indicate that the different approach in processing information mentally of each user group (visual approach for Imagers and verbal approach for Verbals) has affected their performance in the graphical authentication mechanism.

Effectiveness related to User Authentication. For each user authentication session, the total number of attempts made for successfully authenticating in each type was recorded. Table 2 summarizes the means of attempts of each user group (Verbal and Imager) per authentication type (text-based password and graphical). Regarding the text-based password, although Verbals needed on average less attempts to authenticate, no significant differences were observed between the two user groups, as the Mann-Whitney U test revealed (p=0.183). In the case of graphical authentication, on average, users belonging to the Verbal class needed more attempts to authenticate than the Imager group. The Mann-Whitney U test revealed that the differences between Verbal and Imager user groups were statistically significant (p<0.001). In this respect, Imager users' enhanced ability of processing visual information has positively affected their effectiveness compared to Verbal users. This is further strengthened by the fact that the majority of authentication key resets were initiated by Verbal users for graphical authentication keys. In particular, a total of 7 authentication key resets were initiated by Imagers for graphical authentication mechanisms while in the case of Verbals the number of key resets rose to 24. In case of text-based passwords, a total of 11 password resets were performed; 4 by Imagers and 7 by Verbals.

Preference related to User

Focus Groups

Semi-structured focus-group sessions were concentrated around the participants' subjective preference and perception of the authentication mechanisms. The question structure of the sessions was focused around the following points: i) which type of authentication the users prefer, ii) which type of authentication was more efficient, iii) which type of authentication was more effective.

The first question for each session was intended to explore the users' preference on authentication mechanisms. The participants were asked to rate their preference toward a particular type of authentication mechanism (text-based password or graphical). A binomial statistical test was conducted to examine whether there is a general preference relating text-based passwords and graphical authentication mechanisms (H 0 : p(text-based password)=0.5 and p(graphical)=0.5). The results revealed that overall users preferred graphical authentication (p=0.008) with a total of 89 users preferring graphical authentication mechanisms compared to 56 users who preferred the text-based password mechanism (Table 4). A binomial test was conducted separately for each cognitive style group to examine whether a particular cognitive style group prefers specific types of authentication mechanisms. Results revealed that Imagers significantly prefer graphical authentication mechanisms (p=0.001). This supports the aforementioned analysis of results that indicated a significant effect of cognitive style toward preference of authentication mechanisms. In contrast, regarding the Verbal group no clear preference toward textbased passwords or graphical authentication mechanisms was recorded (p=0.904).

As for efficiency and effectiveness of the authentication mechanisms, the participants were asked to rate the efficiency and effectiveness of each mechanism. Table 5 and Table 6 respectively summarize the users' rating regarding efficiency and effectiveness for each authentication type. A Pearson's chi-square test did not reveal a significant difference in efficiency between the text-based password and graphical authentication mechanism (Chi square value=2.452, df=1, p=0.117). Nevertheless, a noticeable difference in opinions about the efficiency of the two authentication types was observed by participants who belonged to the Verbal class since the majority found the text-based password mechanism more efficient. Finally, when asked to rate the difficulty of each authentication type, the majority claimed that graphical authentication mechanisms were easier to recall and recognize the information.

Validity and Limitations of the Study

With the aim to increase internal validity we recruited a sample of participants that were rather experienced and average than novice users with respect to user authentication and therefore, the research design was setup in order to avoid inference errors. There has also been an effort to increase ecological validity of the research since the user authentication tasks were integrated in a real Web-based system and the participants were involved at their own physical environments without the intervention of any experimental equipment or person. In addition, participants were required to authenticate in the system throughout the semester during real-life tasks (i.e., access their university course's material). Finally, given that future studies will contribute to the external validity of the reported research, we argue that providing personalized user authentication mechanisms, adapted to users' cognitive characteristics, as well as other individual characteristics (e.g., cognitive processing abilities, working memory capacity) could improve the overall user experience with regard to user authentication tasks.

The limitations of the reported study are related to the fact that participants were only university students with an age between 17 to 26 years, and shared common cultural backgrounds. Given that Asian-type alphabets are primarily processed by the visual cognitive sub-system, in contrast to the Western-type alphabets that are primarily processed by the verbal cognitive sub-system [START_REF] Tavassoli | Temporal and Associative Memory in Chinese and English[END_REF], cognitive styles might have affected differently the user performance and preference of the authentication mechanisms in case participants had knowledge of non-Western-type languages. In addition, carrying out a single assessment of users' cognitive style might not fully justify the users' classification into specific cognitive-based groups since individuals might be influenced by other circumstances over time such as emotions, urgency, etc. Finally, the results might be affected by the fact that the two types of authentication mechanisms diverge in term of user interface since the text-based password keys are entered through keyboard clicks, while the graphical keys are entered through mouse clicks on the images. To this end, future studies need to be conducted with a greater sample of varying profiles, cultures and ages, as well as investigate the effect of contextual factors (i.e., user device) in combination with cognitive styles of users in order to reach to more concrete conclusions about the effect of individuals' cognitive style on their performance and preference related to authentication mechanisms. In addition, considering that in real life, users have to memorize multiple authentication keys [START_REF] Everitt | A Comprehensive Study of Frequency, Interference, and Training of Multiple Graphical Passwords[END_REF], another important aspect for investigation is to study the effect of cognitive styles on user performance and preference when using multiple text-based password and graphical authentication mechanisms over time.

