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The continuity of feedback is a central component of direct

manipulation (Shneiderman, 1983), which leads UI designers to

visual representations of music data that, while continuous, can be highly abstract. Rapid, shorter, more focused musical feedback, interleaves the domain representation with lower-level interaction (e.g. simple edits), towards not only support for more “continuous representation of objects of interest”, but through “rapid reversible incremental actions with immediate feedback” (see Table 2).

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es _{• continuous representation of the objects and actions of interests;}

• rapid reversible incremental actions with immediate feedback about the object of interest; • physical actions and button pressing instead of issuing commands with complex syntax;

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• helping beginners learn basic functionality rapidly;

• enabling experienced users to work rapidly on a wide range of tasks;

• allowing infrequent users to remember how to carry out operations over time; • preventing the need for error messages, except very rarely;

• showing users immediately how their actions are furthering their goals; • reducing users’ experiences of anxiety;

• helping users gain confidence and mastery and feel in control;

Table 2 - The principles and benefits of direct manipulation,

as summarised by Sharp et al (2007), based on Shneiderman (1983).

Sequencers and DAWs offer graphical user interfaces (GUIs) based on traditional applications of direct manipulation principles, developed for visual mediums. Digital tools and processes are linked to musical concepts through visual representations, which help trained musicians understand the workings of a program. Leman (2008) argues that this use of visual notations is harmful to music interaction, where users are involved in music only “indirectly”, through the visual proxy of notation (see also system

indirection, Figure 4-9). While trackers do not abandon the

advantages of a notation (e.g. abstraction as a tool), feedback is shifted to prioritise audio representations of the musical data, but in a way where the aforementioned goals and principles of direct manipulation are respected. This section has shown that this approach to direct manipulation for audio, using frequent, rapid, short episodes of audio feedback, confers the same advantages Shneiderman (1983) observed in visual mediums (see Table 2). The previous section described the use of musical feedback to facilitate a user’s understanding and use of tracker notation. In this section, these analyses are extended to consider the editing activity and complexity of notation use that precipitates playback, and the effect of experience.

measuring directness

Figure 10 shows the elapsed time spent editing the music, between playback, for novice and expert tracker users, generalising trends observed in user sessions (see Figure 9 (a)). Logarithmic sampling is used, so that the area under the curve remains proportional to the number of episodes, using a log scale (see inset for an illustration of the intervals used). In the Expert case, the curve exhibits a log-normal distribution centred on a mode of 17.13s and median of 15.92s. For Novice users, the distribution is skewed towards considerably longer editing episodes, with a median of 67.15s and mode of 155.76s (2m36s).

However, a notable increase, relative to a log-normal distribution, is apparent at very short editing durations, below 10s (local minima, 1.30s), which may indicate inexperienced users tinkering with the tracker; making small changes to the notation, then using playback to understand their effect. This behaviour largely disappears with minimal experience.

The extent to which novice users are working slower, rather than simply longer, is indicated by Figure 11, which plots the number of edits (inputs that affect the data) between auditions, rather than absolute time. Here, experts, like novices, are shown to also favour individual edits, but as part of a wider trend towards shorter editing sequences (median = 2.36 edits), whilst novice interaction is still characterised by greater editing activity between requests for musical feedback (median = 5.44 edits).

(a) Tracker Novice User #129 (Recorded 21/09/10) Tracker Expert User #32 (Recorded 04/08/09) (b) ∆d × ∆t uncertainty ~ 2 (c)

Figure 9 – Editing metrics and uncertainty. Examples, explanations, and definitions relating to analysis

of editing episodes: (a) Total data changes plotted against session time, as visualised in iMPULS|IVE (see Section 5.4), taken from two representative session logs, with corresponding keyboard activity indicated on the time axis, in green; (b) Illustrative example of the roles of edits, selections and playback within an editing episode; (c) Proposed model for uncertainty, as used in Figure 12.

Table 3 Duration and editing statistics

from Figures 10-12

editing episodes novices experts Duration median 67.15s 13.24s

mode 155.76s 17.13s Number of edits median 5.44 2.36

mode 1 1

Data created/modified median 5.70 4.00

Figure 10 – Editing Episode Durations. The elapsed time (in seconds) between uses of playback,

during which data is edited, for novice (green, n=548) and expert (red, n=574) tracker users. Data taken from sessions with over 30 minutes of interaction sampled logarithmically (see inset).

0% 5% 10% 15% 20% 1 10 100 1000

Figure 11 – Editing Activity between Auditions. The number of edit actions between uses of playback,

during which data is edited, for novice (green, n=548) and expert (red, n=574) tracker users. Data taken from sessions with over 30 minutes of interaction sampled logarithmically. Adjusted (dotted) lines account for the increased scope of selections-based edits.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.1 1 10 100 1000 10000 100000 1000000

Figure 12 – Model and Plot of Editing Uncertainty. Uncertainty modelled as the product of data

modifications and time between auditions (see inset equation), for novice (green, n=548) and expert (red, n=574) tracker users. Data taken from sessions with over 30 minutes of interaction (sampled logarithmically). 0% 2% 4% 6% 8% 10% 0.1 1 10 100 1000 10000 1000

It is expected that expert users enter data more efficiently, exploiting features to manipulate blocks of music, rather than just individual notes. To account for the varying complexity of edits, logs were analysed to track the cumulative amount of data created (or modified) prior to each audition (illustrated in Figure 9 (b)), factoring for the use of selections and the clipboard. Figure 11 includes adjusted curves for both groups, which effectively weight edits according to the size of the selection they address. As the users’ musical data is not collected, the data density of selections is estimated using a 25% coverage heuristic, based on tracker usage established from saved file summaries and publicly-available tracker songs.14,15

The adjustment brings the expert profile closer to that of novices, who show only minimal selection-based editing (likely through drag-and-drop). However, the expert’s greater tendency to make shorter edits remains, and thus neither speed nor efficiency can completely explain their more frequent use of auditions.

