Original: Exploring the Effects of Intended Use on Targeting in Virtual Reality
Revised: Exploring the Effects of Intended Use on Targeting in Virtual Reality

Original: A BSTRACT
Revised: A BSTRACT

Original: Researchers have shown that distance and size are not the only fac- tors that impact the target acquisition time in desktop interfaces, but that its intended use, whether it is selected, dragged, or other- wise manipulated, can also have a significant influence.
Revised: Researchers have shown that distance and size are not the only fac- tors that impact the target acquisition time in desktop interfaces, but that its intended use, whether it is selected, dragged, or other- wise manipulated, can also have a significant influence.

Original: However, despite the increasing popularity of virtual 3D environments, the intended use of targets in these contexts has never been investigated, in spite of the richer, multidimensional manipulations afforded by these environments.
Revised: However, despite the increasing popularity of virtual 3D environments, the intended use of targets in these contexts has never been investigated, in spite of the richer, multidimensional manipulations afforded by these environments.

Original: To better understand the effects of intended use on target acquisition in virtual environments, we present the results of a study examining five different manipulation tasks: targeting, dual-targeting, throwing, docking and reorienting.
Revised: To better understand the effects of intended use on target acquisition in virtual environments, we present the results of a study examining five different manipulation tasks: targeting, dual-targeting, throwing, docking and reorienting.

Original: Our results demonstrate that the intended use of a target affects its acquisition time and, correspondingly, the movement towards the target.
Revised: Our results demonstrate that the intended use of a target affects its acquisition time and, correspondingly, the movement towards the target.

Original: As these environments become more commonplace settings for work and play, our work provides valuable information on throughput, applicable to a wide range of tasks.
Revised: As these environments become more commonplace settings for work and play, our work provides valuable information on throughput, applicable to a wide range of tasks.

Original: 1 I NTRODUCTION
Revised: 1 I NTRODUCTION

Original: Target acquisition is one of the most common actions performed in an interactive system regardless of whether the computing platform is a desktop computer [25,36], a touch-based device [23], a large physical display [51] or an Augmented Reality environment [13].
Revised: Target acquisition is one of the most common actions performed in an interactive system regardless of whether the computing platform is a desktop computer [26,37], a touch-based device [24], a large physical display [52] or an Augmented Reality environment [14].

Original: Fitts’s Law [36] is the most commonly used model to describe the movement time taken to acquire a target.
Revised: Fitts’s Law [37] is the most commonly used model to describe the movement time taken to acquire a target.

Original: Movement time ( MT ) for a one-dimensional pointing task is described by a linear function of the index of difficulty ( ID ) of the pointing task:
Revised: Movement time ( MT ) for a one-dimensional pointing task is described by a linear function of the index of difficulty ( ID ) of the pointing task:

Original: [Equation]
Revised: [Equation]

Original: where a, b are empirically determined regression coefficients and ID is a logarithmic term of target amplitude (A) and target width (W) [35]:
Revised: where a, b are empirically determined regression coefficients and ID is a logarithmic term of target amplitude (A) and target width (W) [36]: ID [Equation] (cid:19)

Original: [Equation]
Revised: [Equation]

Original: However, target amplitude and width are not the only factors impacting acquisition performance [62].
Revised: However, target amplitude and width are not the only factors impacting acquisition performance [63].

Original: One factor that may impact target acquisition time is the intended use of the target.
Revised: One factor that may impact target acquisition time is the intended use of the target.

Original: By intended use , we refer to manipulations that a user intends to do or will do with the target they acquire.
Revised: By intended use , we refer to manipulations that a user intends to do or will do with the target they acquire.

Original: For example, in virtual environments, beyond simply acquiring an object, users can scale objects and move them around in the space [61], rotate objects to reveal the occluded view [15] or manipulate objects’ motion to simulate physical phenomena [4].
Revised: For example, in virtual environments, beyond simply acquiring an object, users can scale objects and move them around in the space [62], rotate objects to reveal the occluded view [16] or manipulate objects’ motion to simulate physical phenomena [4].

Original: In order to perform any of these intended uses of a target, we must first acquire it.
Revised: In order to perform any of these intended uses of a target, we must first acquire it.

Original: Acting on an object in an interface is thus a compound action comprised of acquiring it and performing the action or manipulation of it (See Figure 1) and the question we pose in this paper is whether the manipulation that we will perform on an object in a virtual environment, our intended use, impacts the performance of acquiring the target.
Revised: Acting on an object in an interface is thus a compound action comprised of acquiring it and performing the action or manipulation of it (See Figure 1) and the question we pose in this paper is whether the manipulation that we will perform on an object in a virtual environment, our intended use, impacts the performance of acquiring the target.

Original: In 2D computer interfaces, research has shown that target ac-
Revised: In 2D computer interfaces, research has shown that target ac-

Original: quisition time may vary depending on whether the user wishes to simply acquire a target (e.g. targeting), move it (e.g. dragging or docking), or throw it (e.g. flicking) – independent of the time taken by the subsequent task [37,47].
Revised: quisition time may vary depending on whether the user wishes to simply acquire a target (e.g. targeting), move it (e.g. dragging or docking), or throw it (e.g. flicking) – independent of the time taken by the subsequent task [38,48].

Original: It is always tempting to assume that results in 2D can be directly applied to 3D, particularly to Virtual
Revised: It is always tempting to assume that results in 2D can be directly applied to 3D, particularly to Virtual

Original: Reality (VR), but we hesitate to make such assumption for several reasons.
Revised: Reality (VR), but we hesitate to make such assumption for several reasons.

Original: For instance, the controller in VR is an absolute input while the mouse is a relative input, leading to different acquisition and manipulation behaviours, and some tasks more frequent in 3D were not investigated in 2D (e.g. reorienting a target).
Revised: For instance, the controller in VR is an absolute input while the mouse is a relative input, leading to different acquisition and manipulation behaviours, and some tasks more frequent in 3D were not investigated in 2D (e.g. reorienting a target).

Original: Besides, to the best of our knowledge no similar analysis has been performed in the increasingly common context of direct target acquisition in VR.
Revised: Besides, to the best of our knowledge no similar analysis has been performed in the increasingly common context of direct target acquisition in VR.

Original: As we incorporate VR into both work [17,34] and entertainment [50], understanding the impact of various independent variables on per- formance measures for VR-based selection tasks remains an area of active research interest (see [9] for a review).
Revised: As we incorporate VR into both work [18,35] and entertainment [51], understanding the impact of various independent variables on per- formance measures for VR-based selection tasks remains an area of active research interest (see [10] for a review).

Original: To explore the impact of intended use on targeting in virtual en- vironments, more precisely in the context of proximal interaction using the virtual hand metaphor, we examine five common manip- ulations in various VR systems, such as games [3], social [38] and educational applications [24] – T ARGETING (selecting a target), D UAL T ARGETING (selecting one then another target), T HROWING (acquiring and pushing a target), D OCKING (acquiring and placing a target) and R EORIENTING (rotating a target about its axes) – and measure how long it takes to acquire the target to be manipulated.
Revised: To explore the impact of intended use on targeting in virtual en- vironments, more precisely in the context of proximal interaction using the virtual hand metaphor, we examine five common manip- ulations in various VR systems, such as games [3], social [39] and educational applications [25] – T ARGETING (selecting a target), D UAL T ARGETING (selecting one then another target), T HROWING (acquiring and pushing a target), D OCKING (acquiring and placing a target) and R EORIENTING (rotating a target about its axes) – and measure how long it takes to acquire the target to be manipulated.

Original: We find that simple targeting exhibits the fastest acquisition times for that target, reorienting the slowest acquisition times, and other intended target manipulations result in acquisition times between these extremes.
Revised: We find that simple targeting exhibits the fastest acquisition times for that target, reorienting the slowest acquisition times, and other intended target manipulations result in acquisition times between these extremes.

Original: Given the initial finding that the intended use of the target impacts time, we probe additional characteristics of movement toward a target with the goal of understanding how and why prior movement time is impacted by the subsequent intended use of the target.
Revised: Given the initial finding that the intended use of the target impacts time, we probe additional characteristics of movement toward a target with the goal of understanding how and why prior movement time is impacted by the subsequent intended use of the target.

Original: Movement profiles highlight both characteristics and differences in peak speed, time to peak speed, movement prior to selection and selection speed across different intended uses.
Revised: Movement profiles highlight both characteristics and differences in peak speed, time to peak speed, movement prior to selection and selection speed across different intended uses.

Original: This paper is organized as follows.
Revised: This paper is organized as follows.

Original: After presenting the related work on target acquisition modelling in both 2D and 3D, we describe and explain the experiment setup and task design of our study.
Revised: After presenting the related work on target acquisition modelling in both 2D and 3D, we describe and explain the experiment setup and task design of our study.

Original: Then, we detail our analysis from three aspects: selection time, motion kinematics and Fitts’ law modelling.
Revised: Then, we detail our analysis from three aspects: selection time, motion kinematics and Fitts’ law modelling.

Original: Finally, we present our findings and discuss their implications.
Revised: Finally, we present our findings and discuss their implications.

Original: In summary, this is the first work to explore the impact of intended use on targeting in VR and our contributions include:
Revised: In summary, this is the first work to explore the impact of intended use on targeting in VR and our contributions include:

Original: • Investigating impacts of intended use on target acquisition in VR and summarizing characteristics of each use.
Revised: • Investigating impacts of intended use on target acquisition in VR and summarizing characteristics of each use.

Original: • Presenting design implications for interfaces and interactions in VR.
Revised: • Presenting design implications for interfaces and interactions in VR.

Revised: 2 R ELATED W ORK

Original: Fitts’ Law [21, 36] is probably the most well-examined relation- ship in human-computer interaction (HCI) research.
Revised: Fitts’ Law [22, 37] is probably the most well-examined relation- ship in human-computer interaction (HCI) research.

Original: While Fitts’
Revised: While Fitts’

Original: Law was originally formulated in terms of a 1D pointing task, re- searchers in HCI have long recognized that understanding the cost of target acquisition in graphical interfaces is useful, as it allows us to characterize the relative efficiency of different arrangements of interfaces.
Revised: Law was originally formulated in terms of a 1D pointing task, re- searchers in HCI have long recognized that understanding the cost of target acquisition in graphical interfaces is useful, as it allows us to characterize the relative efficiency of different arrangements of interfaces.

Original: As a result, researchers have proposed a number of extensions to Fitts’ Law to model 2D targeting [2], 3D targeting [53], gaze-based targeting [57], foot-based movement [27, 56], among others.
Revised: As a result, researchers have proposed a number of extensions to Fitts’ Law to model 2D targeting [2], 3D targeting [54], gaze-based targeting [58], foot-based movement [28, 57], among others.

Original: Researchers have also explored modelling error in Fitts’
Revised: Researchers have also explored modelling error in Fitts’

Original: Law [59], generalized Fitts’ Law to incorporate steering tasks [1,41], and leveraged the fundamental components of Fitts’ Law to design a host of pointing facilitation techniques (see [6] for a review).
Revised: Law [60], generalized Fitts’ Law to incorporate steering tasks [1,42], and leveraged the fundamental components of Fitts’ Law to design a host of pointing facilitation techniques (see [6] for a review).

Original: The goal of research into Fitts’ Law is to understand and improve throughput [62] in the use of interactive systems.
Revised: The goal of research into Fitts’ Law is to understand and improve throughput [63] in the use of interactive systems.