Conclusions

The purpose of this paper is to present results of an ecological valid user study which was designed with the aim to investigate whether there is a main effect of specific individual characteristics of users targeting on cognitive styles, towards performance and preference of two complementary types of user authentication mechanisms (textbased and graphical). Such an endeavor is considered valuable for the design and the deployment of more usable computer-human interaction processes with the aim to offer personalized and adaptive authentication mechanisms aiming to assist users to accomplish efficiently and effectively comprehensive and usable authentication tasks.

For the purpose of this research we have designed a three phase experimental study which entailed a credible psychometric-based test for eliciting users' cognitive style based on the dual coding theory that suggests two sub-cognitive systems for processing and representing verbal and visual information in parallel threads.

The results of this paper can be interpreted under the light of this theory as they demonstrate a main effect of cognitive styles on both performance and preference related to authentication mechanisms. Regarding performance, results revealed that users of the Imager class performed faster in graphical authentication than in textbased password authentication, whereas users of the Verbal class did not authenticate significantly faster in either of the two authentication types. Furthermore, Imagers performed significantly faster in graphical authentication than Verbals did, however in the case of text-based passwords performance of both cognitive style groups was not considerably different, with Verbals being slightly faster than Imagers. An interpretation of this result can be based on the fact that all users were more familiar and experienced interacting with text-based passwords, hence no significant difference was observed between the Verbal and the Imager. On the other hand, since the familiarity factor did not affect the graphical authentication mechanism, we have observed that the visual approach of processing and organizing information of the Imagers has positively affected their performance compared to the Verbals.

Regarding effectiveness (i.e., total number of attempts), we conclude that both authentication mechanisms were effective in use throughout the study. Nevertheless, both number of attempts and number of authentication key resets reveal that graphical authentication mechanisms have positively affected user belonging to the Imager class as they needed significantly less attempts and key resets in graphical authentication mechanisms compared to Verbal users.

Finally, participants in general preferred graphical authentication mechanisms. Results also demonstrate that users categorized in the Imager group significantly prefer graphical authentication mechanisms. A possible interpretation of this result might be based on the novelty effect of graphical authentication. However, results suggest that if this would be the main factor that influences users' preference then it would be observed across all user groups regardless their cognitive style, which in the current sample is not the case, since users categorized into the Verbal group did not significantly prefer a particular authentication type.

Based on the presented results which embrace objective quantitative data (captured data during experimentations) as well as subjective qualitative self-reporting data (focus group studies), we argue that following a user-centered design methodology, it is necessary that designers of authentication mechanisms should clearly bear in mind individual differences of users while interacting with the system. Currently, there is a strong underlying design assumption that text-based passwords are the most comprehensive way for user authentication [START_REF] Herley | A Research Agenda Acknowledging the Persistence of Passwords[END_REF]. The results of this study suggest enhancing current authentication mechanisms aiming to embrace both text-based and recognition-based, graphical authentication mechanisms. As the results suggest, such an approach would have many positive implications from a usability and user experience point of view since, recommending authentication mechanisms, personalized to the users' cognitive style (especially in the case of Imagers, as results indicate) would increase the users' memorability and information processing efficiency of the authentication key, and thus improve task completion efficiency and effectiveness, and user satisfaction. At the same time, graphical authentication mechanisms provide similar security protection levels as text-based passwords from a service provider point of view, taking into consideration that they are encrypted properly on the service provider database layer and submitted securely on the transmission layer [START_REF] Mihajlov | On Designing Usable and Secure Recognition-based Graphical Authentication Mechanisms[END_REF][START_REF] Biddle | Graphical Passwords: Learning from the First Twelve Years[END_REF]. On the other hand, from a user's point of view, they entail similar threats as text-based authentication mechanisms with regard to guessing (e.g., brute-force attacks) or capturing attacks (e.g. shoulder surfing, malware, phishing attacks, etc.) [START_REF] Mihajlov | On Designing Usable and Secure Recognition-based Graphical Authentication Mechanisms[END_REF].