The variables modelled in Figure 11 and 12 can be seen as factors in the user’s perception of liveness, which concerns the mapping of physical action to its effect on the domain (see 4.2.4). The difference between discrete levels of liveness is described by the nature and extent of the delay (in time or edits) in domain feedback, inherent in a notation or UI. Music software can exhibit Level 2 (manually-triggered), Level 3 (edit-triggered), or Level 4 (real-time) liveness. However, the findings above illustrate how experienced tracker users manually-trigger playback at or near the edit rate, influencing the effective liveness of the user experience. Greater liveness, through timely domain feedback, makes it easier for the users to understand the effect of their interactions within the notation, so far as they relate to the domain. As time passes, individual edits accumulate, making it harder for a user to recall and maintain a mental image of the product described, based solely on the abstract visual representation. Eventually, a threshold is reached, whereby it becomes necessary to execute (e.g. audition) the work to comprehend it. To account for the effect of both memory and editing complexity, Figure 9 (c) presents a hypothetical model of this threshold, modelled as uncertainty with

14

This corresponds to an average of one note every four tracker rows, or single musical beat. This fits with expectations regarding the use of the notation, balancing denser percussive patterns, which divide the beat, and sparser harmonies, which combine several beats (or bars) and tracks.

15

Note that a constant coefficient does not account for the tendencies of users, especially experts, to chunk selections into more abstract groupings (e.g. beat, bar, instrument, melody, harmony, pattern), which serve to make large selections more manageable. If we were to assume that most selections encapsulate single gestalts, such as a bar, then complexity is best modelled simply by the number of edits.

the notational representation that grows in proportion to both the number of changes in data, ∆d, and passage of time, ∆t (in seconds).16 Figure 12, as the product of the distributions in Figures 10 and 11, exhibits a log-normal distribution under the model, with modes of 225.02 for novices and 24.75 for experts.

Whilst greater expertise and literacy in most fields, including music, typically enables an individual to work more exclusively with written notation without recourse to live simulation, the findings in this section suggest this is not the case in tracking. In contrast, experience with trackers leads to a lower threshold, and sees tracker experts relying more on audio feedback (from the

musical domain) rather than visual feedback (from the notation).

Unfortunately, the absence of equivalent data sets for other authoring software prevents the wider testing of the model, in respect of its applicability to more general interaction in music production and creativity. This work is left to future research. However, if verified, there are implications and applications of this metric, for UI design.

Less dependence on visual notation reduces the literacy threshold; tracker novices should instead seek to introduce more frequent auditions into their interaction. This frequency and the other editing metrics explored in this section (complexity, uncertainty), are measurements and calculations that can be made at the time of interaction. A program can track them and use them in support of the user’s interaction and development. For example, music programs could display “liveness status”,17 as a visual cue to encourage more frequent auditions. This might be as simple as a visible timer counting from the last instance of playback, a counter of the un-auditioned edits, or varied shading of data to indicate when, if ever, it was last heard. More complex implementations might track a user’s average over time, activating a response only if it deviates sufficiently from an established optimum.18

In the samples studied, novices have tended towards longer editing episodes. Some evidence has been identified for the use of very short edits, possibly as a learning device. In Figures 10 and 12, these appear as local maxima, outside the main log-normal

16

This variable can be seen to approximate the area under the line, in Figure 9 (c).

17

For example, as a timer counting from the last instance of playback, or a meter counting the subsequent number or extent of edits. Sections of music could also be colour-coded (e.g. desaturated) to reflect the times they were last heard live, creating a heat map signifying editing activity within a piece. Rothermel et

al (2000) have used similar visual feedback mechanisms to indicate “testedness” in spreadsheet use and

software development, representing the degree to which a formula or block of code has been executed (tested). Notably, however, the objective in artistic creativity is to support a user’s focus on their music, rather to encourage “correctness” or guard against “overconfidence” (see Rothermel et al, 2000).

18

distributions, this time below the averages for experts. In these cases, extensive use of playback is punctuated by trivial edits that limit a user’s productivity, if maintained. This bimodal distribution may suggest a goldilocks principle; a happy medium between editing that is too long or short, too complex or trivial, or involves too much or too little uncertainty. In this way, an individual’s use of playback in managing liveness may be related to flow theory’s

balance of challenge and ability (see 3.7), and the “flow channel”

that lies between boredom and anxiety (Figure 3-7). The challenge of mentally simulating a visual representation of the music is mitigated by aural feedback, but greater ability to work without this scaffold benefits overall productivity and allows the individual to tackle greater musical challenges.