Original: If we characterize the temporal cost and error rate of individual interactions (e.g. se- lecting a target, manipulating that target, keyboarding, homing, etc.), then the overall temporal cost and error rate of a compound interac- tion is the sum of these basic interactions [12,18].
Revised: If we characterize the temporal cost and error rate of individual interactions (e.g. se- lecting a target, manipulating that target, keyboarding, homing, etc.), then the overall temporal cost and error rate of a compound interac- tion is the sum of these basic interactions [13,19].

Original: By improving the speed of target acquisition via new target acquisition techniques [6] and the speed of interactions via new interaction techniques [9], each of these subtasks becomes more efficient, increasing the over- all efficiency of the interaction.
Revised: By improving the speed of target acquisition via new target acquisition techniques [6] and the speed of interactions via new interaction techniques [10], each of these subtasks becomes more efficient, increasing the over- all efficiency of the interaction.

Original: Essentially, the assumption is that each individual interaction can be independently optimized.
Revised: Essentially, the assumption is that each individual interaction can be independently optimized.

Original: This is equally true in VR research: Bergstr¨om et al.
Revised: This is equally true in VR research: Bergstr¨om et al.

Original: [9], analyzing 20 years of VR research, note that studies with selection tasks measure the time a participant needs to select the next target (occasionally with additional measures of error and throughput for the selection task), while studies with manipulation tasks measure the time a participant manipulates virtual objects.
Revised: [10], analyzingyears of VR research, note that studies with selection tasks measure the time a participant needs to select the next target (occasionally with additional measures of error and throughput for the selection task), while studies with manipulation tasks measure the time a participant manipulates virtual objects.

Original: While, to the best of our knowledge, the independence of basic in- teractions has never been evaluated in VR, we have reason to believe that this assumption is questionable in traditional two-dimensional computer interfaces.
Revised: While, to the best of our knowledge, the independence of basic in- teractions has never been evaluated in VR, we have reason to believe that this assumption is questionable in traditional two-dimensional computer interfaces.

Original: Mandryk and Lough [37] examined how the intended use of a target impacts the time it takes to acquire the target.
Revised: Mandryk and Lough [38] examined how the intended use of a target impacts the time it takes to acquire the target.

Original: Mandryk and Lough note that, in real-world interfaces, the user acquires a target with a specific goal in mind.
Revised: Mandryk and Lough note that, in real-world interfaces, the user acquires a target with a specific goal in mind.

Original: Perhaps they wish to click the target (targeting).
Revised: Perhaps they wish to click the target (targeting).

Original: Perhaps the target activates a secondary set of widgets and they need to then click on a second target (i.e. dual targeting).
Revised: Perhaps the target activates a secondary set of widgets and they need to then click on a second target (i.e. dual targeting).

Original: Perhaps they wish to move the target in some way, e.g. to re-position it imprecisely (flicking or dragging) or to re-position it precisely at a new location (docking).
Revised: Perhaps they wish to move the target in some way, e.g. to re-position it imprecisely (flicking or dragging) or to re-position it precisely at a new location (docking).

Original: Mandryk and
Revised: Mandryk and

Original: Lough found that, if the intended use was targeting or dual targeting, participants acquired the target significantly faster than if the intended use was flicking or dragging.
Revised: Lough found that, if the intended use was targeting or dual targeting, participants acquired the target significantly faster than if the intended use was flicking or dragging.

Original: They also noted a difference in acceleration and deceleration during the selection of the target to be manipulated: if the participants intended to flick or dock the target, then the selection movement toward that target exhibited a higher peak speed and a longer deceleration phase.
Revised: They also noted a difference in acceleration and deceleration during the selection of the target to be manipulated: if the participants intended to flick or dock the target, then the selection movement toward that target exhibited a higher peak speed and a longer deceleration phase.

Original: Follow-on work by Ruiz and Lank [47] replicated these results and, via a more complete analysis of movement profiles, analyzed their potential impact on kinematics-linked endpoint prediction [33].
Revised: Follow-on work by Ruiz and Lank [48] replicated these results and, via a more complete analysis of movement profiles, analyzed their potential impact on kinematics-linked endpoint prediction [34].

Original: While Ruiz and Lank note that the impact on kinematics-linked modelling was likely not a concern, in both cases results implied that the potential benefits of new interaction techniques observed during manipulation may not be realized if they result in a corresponding increase in acquisition time for the target to be manipulated.
Revised: While Ruiz and Lank note that the impact on kinematics-linked modelling was likely not a concern, in both cases results implied that the potential benefits of new interaction techniques observed during manipulation may not be realized if they result in a corresponding increase in acquisition time for the target to be manipulated.

Original: There is also a significant possibility that virtual reality manipula- tions will differ from both real-world and two-dimensional interface manipulations.
Revised: There is also a significant possibility that virtual reality manipula- tions will differ from both real-world and two-dimensional interface manipulations.

Original: Considering real-world manipulations, the field of psychology has actively studied the act of reaching and grasping for many years [46].
Revised: Considering real-world manipulations, the field of psychology has actively studied the act of reaching and grasping for many years [47].

Original: Factors, including perception of the object to be grasped [22], the manipulations to be performed on the object [49], and tactile feedback during the act of grasping [29] impact both tra- jectories toward an object and the positioning and speed associated with the grasping of an object.
Revised: Factors, including perception of the object to be grasped [23], the manipulations to be performed on the object [50], and tactile feedback during the act of grasping [30] impact both tra- jectories toward an object and the positioning and speed associated with the grasping of an object.

Original: However, there exists a disconnect be-
Revised: However, there exists a disconnect be-

Original: tween real world affordances and perceived affordances [42] and an absence of the physiological interactions between hand and object, which may impact behaviour.
Revised: tween real world affordances and perceived affordances [43] and an absence of the physiological interactions between hand and object, which may impact behaviour.

Original: Furthermore, while it is always tempting to assume that previous results in 2D [37,47] can be directly applied to 3D, especially to VR, past research indicates that this may not be true.
Revised: Furthermore, while it is always tempting to assume that previous results in 2D [38,48] can be directly applied to 3D, especially to VR, past research indicates that this may not be true.

Original: As one example of this, Cockburn and Mckenzie [16] found that user performance deteriorated for a locate-and-point task when transforming from a 2D interface to a 3D interface.
Revised: As one example of this, Cockburn and Mckenzie [17] found that user performance deteriorated for a locate-and-point task when transforming from a 2D interface to a 3D interface.

Original: Further- more, movement planning, whether in real world or in computer interfaces, requires trajectory planning [33,46]; in immersive VR environments, depth has been found to greatly impact both perceived width and distance [5,14], a factor that may significantly alter the trajectory, kinematics and the impact of intended use.
Revised: Further- more, movement planning, whether in real world or in computer interfaces, requires trajectory planning [34,47]; in immersive VR environments, depth has been found to greatly impact both perceived width and distance [5,15], a factor that may significantly alter the trajectory, kinematics and the impact of intended use.

Original: 3 M ETHODOLOGY
Revised: 3 M ETHODOLOGY

Original: We conducted a controlled experiment to investigate the effect of intended use of a target on the time taken to acquire it in VR.
Revised: We conducted a controlled experiment to investigate the effect of intended use of a target on the time taken to acquire it in VR.

Original: We focused on interaction at arms’ length using a 1-to-1 mapping of physical controller movement to virtual controller movement, a direct 3D target acquisition technique common in VR [14,44,45].
Revised: We focused on interaction at arms’ length using a 1-to-1 mapping of physical controller movement to virtual controller movement, a direct 3D target acquisition technique common in VR [15,45,46].

Original: Figure 2 depicts the timeline of a trial in this experiment.
Revised: Figure 2 depicts the timeline of a trial in this experiment.

Original: [Figure]
Revised: [Figure]

Original: 3.1 Apparatus and Participants
Revised: 3.1 Apparatus and Participants

Original: The system was implemented in Unity 2020.3.7f1 with the Oculus Integration v29.0 and the study was conducted standalone on the
Revised: The system was implemented in Unity 2020.3.7f1 with the Oculus Integration v29.0 and the study was conducted standalone on the

Original: Oculus Quest 2 (tracking frequency is 72Hz) with Oculus left- and right-hand controllers.
Revised: Oculus Quest 2 (tracking frequency is 72Hz) with Oculus left- and right-hand controllers.

Original: In pilot studies we noted that the virtual controllers could occlude targets and their irregular shape caused problems of precision when selecting a target (because the exact selection location was unclear).
Revised: In pilot studies we noted that the virtual controllers could occlude targets and their irregular shape caused problems of precision when selecting a target (because the exact selection location was unclear).

Original: To address this, we used a smaller controller 3D model (from GEAR
Revised: To address this, we used a smaller controller 3D model (from GEAR

Original: VR), made the models translucent, and added a blue sphere ( cursor ) to the top side of the virtual controller indicating selection location, as shown in Fig.
Revised: VR), made the models translucent, and added a blue sphere ( cursor ) to the top side of the virtual controller indicating selection location, as shown in Fig.

Original: Although the physical and virtual controllers are not in the same shape, this design rarely affects how participants recognize them as participants only see virtual controllers rather than physical ones.
Revised: Although the physical and virtual controllers are not in the same shape, this design rarely affects how participants recognize them as participants only see virtual controllers rather than physical ones.

Original: In addition, participants focus on using cursor , rather than the model for interactions.
Revised: In addition, participants focus on using cursor , rather than the model for interactions.

Original: Making the model semi- transparent also does not impact the selection performance of a virtual hand [31,55].
Revised: Making the model semitransparent also does not impact the selection performance of a virtual hand [32,56].

Original: Besides, cursor is useful for target selection in VR [7] and our design presents better visual tracking on it.
Revised: Besides, cursor is useful for target selection in VR [7] and our design presents better visual tracking on it.

Original: We also noted that, as participants became fatigued, they would sometimes switch hands for a brief period, interacting with their non-dominant hand which impacted performance.
Revised: We also noted that, as participants became fatigued, they would sometimes switch hands for a brief period, interacting with their non-dominant hand which impacted performance.

Original: Therefore, along- side instructing participants to perform the study only with their dominant hand, we disabled the non-dominant hand controller at the outset of the study and invited participants to take breaks during the experiment if needed.
Revised: Therefore, alongside instructing participants to perform the study only with their dominant hand, we disabled the non-dominant hand controller at the outset of the study and invited participants to take breaks during the experiment if needed.

Original: A total of 15 participants, aged from 22 to 33 ( M = 26 .
Revised: A total of 15 participants, aged from 22 to 33 ( M = 26 .

Original: 1 , SD = 3 . 1, 5 identified as women, all right-handed) participated in the study.
Revised: 1 , SD = 3 . 1, 5 identified as women, all right-handed) participated in the study.

Original: 12 participants had experienced VR prior to the study; those participants primarily used VR for entertainment activities, such as playing games.
Revised: 12 participants had experienced VR prior to the study; those participants primarily used VR for entertainment activities, such as playing games.

Original: The experiment took approximately 45 minutes and participants received $15 for their participation.
Revised: The experiment took approximately 45 minutes and participants received $15 for their participation.

Original: 3.2 Interaction for Target Acquisition
Revised: 3.2 Interaction for Target Acquisition

Original: Fig.
Revised: Fig.

Original: 3 illustrates the visual interaction of the target acquisition.
Revised: 3 illustrates the visual interaction of the target acquisition.

Original: To acquire a target, participants place the cursor (1cm width) partially inside a target and press the selection button (in our implementation, A on the right controller) to select that target.
Revised: To acquire a target, participants place the cursor (1cm width) partially inside a target and press the selection button (in our implementation, A on the right controller) to select that target.