A practical implication of this work could be either to allow a user to explicitly declare the preferred authentication mechanism or by implicitly recommend the "bestfit" authentication mechanism based on historical usage data of the user in regard with efficiency and effectiveness of authentication tasks, or based on user interactions with a psychometric-based, cognitive style elicitation test. A more sophisticated architecture solution could be based on a recommendation engine as part of an adaptive system specialized on user authentication tasks aiming to extract from interaction data, the individuals' characteristics by use of statistical means.

Studies like the reported one can be useful for improving usable security on the World Wide Web through adaptivity in user interface designs with regard to authentication mechanisms, aiming to organize and present information and functionalities related with security tasks in an adaptive format to diverse user groups, by using different levels of abstractions through appropriate interaction styles, terminology, information presentation and user modeling techniques.

Fig. 2

 2 Fig. 2. G phical authent based on the y Mihajlov an ation mechan bject images a elated to the d e Verbal/Imag creation, user uence out of a atabase. In cas oad a differen o possible in t mages). After n images, co decoy images since if the d ation key cou subsequent se cted and syste in the selecti ereafter, users

  Authentication. During Phase C, the system provided the option to the users to choose which authentication type to use for accessing the

  There is general preference of users toward text-based password mechanisms or recognition-based, graphical authentication mechanisms.  H 2 . Cognitive styles of users have a main effect on users' preference toward textbased password mechanisms or recognition-based, graphical authentication mechanisms.  H 3 . There is a significant difference with regard to time (efficiency) and total number of attempts (effectiveness) needed to authenticate through a text-based password mechanism or a recognition-based, graphical authentication mechanism among users belonging to the Verbal and Imager class.

Table 1 .

 1 Eff

	T
	G

Table 2 .

 2 Effectiveness per cognitive style group and authentication type.

			Verbal		Imager
		Mean	Std. Dev.	Mean	Std. Dev.
	Text-based	1.15	0.40	1.30	0.63
	Graphical	1.23	0.50	1.09	0.34
	Shapiro-Wilk tests revealed that these distributions do not follow the normal distribu-
	tion.

Table 3 .

 3 Pre

		current authen ntication type	that was activ ve after Phase e B, or use
	n type of Phas se A. In Table e 3 we summ marize the user rs' prefer-
	ular authentica ation type bas ed on cognitiv ve style. Figu ure 4 illus-
		of authentica ation sessions i in Phase C pe er cognitive sty yle group.
	eference per co	gnitive style gr roup and authen ntication type
				Verbal	Im mager	Tot tal
	Te ext-based		29		23	52 2
	Gr raphical		39		54	9	3
	To otal		68		77	14 45
	omial statistic cal test was c conducted to	examine whe ether there is	a general
	ce relating tex xt-based pass swords and g graphical auth hentication me echanisms
	ext-based pas sword)=0.5 a and p(graphic cal)=0.5). The e results reve ealed that
	users preferred d graphical au uthentication (p p=0.001).
	Fig g. 4. Users' pref ference per cog gnitive style gro oup.
	a Pearson's ch hi-square test		was conducte ed to examine	whether there e is a rela-
	between users s' cognitive st tyle and their	preference to oward a specif fic type of
	ation (i.e., te xt-based pass sword or grap phical). The r results reveal	ed that in
	here is no sig gnificant relat tionship betw een these two o variables (C Chi square
	563, df=1, p= =0.109). Furt thermore, exa amining each	cognitive sty yle group
	ally with respe ect to preferen nce toward a p particular auth hentication m mechanism,
	een identified	that users be elonging to th he Imager cla ass significan ntly prefer
	l authenticatio on mechanism ms (p=0.001). I In contrast ho owever, users	belonging
	Verbal class d do not signifi	cantly prefer	a particular	type of auth hentication
	).

Table 4 .

 4 Authentication preference per cognitive style group.

		Verbal	Imager	Total
	Text-based	33	23	56
	Graphical	35	54	89
	Total	68	77	145

Table 5 .

 5 Authentication efficiency per cognitive style group.

		Verbal	Imager	Total
	Text-based	38	33	71
	Graphical	30	44	74
	Total	68	77	145

Table 6 .

 6 Authentication effectiveness per cognitive style group.

		Verbal	Imager	Total
	Text-based	28	22	50
	Graphical	40	55	95
	Total	50	95	145

Acknowledgements. We would like to thank all the participants of the study for their time and qualitative feedback in the frame of the focus group sessions. The work is co-funded by the PersonaWeb project under the Cyprus Research Promotion Foundation (ΤΠΕ/ΠΛΗΡΟ/0311(ΒΙΕ)/10), and the EU projects Co-LIVING (60-61700-98-009) and SocialRobot (285870).