Original: When any part of the cursor enters a target, the target is highlighted in translucent blue, indicating that it is now selectable.
Revised: When any part of the cursor enters a target, the target is highlighted in translucent blue, indicating that it is now selectable.

Original: A successful selection turns the cursor green.
Revised: A successful selection turns the cursor green.

Original: If instead the button is pressed while the cursor is outside the target, the cursor turns red and an alert sound is played to inform participants of the erroneous selection.
Revised: If instead the button is pressed while the cursor is outside the target, the cursor turns red and an alert sound is played to inform participants of the erroneous selection.

Original: Participants are expected to correctly acquire each target, and they can proceed after a correct selection.
Revised: Participants are expected to correctly acquire each target, and they can proceed after a correct selection.

Original: Given that the overall goal of this experiment was to measure how the intended use of a target impacts the time taken to acquire that target, the overall interaction requires acquisition of a target followed by a manipulation of that target, i.e. an intended use or
Revised: Given that the overall goal of this experiment was to measure how the intended use of a target impacts the time taken to acquire that target, the overall interaction requires acquisition of a target followed by a manipulation of that target, i.e. an intended use or

Original: Task .
Revised: Task .

Original: The interaction to start a Task proceeds as follows:
Revised: The interaction to start a Task proceeds as follows:

Original: A participant acquires an initial target, the Start Target .
Revised: A participant acquires an initial target, the Start Target .

Original: To ensure that all targets in different tasks are noticeable and reachable within the field of view, the Start Target is positioned 35cm in front of and 20cm below the head.
Revised: To ensure that all targets in different tasks are noticeable and reachable within the field of view, the Start Target is positioned 35cm in front of and 20cm below the head.

Original: When the participant correctly selects the Start Target , the Start Target vanishes and a Primary Target is displayed immediately.
Revised: When the participant correctly selects the Start Target , the Start Target vanishes and a Primary Target is displayed immediately.

Original: He/She is asked to acquire and manipulate this Primary Target .
Revised: He/ is asked to acquire and manipulate this Primary Target .

Original: Considering that the arms of healthy adults typically reach at least 60cm from the torso [5, 14, 30, 32, 43], targets are placed within 60cm of participants in a region surrounding the location of the Start Target.
Revised: Considering that the arms of healthy adults typically reach at least 60cm from the torso [5, 15, 31, 33, 44], targets are placed within 60cm of participants in a region surrounding the location of the Start Target

Original: Participants move the cursor from the Start Target to the Pri- mary Target and perform a Task on the Primary Target .
Revised: Participants move the cursor from the Start Target to the Pri- mary Target and perform a Task on the Primary Target .

Original: By default, the Start Target ’s position remains the same in the vir- tual world for all tasks and targets are anchored relatively to the
Revised: By default, the Start Target ’s position remains the same in the vir- tual world for all tasks and targets are anchored relatively to the

Original: Start Target ’s position, which do not follow participants’ movement.
Revised: Start Target ’s position, which do not follow participants’ movement.

Original: However, as participants could adjust their position during the experiment, we allow them to re-calibrate the Start Target ’s position when they notice a shift in their position, before starting any new
Revised: However, as participants could adjust their position during the experiment, we allow them to re-calibrate the Start Target ’s position when they notice a shift in their position, before starting any new

Original: Task .
Revised: Task .

Original: In practice, the re-calibration was seldom used.
Revised: In practice, the re-calibration was seldom used.

Original: 3.3 Independent Variables
Revised: 3.3 Independent Variables

Original: Independent variables (IVs) in this study include the Index of Dif- ficulty ( ID ) of the Primary Target , Primary Direction , Secondary
Revised: Independent variables (IVs) in this study include the Index of Dif- ficulty ( ID ) of the Primary Target , Primary Direction , Secondary

Original: Direction and Task .
Revised: Direction and Task .

Original: ID of the acquisition action is computed using [35] for the Ampli- tude
Revised: ID of the acquisition action is computed using MacKenzie’s for-

Original:  A (distance between the Start Target and the Primary Target ) of values 9cm, 12 . 5cm, or 16cm, and the Width W of the Primary Target of values 3cm or 5cm).
Revised: mula [36] for the Amplitude A (distance between the Start Target and the Primary Target ) of values 9cm, 12 . 5cm, or 16cm, and the Width W of the Primary Target of values 3cm or 5cm).

Original: This yields six different IDs (1.49, 1.81, 2.00, 2.07, 2.37, and 2.66 bits).
Revised: This yields six different IDs (1.49, 1.81, 2.00, 2.07, 2.37, and 2.66 bits).

Original: Primary Direction D and Secondary Direction D ′ (Up, Down,
Revised: Primary Direction D and Secondary Direction D ′ (Up, Down,

Original: Forward, Backward, Left or Right) represent the direction of move- ment from the Start Target to the Primary Target and the Primary
Revised: Forward, Backward, Left or Right) represent the direction of move- ment from the Start Target to the Primary Target and the Primary

Original: Target to the Secondary Target respectively.
Revised: Target to the Secondary Target respectively.

Original: The rationale for these values is as follows.
Revised: The rationale for these values is as follows.

Original: The Primary Target can be located in six basic directions from the Start Target , i.e. Up,
Revised: The Primary Target can be located in six basic directions from the Start Target , i.e. Up,

Original: Down, Forward, Backward, Left and Right.
Revised: Down, Forward, Backward, Left and Right.

Original: Given the position of the Start Target , six distinct directions, and the possible existence of sequential manipulation after acquiring the Primary Target , the maximum values of target amplitude and target width are set to 20cm and 10cm respectively to avoid a target appearing outside of the field of view or outside of the reachable workspace.
Revised: Given the position of the Start Target , six distinct directions, and the possible existence of sequential manipulation after acquiring the Primary Target , the maximum values of target amplitude and target width are set to 20cm and 10cm respectively to avoid a target appearing outside of the field of view or outside of the reachable workspace.

Original: These constraints motivate the above values for independent variables.
Revised: These constraints motivate the above values for independent variables.

Original: With this configuration, participants observe targets that are close to them from a top view rather than a straight horizontal view, which helps to reduce the depth impact on the perceived width and addresses the occlusion issue between Primary Target and Secondary Target .
Revised: With this configuration, participants observe targets that are close to them from a top view rather than a straight horizontal view, which helps to reduce the depth impact on the perceived width and addresses the occlusion issue between Primary Target and Secondary Target .

Original: 3.3.1 Task
Revised: 3.3.1 Task

Original: The Tasks that participants were asked to complete were one of five manipulations of the primary target: T ARGETING , D UAL T ARGETING , D OCKING , T HROWING , or R EORIENTING (illustrated in Fig. 5).
Revised: The Tasks that participants were asked to complete were one of five manipulations of the primary target: T ARGETING , D UAL T ARGETING , D OCKING , T HROWING , or R EORIENTING (illustrated in Fig. 5).

Original: Detailed description of individual tasks follows.
Revised: Detailed description of individual tasks follows.

Original: T ARGETING :
Revised: T ARGETING :

Original: Correctly selecting the Start Target of width W reveals a white sphere ( Primary Target ) of width W located at the amplitude
Revised: Correctly selecting the Start Target of width W reveals a white sphere ( Primary Target ) of width W located at the amplitude

Original: A in direction D from the position of Start Target .
Revised: A in direction D from the position of Start Target .

Original: Par- ticipants simply have to acquire the Primary Target by moving the controller to the target and selecting it.
Revised: Par- ticipants simply have to acquire the Primary Target by moving the controller to the target and selecting it.

Original: D UAL T ARGETING :
Revised: D UAL T ARGETING :

Original: Correctly selecting the Start Target of width W reveals a white sphere (the Primary Target ) and a red sphere (the Secondary Target )
Revised: Correctly selecting the Start Target of width W reveals a white sphere (the Primary Target ) and a red sphere (the Secondary Target )

Original: both of width W. The Primary Target is located in direction D with amplitude
Revised: both of width W. The Primary Target is located in direction D with amplitude

Original: A from Start Target while Secondary Target is located in another direction D ′
Revised: A from Start Target while Secondary Target is located in another direction D ′

Original: (different from previous and next trials) with amplitude 9cm from the Primary Target (a distance that guarantees that both targets still remain within arms’ reach for the participant, see Sect. 3.3).
Revised: (different from previous and next trials) with amplitude 9cm from the Primary Target (a distance that guarantees that both targets still remain within arms’ reach for the participant, see Sect. 3.3).

Original: Participants must select these two spheres sequentially: first the Primary Target , and then the Secondary Target .
Revised: Participants must select these two spheres sequentially: first the Primary Target , and then the Secondary Target .

Original: D OCKING : Correctly selecting the Start Target of width W re- veals a white sphere (the Primary Target ) of width W and amplitude A in direction D, and a semi-transparent sphere (the Secondary Tar- get ) 1.5 times larger than the Primary Target and located 9cm away from the Primary Target in direction D ′ .
Revised: D OCKING : Correctly selecting the Start Target of width W re- veals a white sphere (the Primary Target ) of width W and amplitude A in direction D, and a semi-transparent sphere (the Secondary Tar- get ) 1.5 times larger than the Primary Target and located 9cm away from the Primary Target in direction D ′ .

Original: The width of the Secondary
Revised: The width of the Secondary

Original: Target reduces the required precision of the task, allowing partici- pants to finish this task more easily.
Revised: Target reduces the required precision of the task, allowing partici- pants to finish this task more easily.

Original: Participants are instructed to drag-and-release the Primary Target into Secondary Target .
Revised: Participants are instructed to drag-and-release the Primary Target into Secondary Target .

Original: T HROWING :
Revised: T HROWING :

Original: Correctly selecting the Start Target of width W reveals a white sphere (the Primary Target ) of width W located at a position direction D from Start Target and at a distance of amplitude A, and a semi-transparent green wall (the Secondary
Revised: Correctly selecting the Start Target of width W reveals a white sphere (the Primary Target ) of width W located at a position direction D from Start Target and at a distance of amplitude A, and a semi-transparent green wall (the Secondary

Original: Target ) located 9cm away from the Primary Target in direction D ′ .
Revised: Target ) located 9cm away from the Primary Target in direction D ′ .

Original: To reduce task difficulty (wall size being too small) and avoid visual distraction (wall size being too big), the size of this wall is set to 40cm × 40cm × 1cm which is over 8 times larger than the size of Primary Target in width.
Revised: To reduce task difficulty (wall size being too small) and avoid visual distraction (wall size being too big), the size of this wall is set to 40cm × 40cm × 1cm which is over 8 times larger than the size of Primary Target in width.

Original: Participants are instructed to “throw” the Primary Target towards the Secondary Target by releasing the pressed button.
Revised: Participants are instructed to “throw” the Primary Target towards the Secondary Target by releasing the pressed button.

Original: The released Primary Target then moves in the throwing direction.
Revised: The released Primary Target then moves in the throwing direction.

Original:  :Correctly selecting the
Revised: R EORIENTING :

Revised: Correctly selecting the Start Target of width W reveals a white object (the Primary Target ) and a red object (the Secondary Target ) whose bodies are both spheres of width W. Different shapes (i.e. a capsule, a cylinder and a cube) are placed into both Primary Target and Secondary Target to indicate the orientation, which only serve as visual references and are not selectable.

Original:  Target of width W reveals a and
Revised: The Primary Target is located at amplitude

Original:  Secondary bodies spheres
Revised: A from Start Target in direction D and Secondary Target 9cm away from Primary Target in direction D ′ . The Secondary Target has an orientation along its roll axis with a random angle in the range (- π 2 to π 2 ), values empirically obtained from pilot studies to reduce clutching and task difficulty. To move to the next trial, participants were asked to rotate the Primary Target such that the Primary Target has a similar orientation to the Secondary Target , that is when the angle difference in each axis is smaller than π 12 .

Original: W placed into to only serve as references and Except T ARGETING , all other tasks require a manipulation sub- task on either the Primary Target or the Secondary Target .
Revised: The colour of Secondary Target changes to green to inform participants of the correct orientation of the Primary Target . Except T ARGETING , all other tasks require a manipulation sub- task on either the Primary Target or the Secondary Target .

Original: For
Revised: For

Original: D UAL T ARGETING , a second selection is required on the Secondary Target and other tasks require a ”press and hold” behaviour on the
Revised: D UAL T ARGETING , a second selection is required on the Secondary Target and other tasks require a ”press and hold” behaviour on the

Original: Primary Target for object manipulation.
Revised: Primary Target for object manipulation.

Original: While behaviours in the manipulation sub-task vary across Task , the acquisition time of the Primary Target (delimited by the controller ”button down” action on the primary target) is consistent across all tasks.
Revised: While behaviours in the manipulation sub-task vary across Task , the acquisition time of the Primary Target (delimited by the controller ”button down” action on the primary target) is consistent across all tasks.

Original: 3.4 Procedure
Revised: 3.4 Procedure

Original: Participants were recruited from our local university.
Revised: Participants were recruited from our local university.

Original: To preserve social distancing requirements, the study was conducted remotely, and both the VR headset and controllers were sanitized before being delivered to participants.
Revised: To preserve social distancing requirements, the study was conducted remotely, and both the VR headset and controllers were sanitized before being delivered to participants.

Original: Before written consent was obtained, participants were asked to read an information letter in which they were warned about potential  motion sickness as a result of wearing the head-mounted display device and that they could stop the experiment at any time without penalty if they felt uncomfortable or simply did not wish to con- tinue.
Revised: Before written consent was obtained, participants were asked to read an information letter in which they were warned about potential [Figure] motion sickness as a result of wearing the head-mounted display device and that they could stop the experiment at any time without penalty if they felt uncomfortable or simply did not wish to con- tinue.

Original: They were then asked to watch an instructional video, answer a demographics questionnaire (asking for their gender [52], age, handedness, and VR experience).
Revised: They were then asked to watch an instructional video, answer a demographics questionnaire (asking for their gender [53], age, handedness, and VR experience).

Original: They were then asked to sign the informed consent.
Revised: They were then asked to sign the informed consent.

Original: Participants started the experimental software by connecting with one of the researchers via video conferencing tools.
Revised: Participants started the experimental software by connecting with one of the researchers via video conferencing tools.

Original: The researcher verified informed consent, walked participants through disabling the non-dominant hand controller, and then guided the participant to start the experiment.
Revised: The researcher verified informed consent, walked participants through disabling the non-dominant hand controller, and then guided the participant to start the experiment.

Original: In addition to researchers’ verbal guidance, participants could also follow the instructions text in the system during the experiment, which presented detailed steps to guide them to control the system and finish each task.
Revised: In addition to researchers’ verbal guidance, participants could also follow the instructions text in the system during the experiment, which presented detailed steps to guide them to control the system and finish each task.

Original: As noted above, to evaluate the impact of intended use on target acquisition in VR, we represented different intended uses as a Task[Figure] for the participant.
Revised: As noted above, to evaluate the impact of intended use on target acquisition in VR, we represented different intended uses as a Task for the participant.

Original: The experiment consisted of repeated blocks of trials where participants had to select a target and complete a given
Revised: The experiment consisted of repeated blocks of trials where participants had to select a target and complete a given

Original: Task on that target using the controller in their dominant hand.
Revised: Task on that target using the controller in their dominant hand.

Original: Each trial, therefore, consisted of the following steps.
Revised: Each trial, therefore, consisted of the following steps.

Original: First, the participant had to acquire the Start Target to calibrate a starting position.
Revised: First, the participant had to acquire the Start Target to calibrate a starting position.

Original: Correctly selecting this target would hide it, start a count- down timer, and reveal a Primary Target that the participant was asked to acquire and manipulate to fulfill the Task (Section 3.3.1) for the given condition.
Revised: Correctly selecting this target would hide it, start a countdown timer, and reveal a Primary Target that the participant was asked to acquire and manipulate to fulfill the Task (Section 3.3.1) for the given condition.

Original: A trial ended once the Task was completed successfully or if the countdown timer exceeded 15 seconds.
Revised: A trial ended once the Task was completed successfully or if the countdown timer exceeded 15 seconds.

Original: The countdown timer was implemented to avoid excessive trial completion times, i.e., to limit study duration.
Revised: The countdown timer was implemented to avoid excessive trial comple- tion times, i.e., to limit study duration.

Original: The experimental system then moved to the next trial and revealed the Start Target again (see Fig. 4 & Fig. 2).
Revised: The experimental system then moved to the next trial and revealed the Start Target again (see Fig. 4 & Fig. 2).

Original: Participants were instructed to complete the trials as quickly and accurately as possible while keeping an error rate below 5% (error rate was displayed to the participant at the end of each block).
Revised: Participants were instructed to complete the trials as quickly and accurately as possible while keeping an error rate below 5% (error rate was displayed to the participant at the end of each block).

Original: In case of an error, the trial was appended to the end of the current block, and participants needed to complete all trials within a block successfully before moving on to the next block, thus ensuring the same number of correct trials for all conditions.
Revised: In case of an error, the trial was appended to the end of the current block, and participants needed to complete all trials within a block successfully before moving on to the next block, thus ensuring the same number of correct trials for all conditions.

Original: While participants were not required to repeat a block if the error rate exceeded 5%, controlling the error rate is a common practice to balance speed and accuracy in pointing experiments [7].
Revised: While participants were not required to repeat a block if the error rate exceeded 5%, controlling the error rate is a common practice to balance speed and accuracy in pointing experiments [7].

Original: Participants were allowed to take a break without a time limit between each block and after completing each Task .
Revised: Participants were allowed to take a break without a time limit between each block and after completing each Task .

Original: 3.5 Data Collection
Revised: 3.5 Data Collection

Original: All participant movement data was logged as a sequence of time- stamped, three-dimensional coordinates.
Revised: All participant movement data was logged as a sequence of time- stamped, three-dimensional coordinates.

Original: The system also logged selection actions, whether the selection was an error and subsequent task manipulations.
Revised: The system also logged selection actions, whether the selection was an error and subsequent task manipulations.

Original: Recallthatthegoal of this experiment is to measure the effect of different Task s (intended uses of the Primary Target ) on the time taken to select the Primary Target .
Revised: Recallthatthegoal of this experiment is to measure the effect of different Task s (intended uses of the Primary Target ) on the time taken to select the Primary Target .

Original: The dependent variables collected and logged by the system were selection time ( T sel ) and errors.
Revised: The dependent variables collected and logged by the system were selection time ( T sel ) and errors.

Original: For all trials, we use button press down events on the Start
Revised: For all trials, we use button press down events on the Start

Original: Target and Primary Target to determine the beginning and end of the Primary Target ’s selection movement.
Revised: Target and Primary Target to determine the beginning and end of the Primary Target ’s selection movement.

Original: The selection time was the time interval from the button press action of the Start Target to the button press action on the Primary Target .
Revised: The selection time was the time interval from the button press action of the Start Target to the button press action on the Primary Target .

Original: The length of time to complete the Task was logged, but is immaterial to this experiment as we are only interested in the impact of Task on Primary Target selection time.
Revised: The length of time to complete the Task was logged, but is immaterial to this experiment as we are only interested in the impact of Task on Primary Target selection time.

Original: As noted above, the system also identified errors.
Revised: As noted above, the system also identified errors.

Original: We consider errors that occurred only while acquiring the Primary Target .
Revised: We consider errors that occurred only while acquiring the Primary Target .

Original: Errors were classified as one of three error types:
Revised: Errors were classified as one of three error types:

Original: • enter error — when participants pressed the button outside the Primary Target before having entered it.
Revised: • enter error — when participants pressed the button outside the Primary Target before having entered it.

Original: • exit error — when participants pressed the button outside the Primary Target after having entered it at least once • pass error — when the countdown timer reached 0 before the Primary Target was correctly selected.
Revised: • exit error — when participants pressed the button outside the Primary Target after having entered it at least once • pass error — when the countdown timer reached 0 before the Primary Target was correctly selected.

Original: The Error rate, displayed to participants and in our analysis, refers to the percentage of erroneous trials in a block over the total number of trials in the block (including any repeated trials).
Revised: The Error rate, displayed to participants and in our analysis, refers to the percentage of erroneous trials in a block over the total number of trials in the block (including any repeated trials).

Original: For example, if a participant failed once on every trial within a block and then succeeded on the second attempt, the error rate would be 50%.
Revised: For example, if a participant failed once on every trial within a block and then succeeded on the second attempt, the error rate would be 50%.

Original: 3.6 Design Summary
Revised: 3.6 Design Summary

Original: We adopted a repeated-measures within-subjects design.
Revised: We adopted a repeated-measures within-subjects design.

Original: We effectively looked at three independent variables (IVs): Task (T ARGETING , D UAL T ARGETING , D OCKING , T HROWING and R EORIENTING ), ID (1.49, 1.81, 2.00, 2.07, 2.37, and 2.66 bits), and Block (1-4).
Revised: We effectively looked at three independent variables (IVs): Task (T ARGETING , D UAL T ARGETING , D OCKING , T HROWING and R EORIENTING ), ID (1.49, 1.81, 2.00, 2.07, 2.37, and 2.66 bits), and Block (1-4).

Original: The order of Task was counterbalanced across participants using a Latin square [58].
Revised: The order of Task was counterbalanced across participants using a Latin square [59].

Original: Note that for each ID , Primary Direction D and Secondary Direction D ′ were randomly ordered for generalization.
Revised: Note that for each ID , Primary Direction D and Secondary Direction D ′ were randomly ordered for generalization.

Original: The combination between D and ID and that of D and D ′ were not controlled in our experiment.
Revised: The combination between D and ID and that of D and D ′ were not controlled in our experiment.

Original: In summary, each participant completed 4 Block s × 6 ID s × 6 Primary Direction s × 5 (counterbalanced) Task s, i.e. 720 trials.
Revised: In summary, each participant completed 4 Block s × 6 ID s × 6 Primary Direction s × 5 (counterbalanced) Task s, i.e. 720 trials.

Original: This resulted in a data set containing 10 800 successful trials for our 15 participants.
Revised: This resulted in a data set containing 10 800 successful trials for our 15 participants.

Original: 4 R ESULTS
Revised: 4 R ESULTS

Original: We analyze our results in terms of error rate, Primary Target se-
Revised: We analyze our results in terms of error rate, Primary Target se-

Original: lection time ( T sel ), motion kinematics and Fitts’ Law modelling.
Revised: lection time ( T sel ), motion kinematics and Fitts’ Law modelling.

Original: Note that our focus is on T sel , the time taken to move from the Start Target to the Primary Target given that our interest is on how Task s performed on the Primary Target impact the time to select it, T sel .
Revised: Note that our focus is on T sel , the time taken to move from the Start Target to the Primary Target given that our interest is on how Task s performed on the Primary Target impact the time to select it, T sel .

Original: 4.1 Error Rate
Revised: 4.1 Error Rate

Original:  Recall that, in order to complete a block, participants were required to successfully complete each trial.
Revised: [Figure] Recall that, in order to complete a block, participants were re- quired to successfully complete each trial.

Original: In the case of an error in selecting the Primary Target , participants would need to repeat the[Figure] corresponding trial at the end of the Block.
Revised: In the case of an error in selecting the Primary Target , participants would need to repeat the corresponding trial at the end of the Block.

Original: As a result, alongside the 10,800 correct trials, we collected an additional 289 erroneous trials, for a total of 11,089 trials.
Revised: As a result, alongside the 10,800 correct trials, we collected an additional 289 erroneous trials, for a total of 11,089 trials.

Original: Among 289 trials, only 3 trials had pass error and participants did not report the time pressure during the study, implying that the timer did not push participants.
Revised: Among 289 trials, only 3 trials had pass error and participants did not report the time pressure during the study, implying that the timer did not push participants.

Original: Given the non-normal distribution of error rate, a Friedman test was conducted for three independent variables (IVs): Task ,
Revised: Given the non-normal distribution of error rate, a Friedman test was conducted for three independent variables (IVs): Task ,

Original: Block , and ID .
Revised: Block , and ID .

Original: We found a significant effect of Block on error rate ( χ 2 Block ( 3 ) =15.46, p < 0.005 ).
Revised: We found a significant effect of Block on error rate ( χ 2 Block ( 3 ) =15.46, p < 0.005 ).

Original: However, pairwise Wilcoxon rank sum tests with Bonferroni corrections did not show significant differences between blocks.
Revised: However, pairwise Wilcoxon rank sum tests with Bonferroni corrections did not show significant differences between blocks.

Original: With all blocks, error rate ( M = 2.20% , SD = 5.77 ) was below the 5% error rate threshold that we recommended our participants not to exceed.
Revised: With all blocks, error rate ( M = 2.20% , SD = 5.77 ) was below the 5% error rate threshold that we recommended our participants not to exceed.

Original: Error rate of each Task was shown in Fig.
Revised: Error rate of each Task was shown in Fig.

Original: The Friedman test did not reveal a significant effect of Task on error rate, but showed a significant effect of ID ( χ 2 Block ( 3 ) =12.92, p < 0.05 ).
Revised: The Friedman test did not reveal a significant effect of Task on error rate, but showed a significant effect of ID ( χ 2 Block ( 3 ) =12.92, p < 0.05 ).

Original: How- ever, pairwise comparisons did not reveal any significant differences across ID s.
Revised: How- ever, pairwise comparisons did not reveal any significant differences across ID s.

Original: 4.2 Selection Time
Revised: 4.2 Selection Time

Original: [Figure]
Revised: [Figure]

Revised: l

Revised: [Figure]

Revised: t

Revised: i

Revised: [Figure]

Original:  We aggregated non-erroneous trials and removed outliers by elim- inating any trial whose selection time was more than three standard deviations from the mean, leaving 10,645 trials for analysis.
Revised: T i [Figure] ( s ) We aggregated non-erroneous trials and removed outliers by elim- inating any trial whose selection time was more than three standard deviations from the mean, leaving 10,645 trials for analysis.

Original: Given the non-normal distribution of the data, a Box-Cox trans- formation [10] was applied to selection time.
Revised: Given the non-normal distribution of the data, a Box-Cox transformation [11] was applied to selection time.

Original: When sphericity was violated using Mauchly’s test, Greenhouse-Geisser correction to the DoFs was applied.
Revised: When sphericity was violated using Mauchly’s test, Greenhouse-Geisser correction to the DoFs was applied.

Original: When significant effects were found, pairwise t-tests with Bonferroni corrections were conducted for post-hoc analysis.
Revised: When significant effects were found, pair- wise t-tests with Bonferroni corrections were conducted for post-hoc analysis.

Original: Effect sizes were reported as partial eta squared ( η 2 p ).
Revised: Effect sizes were reported as partial eta squared ( η 2 p ).

Original: We first conducted a two-way RM-ANOVA ( α =0.05) for selec- tion time on Block and Task to test for a possible learning effect.
Revised: We first conducted a two-way RM-ANOVA ( α =0.05) for selection time on Block and Task to test for a possible learning effect.

Original: We found a significant effect of Block ( F 3 , 42 =41.64, p < 0.001, η 2 p =0.75 ).
Revised: We found a significant effect of Block ( F 3 , 42 =41.64, p < 0.001, η 2 p =0.75 ).

Original: Pair- wise comparisons revealed significant differences between Block( M = 1 . 03 s ) and the other three blocks ( p < 0.001, 2: 0 .
Revised: Pairwise comparisons revealed significant differences between Block 1 ( M = 1 . 03 s ) and the other three blocks ( p < 0.001, 2: 0 .

Original: 97 s , 3: 0 .
Revised: 97 s , 3: 0 .

Original: 94 s & 4: 0 .
Revised: 94 s & 4: 0 .

Original: 93 s ).
Revised: 93 s ).

Original: We found a significant effect of Task ( F [Figure] , 56 =33.11, p < 0.001, η 2 p =0.70 ) but we did not find a significant interaction effect between Block and
Revised: We found a significant effect of Task ( F 4 , 56 =33.11, p < 0.001, η 2 p =0.70 ) but we did not find a significant interaction effect between Block and

Original: Task .
Revised: Task .

Original: These results suggested a potential learning effect that did not differ between tasks; we thus removed the first block in our remaining analysis.
Revised: These results suggested a potential learning effect that did not differ between tasks; we thus removed the first block in our remaining analysis.

Original: After removal of the first Block ,we found a significant effect of Task ( F 4 , 56 =29.11, p < 0.001, η 2 p =0.68 ) on T sel .
Revised: After removal of the first Block ,we found a significant effect of Task ( F 4 , 56 =29.11, p < 0.001, η 2 p =0.68 ) on T sel .

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 7, pair- wise comparisons showed that T ARGETING ( 0 . 85 s ) was significantly faster than the other four tasks: p < 0.001 for all, D UAL T ARGETING ( 0 .
Revised: 7, pairwise comparisons showed that T ARGETING ( 0 . 85 s ) was significantly faster than the other four tasks: p < 0.001 for all, D UAL T ARGETING ( 0 .

Original: 91 s ), D OCKING ( 0 . 93 s ), T HROWING ( 0 .
Revised: 91 s , 7 . 1% faster), D OCKING ( 0 . 93 s , 9 . 4% faster), T HROWING ( 0 .

Original: 93 s ) and R EORIENTING ( 1 .
Revised: 93 s , 9 . 4% faster) and R EORIENTING ( 1 .

Original: 12 s ).
Revised: 12 s , 31 .

Original:  Moreover, R EORIENTING was found significantly slower than other tasks: p < 0.001 for all.
Revised: 8% faster). Moreover, R EORIENTING was found significantly slower than other tasks: p < 0.001 for all.

Original: We found a significant effect of ID ( F 2 . 1 , 29 . 7 =254.54, p < 0.001, η 2 p =0.95 ).
Revised: We found a significant effect of ID ( F 2 . 1 , 29 . 7 =254.54, p < 0.001, η 2 p =0.95 ).

Original: Pairwise comparisons showed significant differences ( p < 0.01 ) between each ID except for 1.81& bits and 2.07&2.37 bits.
Revised: Pairwise comparisons showed significant differences ( p < 0.01 ) be- tween each ID except for 1.81&2 bits and 2.07&2.37 bits.

Original: We also found a significant interaction effect between Task and ID ( F 20 , 280 =3.00, p < 0.001, η 2 p =0.18 ).
Revised: We also found a significant interaction effect between Task and ID ( F 20 , 280 =3.00, p < 0.001, η 2 p =0.18 ).

Original: Pairwise comparisons showed that there were no significant differences between 1.49&1.81 bits for all tasks, 1.49&2 for D OCKING , D UAL T ARGETING and R EORIENTING , and 2&2.07 for R EORIENTING , but that all other levels of ID were significantly different for all tasks ( p < 0.05 ).
Revised: R EORIENTING was significantly slower than other tasks for all IDs ( p < 0.05 ), except T HROWING at 2.66 bits ( p = 0.08 ). T ARGETING was only significantly faster than D UAL T ARGETING and R EORIENTING at 1.49 bits, and T HROWING at 2.37 bits ( p < 0.01 ), as shown in Fig. 8. There were no significant differences among other conditions. [Figure]

Original: 4.3 Motion Kinematics
Revised: 4.3 Motion Kinematics

Original: In order to try to better understand where the difference for T sel between Tasks comes from, we analyzed the motion kinematics profile of participants’ correct target selections.
Revised: In order to try to better understand where the difference for T sel between Tasks comes from, we analyzed the motion kinematics profile of participants’ correct target selections.

Original: We once again kept all non-erroneous trials and removed the first block due to the aforementioned possible learning effect.
Revised: We once again kept all non-erroneous trials and removed the first block due to the aforementioned possible learning effect.

Original: We computed velocity profiles based on the cursor’s 3D posi- tion and corresponding timestamps.
Revised: We computed velocity profiles based on the cursor’s 3D position and corresponding timestamps.

Original: A time interval of each trial was normalized.
Revised: A time interval of each trial was normalized.

Original: Then, the corresponding velocities and normalized distances from the cursor to the Primary Target were interpolated to create a time-equidistant profile (every 2% of trial time).
Revised: Then, the corresponding velocities and normalized distances from the cursor to the Primary Target were interpolated to create a time-equidistant profile (every 2% of trial time).

Original: Next, for each task and participant, these values were aggregated by computing the average for each normalized time interval.
Revised: Next, for each task and participant, these values were aggregated by computing the average for each normalized time interval.

Original: These were subsequently averaged over all participants to produce a single normalized profile for each task (see Fig. 9).
Revised: These were subsequently averaged over all participants to produce a single normalized profile for each task (see Fig. 10).

Original: In order to compare our results to [37], we also used these profiles to compute the following motion kinematics measures: peak velocity (pkV), time to peak velocity (t2pkV), percent of time after peak velocity (afterpkV%), and selection velocity (sV: the velocity at the end of the T sel ) at Primary Target for each trial (see Fig. 
Revised: In order to compare our results to Mandryk & Lough’s work [38], we also used these profiles to compute the following motion kinematics measures: peak velocity (pkV), time to peak velocity (t2pkV), percent of time after peak velocity (afterpkV%), and selection velocity (sV: the velocity at the end of the T sel ) at Primary Target for each trial (see Fig. 9).

Original: For each metric, we removed outliers if the observed value was more than three standard deviations from the mean.
Revised: For each metric, we removed outliers if the observed value was more than three standard deviations from the mean.

Original: Given the nonnormal distribution of dependent variables, we conducted an Aligned Rank Transform [60] for pkV, t2pkV, afterpkV% and sV on two IVs:
Revised: Given the nonnormal distribution of dependent variables, we conducted an Aligned Rank Transform [61] for pkV, t2pkV, afterpkV% and sV on two IVs:

Original: Task and ID .
Revised: Task and ID .

Original: Contrasts ART [19] with Bonferroni corrections was applied as the post-hoc analysis if a significant effect was found and effect sizes were reported as partial eta squared ( η 2 p ).
Revised: Contrasts ART [20] with Bonferroni corrections was applied as the post-hoc analysis if a significant effect was found and effect sizes were reported as partial eta squared ( η 2 p ).

Original: Peak Velocity (Fig. 8 (A)):
Revised: Peak Velocity (Fig. 9 (A)):

Original: We found a significant effect of Task on pkV ( F 4 , 1306 =76.67, p < 0.001, η 2 p =0.19 ).
Revised: We found a significant effect of Task on pkV ( F 4 , 1306 =76.67, p < 0.001, η 2 p =0.19 ).

Original: Contrasts ART showed significant differences of pkV ( p < 0.001 for all) between each Task except between D UAL T ARGETING ( M = 51 .
Revised: Contrasts ART showed significant differences of pkV ( p < 0.001 for all) between each Task except between D UAL T ARGETING ( M = 51 .

Original: 52cm / s ) and D OCKING ( 51 .
Revised: 52cm / s ) and D OCKING ( 51 .

Original: 52cm / s ).
Revised: 52cm / s ).

Revised: R EORIENTING ( 49 .

Revised: 1cm / s ) was 8 .

Revised: 3% slower than T ARGETING ( 53 . 2cm / s ), 4 .

Revised: 8% slower than D UAL T ARGETING and D OCKING respectively, and 13 .

Revised: 6% slower than T HROWING ( 55 .

Original:  We found a significant effect of ID on pkV ( F 5 , 1306 =
Revised: 8cm / s ). We found a significant effect of ID on pkV ( F 5 , 1306 =

Original: 608.60, p < 0.001, η 2 p =0.70 ).
Revised: 608.60, p < 0.001, η 2 p =0.70 ).

Original: Pairwise comparisons revealed significant differ- ences ( p < 0 .
Revised: Pair- wise comparisons revealed significant differences ( p < 0 .

Original: 001 ) between pairs of ID except for 1.49&2 , 1.81&2.37 and 2.07&2.66 bits.
Revised: 001 ) between pairs of ID except for 1.49&2.00 , 1.81&2.37 and 2.07&2.66 bits.

Original: We also found an interaction effect between Task and ID ( F 20 , 1306 =2.14, p < 0.005, η 2 p =0.03 ).
Revised: We also found an interaction effect between Task and ID ( F 20 , 1306 =2.14, p < 0.005, η 2 p =0.03 ).

Original: Contrasts ART also showed that there were no significant differences between [Table] [Table] [Table]
Revised: Contrasts ART also showed that there were no significant differences between 1.49&2.00 , 1.81&2.37 and 2.07&2.66 bits for each task.

Original: [Table]

Original: and 2.07&

Original: [Table]

Original: 66 bits for each task.

Original: In other words, no significant differences on pkV were revealed when target amplitudes were the same.
Revised: In other words, no significant differences on pkV were revealed when target amplitudes were the same.

Original: Time to Peak Velocity (Fig. 8 (B)): We found a significant effect of Task on t2pkV ( F 4 , 1306 =
Revised: Time to Peak Velocity (Fig. 9 (B)): We found a significant effect of Task on t2pkV ( F 4 , 1306 =

Original: 43.31, p < 0.001, η 2 p =0.12 ).
Revised: 43.31, p < 0.001, η 2 p =0.12 ).

Original: Pairwise comparisons showed that T ARGETING ( 0 . 51s ) and D UAL T ARGETING ( 0 . 52s ) had significantly shorter t2pkV ( p < 0.001 for all) than the other three tasks.
Revised: Pairwise comparisons showed that T ARGETING ( 0 . 51s ) and D UAL T ARGETING ( 0 . 52s ) had significantly shorter t2pkV ( p < 0.001 for all) than the other three tasks.

Revised: More specifically, they reached the peak velocity at least 3 .

Revised: 8% earlier than D OCKING , T HROWING and R EORIENTING ( 0 . 54s for all).

Original: We found a significant effect of ID ( F 4 , 1306 =27.56, p < 0.001, η 2 p =0.10 ).
Revised: We found a significant effect of ID ( F 4 , 1306 = 27.56,p < 0.001, η 2 p =0.10 ).

Original: Pairwise comparisons showed significant differences ( p < 0 .
Revised: Pairwise comparisons showed significant differences ( p < 0 .

Original: 001 ) between each ID except for 1.49&2.00 , 1.81&2.37 , 2.07&2.37 and 2.37&2.66 bits.
Revised: 001 ) between each ID except for 1.49&2.00 , 1.81&2.37 , 2.07&2.37 and 2.37&2.66 bits.

Original: We did not find an interaction effect between Task and ID .
Revised: We did not find an interaction effect between Task and ID .

Original: Percent of Time after Peak Velocity (Fig. 8 (C)):
Revised: Percent of Time after Peak Velocity (Fig. 9 (C)):

Original: A significant effect of Task was revealed on afterpkV% ( F 4 , 1306 =
Revised: A significant effect of Task was revealed on afterpkV% ( F 4 , 1306 =

Original: 182.49, p < 0.001, η 2 p =0.36 ).
Revised: 182.49, p < 0.001, η 2 p =0.36 ).

Original: The post-hoc analysis showedthat T ARGETING ( 38 . 74% ) had significantly smaller afterpkV% ( p < 0.001 for all) than the other four tasks while R EORIENTING ( 49 .
Revised: The post-hoc analysis showed T ARGETING ( 38 . 7% ) had sig- nificantly smaller afterpkV% ( p < 0.001 for all) than the other four tasks while R EORIENTING ( 49 .

Original: 53% ) had significantly larger afterpkV% ( p < 0.001 for all).
Revised: 5% ) had significantly larger afterpkV% ( p < 0.001 for all).

Revised: Participants spent 28% more time after peak velocity in R EORIENTING compared to T ARGETING and at least 20% longer than other three tasks, suggesting a longer deceleration phrase in

Original:  We found a significant effect of ID on afterpkV% ( F 4 , 1306 =
Revised: R EORIENTING . We found a significant effect of ID on afterpkV% ( F 4 , 1306 =

Original: 200.95, p < 0.001, η 2 p =0.43 ).
Revised: 200.95, p < 0.001, η 2 p =0.43 ).

Original: Pairwise comparisons showed significant differences ( p < 0 .
Revised: Pairwise comparisons showed signifi- cant differences ( p < 0 .

Original: 001 ) between each ID except for 2.00&2.07 bits.
Revised: 001 ) between each ID except for 2.00&2.07 bits.

Original: We did not find an interaction effect between Task and ID .
Revised: We did not find an interaction effect between Task and ID .

Original: Selection Velocity (Fig. 8{+
+} (D)):
Revised: Selection Velocity (Fig. {+
+} (D)):

Original: We found a significant effect of Task on sV ( F 4 , 1306 =
Revised: We found a significant effect of Task on sV ( F 4 , 1306 =

Original: 123.43, p < 0.001, η 2 p =0.27 ) and the contrasts ART revealed that R EORIENTING had significantly slower sV ( 7 .
Revised: 123.43, p < 0.001, η 2 p =0.27 ) and the contrasts ART revealed that R EORIENTING had significantly slower sV ( 7 .

Original: 70cm / s, p < 0.001 for all) and T HROWING had significantly higher sV ( 11 .
Revised: 70cm / s, p < 0.001 for all) and T HROWING had significantly higher sV ( 11 .

Original: 87cm / s, p < 0.001 for all) than other tasks.
Revised: 87cm / s, p < 0.001 for all) than other tasks.

Revised: Compared to other tasks, sV for R EORIENTING was at least 25% slower and over 50% slower than T HROWING .

Original: D UAL T ARGETING ( 10 .
Revised: D UAL T ARGETING ( 10 .

Original: 59cm / s ) also had significantly higher sV than T ARGETING ( 9 . 64cm / s, p < 0.01 ).
Revised: 59cm / s ) also had significantly higher sV than T ARGETING ( 9 . 64cm / s,p < 0.01 , 9 . 9% higher).

Original: We found a significant effect of ID on sV ( F 5 , 1306 =95.62, p < 0.001, η 2 p =0.27 ).
Revised: We found a significant effect of ID on sV ( F 5 , 1306 =95.62, p < 0.001, η 2 p =0.27 ).

Original: Pairwise comparisons showed sig- nificant differences ( p < 0 . 01 ) between each ID except for 1.49&1.81 , 1.49&2.07 , 1.81&2.07 , 2&2.37 and 2.37&2.66 bits.
Revised: Pairwise comparisons showed significant differences ( p < 0 . 01 ) be- tween each ID except for 1.49&1.81 , 1.49&2.07 , 1.81&2.07 , 2.00&2.37 and 2.37&2.66 bits.

Original: We also found an in- teraction effect between Task and ID ( F 20 , 1306 =2.51, p < 0.001, η 2 p =0.04 ).
Revised: We also found an interaction effect between Task and ID ( F 20 , 1306 = 2.51, p < 0.001, η 2 p =0.04 ).

Original: Pairwise comparisons showed that there were no significant differences between 1.49&1.81 , 1.49&2.07 , 1.81&2.07 , 2.00&2.37 , 2.37&2.66 and 2.00&2.66 bits in each task.
Revised: Pairwise comparisons showed that there were no significant differences between 1.49&1.81 , 1.49&2.07 , 1.81&2.07 , 2.00&2.37 , 2.37&2.66 and 2.00&2.66 bits in each task.

Original: Interpreting these numbers in terms of tar- get width and amplitude, these results argue that sV is significantly impacted by target width. 
Revised: Inter- preting these numbers in terms of target width and amplitude, these results argue that sV is significantly impacted by target width. [Figure] [Figure]

Original: 4.4 Fitts’ Law
Revised: 4.4 Fitts’ Law

Original: Given our research question, i.e. whether Task users will perform on an object impacts the time taken to acquire the object, Fitts’ law modelling and throughput analysis were applied only to the selection of Primary Target , i.e., only to T sel in Figure 2 but not to the subsequent manipulation task.
Revised: Given our research question, i.e. whether Task users will perform on an object impacts the time taken to acquire the object, Fitts’ law modelling and throughput analysis were applied only to the selection of Primary Target , i [Figure] e., only to T sel in Figure 2 but not to the subsequent manipulation task.

Original: To perform our analysis, we computed the effective target width ( W e ) for each target width by multiplying the standard deviation by 4.133 [36,40] and used W e to calculate the effective ID, ID e accordingly.
Revised: To perform our analysis, we computed the effective target width ( W e ) for each target width by multiplying the standard deviation by 4.133 [37,41] and used W e to calculate the effective ID, ID e accordingly.

Original: As a result, the difference between W and W e ranges from 0cm to 0 .
Revised: As a result, the difference between W and W e ranges from 0cm to 0 .

Original: 6cm and the corresponding difference between ID and ID e ranges from 0 bits to 0.12 bits, as shown in
Revised: 6cm and the corresponding difference between ID and ID e ranges from 0 bits to 0.12 bits, as shown in

Original: Table 1.
Revised: Table 1.

Original: The classical Fitts’ Law was used for modelling [21, 35] due to two concerns: 1. for ID, as our targets are spheres, Fitts’ law variants for targets in arbitrary shapes are not necessary.
Revised: The classical Fitts’ Law was used for modelling [22,36] due to two concerns: 1. for ID, as our targets are spheres, Fitts’ law variants for targets in arbitrary shapes are not necessary.

Original: as [54] points out, no work has included all spatial factors in 3D space and a standard metric for 3D modelling is missing.
Revised: as Triantafyllidis & Li [55] points out, no work has included all spatial factors in 3D space and a standard metric for 3D modelling is missing.

Original: Meanwhile, the classical formula is still a common practice in VR [8,53,54].
Revised: Meanwhile, the classical formula is still a common practice in VR [9,54,55].

Original: [Table]
Revised: [Table]

Original: To compensate for the non-normal distribution of selection time in T sel [Table] we adopted a common practice [13,51] where we first aggre- gated the mean for each effective Index of Difficulty ( ID e ) per Block per Task and then aggregated the median for each ID e and Task .
Revised: To compensate for the non-normal distribution of selection time in T sel , we adopted a common practice [14,52] where we first aggre- gated the mean for each effective Index of Difficulty ( ID e ) per Block per Task and then aggregated the median for each ID e and Task .

Original: ID e ranged from 1.61 to 2.66 bits and the aggregated median time of all tasks correlate with ID e positively ( R 2 ≥ 0 . 96), as shown in Fig.
Revised: ID e ranged from 1.61 to 2.66 bits and the aggregated median time of all tasks correlate with ID e positively ( R 2 ≥ 0 . 96), as shown in Fig.

Original: When looking at the coefficients of these linear regression models in Table [Table] we noticed that slope values b were relatively similar across
Revised: When looking at the coefficients of these linear regression models in Table 2, we noticed that slope values b were relatively similar across

Original: Task but R EORIENTING has a higher y-intercept value a than the[Figure] [Figure] other four tasks.
Revised: Task but R EORIENTING has a higher y-intercept value a than the other four tasks.

Original: We also report throughput scores in Table 2.
Revised: We also report throughput scores in Table 2.

Original: Unsur- prisingly, T ARGETING had the largest throughput, R EORIENTING had the lowest throughput, and the throughputs of the other three tasks were similar, a result consistent with T sel values
Revised: Unsurprisingly, T ARGETING had the largest throughput, R EORIENTING had the lowest throughput, and the throughputs of the other three tasks were similar, a result consistent with T sel values

Original: [Figure]
Revised: [Table]

Revised: M e d i a n

Revised: S e l ec t i on

Revised: T i m e ( s )

Original: 5 G ENERAL D ISCUSSION
Revised: Task TARGETING DUALTARGETING DOCKING THROWING REORIENTING R 2 = 0. TARGETING

Revised: R 2 = 0.

Original: The goal of this paper is to explore the impact of intended use of a target,
Revised: DUALTARGETING R 2 = 0. DOCKING R 2 = 0.

Original: i
Revised: THROWING

Original: [Table]
Revised: R 2 = 0. REORIENTING Fitt's IDe 5 G ENERAL D ISCUSSION

Original: e. the Task performed on the target, on the time required to select that target, i [Table] e. T sel , before performing the manipulation.
Revised: The goal of this paper is to explore the impact of intended use of a target, i.e. the Task performed on the target, on the time required to select that target, i.e. T sel , before performing the manipulation.

Original: We examined five tasks: classical T ARGETING , D UAL T ARGETING ,
Revised: We examined five tasks: classical T ARGETING , D UAL T ARGETING ,

Original: D OCKING , T HROWING [Table] and R EORIENTING .
Revised: D OCKING , T HROWING , and R EORIENTING .

Original: Our hypothesis is that there is an impact, i.e. that the selection that precedes target manipulation is impacted by the specific manipulation Task that we perform on the selected target.
Revised: Our hypothesis is that there is an impact, i.e. that the selection that precedes target manipulation is impacted by the specific manipulation Task that we perform on the selected target.

Original: Our results support this hypothesis.
Revised: Our results support this hypothesis.

Original: While we did not find a significant effect on error rate, we did find significant differences in the time taken to select the target.
Revised: While we did not find a sig- nificant effect on error rate, we did find significant differences in the time taken to select the target.

Original: In particular, we found that[Table] selection preceding classical T ARGETING took significantly less time than selections preceding all other Task s we tested, and that selection preceding a R EORIENTING Task took significantly longer than selections preceding all other tasks.
Revised: In particular, we found that selection preceding classical T ARGETING took significantly less time than selections preceding all other Task s we tested, and that selection preceding a R EORIENTING Task took significantly longer than selections preceding all other tasks.

Original: Target selection preceding
Revised: More specifically, with the interaction effect between Task and ID , T ARGETING was significantly faster than some Task s at certain ID s and R EORIENTING was significantly slower than all other Task s at all ID s except for T HROWING at 2. bits.

Original:  D UAL T ARGETING , D OCKING and T HROWING did not differ signif- icantly in the time taken.
Revised: Target selection preceding D UAL T ARGETING , D OCKING and T HROWING did not differ significantly in the time taken.

Revised: While differences of selection time between those four tasks and T ARGETING was small (within 0 .

Revised: 27 s ), they had been already more than 7% slower, with R EORIENTING over 30% slower than T ARGETING .

Original: Given that there are significant differences for T ARGETING and R EORIENTING , we reject our null hypothesis (no impact of intended use) and claim evidence for the impact of intended use on target selection.
Revised: Given that there are significant differences for T ARGETING and R EORIENTING , we reject our null hypothesis (no impact of intended use) and claim evidence for the impact of intended use on target selection.

Original: To understand where and how this difference occurred, we ana- lyzed movement time during the selection task.
Revised: To understand where and how this difference occurred, we ana- lyzed movement time during the selection task.

Original: First, we created kinematic profiles of distance and speed during target selection.
Revised: First, we created kinematic profiles of distance and speed during target selection.

Original: A visual inspection of Figure 9 shows that R EORIENTING has a lower peak speed and reaches peak speed earlier in movement with a corresponding increase in the length of time spent decelerating.
Revised: A visual inspection of Figure 10 shows that R EORIENTING has a lower peak speed and reaches peak speed earlier in movement with a cor- responding increase in the length of time spent decelerating.

Original: Further kinematic analysis supports this visual analysis; in Figure 8, R EORIENTING has the lowest peak speed ( pkV ) and the longest deceleration phase (greater afterpkV %) than other Task s by a sig- nificant margin.
Revised: Further kinematic analysis supports this visual analysis; in Figure 9, R EORIENTING has the lowest peak speed (at least 4 . 8% lower) and the longest deceleration phase (more than 20% greater) than other Task s by a significant margin.

Original: It implied that participants planned their movement and did not rush during the selection for R EORIENTING .
Revised: It implied that participants planned their movement and did not rush during the selection for R EORIENTING .

Original: Besides, the Fitts’ law modelling confirmed this by showing that the reaction time for R EORIENTING was higher and the throughput value of R EORIENTING was smaller than other tasks.
Revised: Besides, the Fitts’ law modelling confirmed this by showing that the reaction time for R EORIENTING was higher and the throughput value of R EORIENTING was smaller than other tasks.

Original: The kinematic analysis does, however, present some additional observations that merit future investigation.
Revised: The kinematic analysis does, however, present some additional observations that merit future investigation.

Original: For example,while R EORIENTING results in the lowest peak speed during the preceding selection, T HROWING results in the  preceding selection movement characterized bythe highest peak speed, and this peak speed differs M e d i a n S e l ec t i on
Revised: For example, R EORIENTING results in the lowest peak speed; T HROWING results in the highest peak speed in the preceding selection movement, which is followed by peak speed for selection preceding T ARGETING .

Original: T i m e ( s )

Original: Task TARGETING DUALTARGETING

Original: DOCKING THROWING REORIENTING

Original: R 2 = 0.

Original: TARGETING

Original: R 2 = 0.

Original: DUALTARGETING

Original: R 2 = 0.

Original: DOCKING

Original: R 2 = 0.

Original: THROWING

Original: R 2 = 0.

Original: REORIENTING

Original: Fitt's IDe significantly from all other peak speeds.

Original: This is followed by peak speed for selection preceding T ARGETING .

Original: D UAL T ARGETING and D OCKING did not result in significant differences in peak speed in the preceding selection kinematics.
Revised: D UAL T ARGETING and D OCKING do not result in significant differences in peak speed in the preceding selection kinematics.

Original: Effect sizes are not small for various measures of kinematics highlighted in Section 4.3.
Revised: Effect sizes are not small for various measures of kinematics highlighted in Sect. 4.3.

Original: Based on typical interpretations of effect size, η 2 > 0 .
Revised: Based on typical interpretations of effect size, η 2 > 0 .

Original: 14, i.e. large effect size 1 , for all differences except time to peak speed ( t 2 pkV ), where the effect size maps to medium effect.
Revised: 14, i.e. large effect size 1 , for all differences except time to peak speed ( t 2 pkV ), where the effect size maps to medium effect.

Original: The absence of additional significant differences in selection time between D UAL T ARGETING , D OCKING and T HROWING does not imply that differences in se- lection prior to these Task s do not exist; it simply implies that we measured no significant differences in selection time.
Revised: The absence of additional significant differences in selection time between D UAL T ARGETING , D OCKING and T HROWING does not imply that differences in selection prior to these Task s do not exist; it simply implies that we measured no significant differences in selection time.

Original: Future work is planned to probe these effects in more depth.
Revised: Future work is planned to probe these effects in more depth.

Original: It is interesting to contrast our results in Mandryk’s and Lough’s results in 2D [37].
Revised: It is interesting to contrast our results in Mandryk’s and Lough’s results in 2D [38].

Original: Mandryk’s and Lough’s four intended uses ( Task s) correspond to T ARGETING , D UAL T ARGETING , D OCKING , and T HROWING in our experiment.
Revised: Mandryk’s and Lough’s four intended uses ( Task s) correspond to T ARGETING , D UAL T ARGETING , D OCKING , and T HROWING in our experiment.

Original: They found that T ARGETING and D UAL T ARGETING resulted in selection times ( T sel ) that differed significantly from D OCKING ’s and T HROWING ’s selection times, but did not observe differences between T ARGETING and D UAL- T ARGETING .
Revised: They found that T ARGETING and D UAL T ARGETING resulted in selection times ( T sel ) that differed significantly from D OCKING ’s and T HROWING ’s selection times, but did not observe differences between T ARGETING and D UAL T ARGETING .

Original: In contrast, while we, too, found that T ARGETING resulted in shortest preceding selection times, D UAL T ARGETING resulted in T sel more similar to D OCKING ’s and T HROWING ’s T sel .
Revised: In contrast, while we, too, found that T ARGETING resulted in shortest preceding selection times, D UAL T ARGETING resulted in T sel more similar to D OCKING ’s and T HROWING ’s T sel .

Original: In terms of overall differences, we and Mandryk and Lough find differences in T selof just under 10% in overall magnitude for these selections (9.4% in ours and 8.8% estimated in theirs).
Revised: In terms of overall differences, we and Mandryk and Lough find differences in T sel under 10% in overall magnitude for these selections (9.4% in ours and 8.8% estimated in theirs).

Original: Our R EORIENTING is unique to our study, and resulted in the most significant differences in preceding selection time; R EORIENTING resulted in target selection times more than 30% longer(1.12s vs 0.85s) than T ARGETING .
Revised: Our R EORIENTING is unique to our study, and resulted in the most significant differences in preced- ing selection time; R EORIENTING resulted in target selection times more than 30% longer than T ARGETING .

Original: Given our results that the intended use of a target impacts the time taken to select a target, the question that follows from this is what these results mean.
Revised: Given our results that the intended use of a target impacts the time taken to select a target, the question that follows from this is what these results mean.

Original: To address this question, we point, again, to the analysis of 20 years of VR-based by Bergstr ¨ om et al.
Revised: To address this question, we point, again, to the analysis of 20 years of VR-based by Bergstr¨om et al.

Original: As noted earlier in our review of this work, Bergstr¨om et al.
Revised: As noted earlier in our review of this work, Bergstr¨om et al.

Original: highlight that, as dependent variables, selection task studies measure selection time for the selection task and manipulation task studies measure the time participants take to manipulate an object.
Revised: highlight that, as dependent variables, selection task studies measure selection time for the selection task and manipulation task studies measure the time participants take to manipulate an object.

Original: Success in selection-based research is measured by shorting selection times or reducing errors, or both; success in manipulation is similarly based on increased throughput for the manipulation task.
Revised: Success in selection-based research is measured by shortening selection times or reducing errors, or both; success in manipulation is similarly based on increased throughput for the manipulation task.

Original: The assumption that underlies these success metrics is, ipso facto , that each individual user action can be optimized in isolation from other tasks in interfaces, i.e., that manipulation does not impinge upon preceding selection, but our results argue that this assumption cannot be made.
Revised: The assumption that underlies these success metrics is, ipso facto , that each individual user action can be optimized in isolation from other tasks in interfaces, i.e., that manipulation does not impinge upon preceding selection, but our results argue that this assumption cannot be made.

Original: If different manipulations impact the time taken for a preceding selection, then measuring only the manipulation time may over-estimate (or under- estimate) the benefits of a novel interaction technique.
Revised: If different manipulations impact the time taken for a preceding selection, then measuring only the manipulation time may over-estimate (or under- estimate) the benefits of a novel interaction technique.

Original: It is also true that we only measure retrospective impact (i.e. the impact of future intended use on preceding selection), but prospective impacts are also possible.
Revised: It is also true that we only measure retrospective impact (i.e. the impact of future intended use on preceding selection), but prospective impacts are also possible.

Original: It is hypothetically possible that a pointing facilitation technique in VR, e.g. an area cursor, might impact a user’s ability to perform a task on a target, e.g. reorientation of the target acquired via the area cursor.
Revised: It is hypothetically possible that a pointing facilitation technique in VR, e.g. an area cursor, might impact a user’s ability to perform a task on a target, e.g. reorien- tation of the target acquired via the area cursor.

Original: While exploring prospective impacts is one area of future work, it also highlights a more general implication for system design.
Revised: While exploring prospective impacts is one area of future work, it also highlights a more general implication for system design.

Original: Specifically, when we haveavailable to us new interaction techniques (i.e. new manipulations) or new pointing facilitation techniques (i.e. new selection techniques), incorporating them into realistic systems requires thinking not just about the individual action that they optimize but also about their place within and more general impacts on the overall task flow of the user.
Revised: Specifically, when we have new interaction techniques (i.e. new manipulations) or new pointing facilitation techniques (i.e. new selection techniques), incorporating them into realistic systems requires thinking not just about the individual action that they optimize but also about their place within and more general impacts on the overall task flow of the user.

Original: 5.1 Applicable Scenarios
Revised: 5.1 Applicable Scenarios

Original: Our results can be framed into concrete applications and interface design in VR.
Revised: Our results can be framed into concrete applications and interface design in VR.

Original: One classical example is menu selection [20, 39].
Revised: One classical example is menu selection [21, 40].

Original: Considering Fig.
Revised: Considering Fig.

Original: 11, our results suggest that interactions that take advantage of D UAL T ARGETING and D OCKING may result in similar selection time prior to the manipulation sub-task and differ from each other based on the design of manipulation techniques.
Revised: 12, our results suggest that interactions that take advantage of D UAL T ARGETING and D OCKING may result in similar selection time prior to the manipulation sub-task and differ from each other based on the design of manipulation techniques.

Original: In contrast, techniques that leverage R EORIENTING already take longer selection time prior to the manipulation.
Revised: In contrast, techniques that leverage R EORIENTING already take longer selection time prior to the manipulation.

Original: Similarly, in data visualization tasks in virtual environments [11], rotational manipulations of a dataset may introduce additional costs if the target acquisition prior to the rotational manipulation is slowed.
Revised: Similarly, in data visualization tasks in virtual environments [12], rotational manipulations of a dataset may introduce additional costs if the target acquisition prior to the rotational manipulation is slowed.

Original: 5.2 Limitations
Revised: 5.2 Limitations

Original: One  limitationthat we highlight is that  our  experiment was con- ducted remotely in participants’ homes.
Revised: One highlighted limitation is that the range of IDs ( 1.49 to 2.66 bits) in this experiment is fairly small for a Fitts’ Law design. This is because we restrict our current experiment to arms’ length interactions with a controller on static targets, so target widths and distances are constrained for a reachability concern in VR. While these IDs are commonly used in a arms’ length interactions in VR environment, they are low compared to desktop interfaces [38], touch-based interfaces [24], distant interactions in VR [7, 9, 14, 15], contexts where movement amplitudes can increase due to the greater distance of targets from the user. This explains why the throughput values in Table 2 are relatively lower, compared to throughput scores in other VR studies [9,54].

Revised: However, incorporating more distant interactions adds additional complexity to the selection action because direction (e.g. targeting via a ray) and depth are often controlled differently during distant interaction [7,9].

Revised: Future work could assess the findings for a wider range of IDs.

Revised: While we contrast five different Task s (the intended use) in our study, there exist more complex manipulations in the virtual environments.

Revised: Furthermore, we do not consider objects’ surface characteris- tics, perceived weight, and perceived fragility [30,47] as objects in our study have similar surface, weight and fragility.

Revised: We also do no consider bimanual interactions [27] nor co-articulated actions (e.g.

Revised: 6-dof reorienting, i.e. a docking task that requires both rotation and translation) [29,49].

Revised: As noted in the experiment design, we did not control the Primary Direction and Secondary Direction .

Revised: Machuca and Stuerzlinger [8] found that target acquisition in virtual envi- ronments was slower and had less throughput along the depth-axis, i.e. Forward and Backward in our experiment, than lateral direc- tions.

Revised: Further research can explore how directions can impact the acquisition for various intended uses, particularly for co-articulated actions.

Revised: Finally, it is noted that our experiment was conducted remotely in participants’ homes.

Original: While an in-home environment increases the external validity of our study, it cannot be as controlled as a laboratory one, whose setup and control can assume to be optimized for the experiment.
Revised: While an in-home environment increases the external validity of our study, it cannot be as controlled as a laboratory one, whose setup and control can assume to be optimized for the experiment.

Original: Space in homes may be constrained, and households may present interruptions during the experiment.
Revised: Space in homes may be constrained, and house- holds may present interruptions during the experiment.

Original: To limit this as a factor, we note that participants were provided with detailed instructions, from both pre-recorded videos and experiment systems.
Revised: To limit this as a factor, we note that participants were provided with de- tailed instructions, from both pre-recorded videos and experiment systems.

Original: A researcher was also present via video conferencing tools during the experiment, allowing the en- vironment to be monitored for confounds.
Revised: A researcher was also present via video conferencing tools during the experiment, allowing the environment to be monitored for confounds.

Original: We also note that our within-subjects design partially controls for confounds by ensuring that the environment is similar across each task for a participant.
Revised: We also note that our within-subjects design partially controls for confounds by ensuring that the environment is similar across each task for a participant.

Original: While we contrast five different Task s (the intended use) in our study, there exist more complex manipulations in the virtual environments.

Original: Furthermore, we do not consider objects’ surface charac- teristics, perceived weight, and perceived fragility [29,46] as objects in our study have similar surface, weight and fragility.

Original: We also do no consider bimanual interactions [26] nor co-articulated actions (e.g. 6-dof reorienting, i.e. a docking task that requires both rotation and translation)

Original: [28,48].As noted in the experiment design, we did not control the Primary Direction and Secondary Direction .

Original: Further research can explore how directions can impact the acquisition for various intended uses, particularly for co-articulated actions.

Original: Finally, we restrict our current experiment to arms’ length interactions with a controller on static targets, so target widths and distances are constrained, i.e., we have IDs between 1.49 bits to 2.66 bits.

Original: While these IDs are commonly used in a arms’ length interactions in

Original: VR environment, they are low compared to desktop interfaces [37], touch-based interfaces [23], distant interactions in VR [7,8,13,14], contexts where movement amplitudes can increase due to the greater distance of targets from the user.

Original: This also explains why the throughput values in Table 2 are relatively lower, compared to throughput scores in other VR studies [8,53].

Original: Incorporating more distant interactions does, however, add additional complexity to the selection action because direction (e.g. targeting via a ray) and depth are often controlled differently during distant interaction [7,8].

Original: 5.3 Future Work
Revised: 5.3 Future Work

Original: The results of our intended use study present interesting avenues of future research into interaction in virtual reality environments.
Revised: The results of our intended use study present interesting avenues of future research into interaction in virtual reality environments.

Original: As one example, a novel interaction technique might not result in higher throughput during the interaction, but it is possible that it might speed the selection that precedes the interaction.
Revised: As one example, a novel interaction technique might not result in higher throughput during the interaction, but it is possible that it might speed the selection that precedes the interaction.

Original: In this case, considering both the selection and manipulation of a target as a unified task allows us to identify potentially beneficial novel interactions that might have been ignored if the only metric for success is throughput for the manipulation.
Revised: In this case, considering both the selection and manipulation of a target as a unified task allows us to identify potentially beneficial novel interactions that might have been ignored if the only metric for success is throughput for the manipulation.

Original: Another possible area of inquiry given differences in kinematics noted in Section 4.3 is that a more careful analysis of movement during selection might allow the system to infer what a user intends to do with a target prior to acquiring the target.
Revised: Another possible area of inquiry given differences in kinematics noted in Sect. 4.3 is that a more careful analysis of movement during selection might allow the system to infer what a user intends to do with a target prior to acquiring the target.

Original: This, in turn, could allow us to develop interaction techniques that leverage this infer- ence.
Revised: This, in turn, could allow us to develop interaction techniques that leverage this inference.

Original: Reorientation appears a good initial candidate to identify given the deviation in the selection kinematics shown in Figure 9.
Revised: Reorientation appears a good initial candidate to identify given the deviation in the selection kinematics shown in Sect. 4.3.

Original: 6 C ONCLUSION
Revised: 6 C ONCLUSION

Original: A significant body of research in virtual reality exists that explores both selection and manipulations techniques to speed performance, and this past research typically assumes that these actions – the selection and the subsequent manipulation – can be independently optimized [9].
Revised: A significant body of research in virtual reality exists that explores both selection and manipulations techniques to speed performance, and this past research typically assumes that these actions – the selection and the subsequent manipulation – can be independently optimized [10].

Original: Based upon past work in 2D environments, in this paper we question this assumption.
Revised: Based upon past work in 2D environments, in this paper we question this assumption.

Original: We examine the impact of five common virtual reality manipulations (T ARGETING , D UAL T ARGETING , D OCKING , T HROWING , and R EORIENTING ) on the time taken to select a target prior to performing the manipulation.
Revised: We examine the impact of five common virtual reality manipulations (T ARGETING , D UAL T ARGETING , D OCKING , T HROWING , and R EORIENTING ) on the time taken to select a target prior to performing the manipulation.

Original: We identify the existence of an effect of subsequent use of a target on the act of selecting the target.
Revised: We identify the existence of an effect of subsequent use of a target on the act of selecting the target.

Original: Specifically, we find that T ARGETING had the shortest selection time, R EORIENTING had the longest, and the other three intended uses we evaluate result in acquisition times between these two extremes.
Revised: Specifically, we find that T ARGETING had the shortest selection time, R EORIENTING had the longest, and the other three intended uses we evaluate result in acquisition times between these two extremes.

Original: We synthesize these results and highlight their implications for research and design in VR environments.
Revised: We synthesize these results and highlight their implications for research and design in VR environments.

Revised: A CKNOWLEDGMENTS

Revised: We would like to thank all participants for their help with the remote studies, and reviewers for their valuable feedback.

Revised: This research received ethics clearance from the Office of Research Ethics, Uni- versity of Waterloo, and this work was made possible by the LAI

Revised: R´eapp.

Revised: This paper was published in memory of Edward Lank, whose contribution to science will not be forgotten.