speed and timing

The average user demonstrated a keyboard interaction rate of 9.74±0.44 commands per min (cmd/min). Tracker novices were the slowest, averaging 6.34±0.68 cmd/min (n=67), and tracker experts were significantly faster (p < .05), averaging 11.89±0.50 cmd/min (n=107) – almost twice as fast as novices. However, the fastest overall work rate is demonstrated by reViSiT experts, who can average up to 42.42±1.08 cmd/min (exhibited by the composer who took part in the video study).

These figures average the total number of keyboard commands triggered over a normal period of reViSiT interaction, which also includes thinking time and periods spent interacting with the mouse. Sessions of over 30 minutes are used to calculate a user’s average, ignoring the first 10 minutes, which is characterised by preparatory activity. In most users’ first session, bursts of data entry are also common in the first 2 or 3 minutes. This is attributed to new users entering random notes into the pattern, to experiment with the workings of the pattern editor and keyboard – similar to when users record random music into a sequencer, to test its workings. In both cases, the provisionality of the notation enables the user to learn by experimentation.

Figure 1 shows log graph (with linear detail inset), showing the timing separation of different keys in sequences of keyboard input (within a 10s threshold, and ignoring repeats)1, as a measure of the speed users move around the keyboard.

0.01% 0.10% 1.00% 10.00%

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Tracker Beginners Tracker Experts

0% 1% 2% 3% 4% 0 100 200 300 400 500 600 700 800 900 1000

Figure 1 – Distribution of intervals between distinct keys (ignoring repeated keys).

1

experience improves

speed and consistency Both series decay according to an inverse power law (beginners,

R2=0.963; experts, R2=0.987), but while experts average a faster

overall rate of interaction (median = 400.9ms, compared to 557.4ms for beginners), the mode drops 11% (from 125.0ms, for beginners, to 140ms, for experts). Instead, experts’ higher average is attributable to an increase across the 100-500ms range and decrease in longer intervals (above 1000ms). Two explanations are offered for this: firstly, that the higher median rate for experts leads more quickly to tiredness and a long-term slowdown in performance. Secondly, that experts do not aim for peak performance, but a more relaxed, tempered, and sustained rhythm – pacing interaction and maintaining a sense of control, but also forestalling the onset of tiredness. Both conclusions are supported by the video study (Section 6.1), which not only notes the impact of tiredness, but also a rapid, yet tempered rate of interaction.

0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 0 100 200 300 400 500 600 700 800 900 1000 Tracker Experts reViSiT Experts IT2 Experts Non-Playing 0% 1% 2% 3% 4% 5% 6% 0 100 200 300 400 500 600 700 800 900 1000 Playing 150 120 100 1 2 4

110 Musical Beat (Tempo)

Tracker Row (120bpm)

Figure 2 – Intervals between keys with and without playback (including non-typematic

repeats). Histogram of inter-keystroke intervals (x-axis, in milliseconds), with guidelines for common musical tempo and tracker row intervals.

rhythmic cursoring

In Figure 2, samples are taken of experts with differing tracker backgrounds, and include manually repeated keys, but not typematic repeats (when a key is held), to show the intervals between physical key presses. A similar peak around 150ms, followed by a long tail, is visible in the plot, but also accompanied by local maxima at several other intervals, which correspond to musical timings, notably the musical beat at the sequencer’s default tempo of 120bpm (500ms), which occurs both during and outside music playback.2 reViSiT experts, familiar with the more

2

In Nash and Blackwell (2011), these results were presented including typematic repeats, which lead to large, additional spike at 20-30ms (typematic rate). Notably, it also accentuates the peak at 500ms (typematic delay). To scrutinise a potential link between musical tempo and non-musical interaction, the

complex handling and synchronisation of tempo in the host-plugin configuration show a more diverse use of tempi, with additional peaks corresponding to 100, 110 and 150 beats per minute.

In terms of flow, this is an indication of action-awareness

merging – an implicit coupling of musical perception and motor

action, where the environment influences the user’s behaviour. Such entrainment in music, such as the tendency of listeners to tap a musical beat, is been widely studied in psychoacoustic research (e.g. Clayton et al, 2005), but is here merged with program interaction, and shows that motor behaviour in trackers is subject to both conscious and unconscious influences (also showing the

interference of visual and musical feedback in a manipulation- driven system; inset, see Figure 4-9). This interaction also has the

effect of maintaining the continuity of physical activity in idle time between episodes of more focused editing,3 and may serve as an epistemic action (Kirsch and Maglio, 1994), where the cursor is stepped over musical material to aid mental simulation.

Finer divisions of the beat, corresponding to a single pattern row in the tracker (125ms), are also evident. The non-playing sample excludes note entry (which triggers playback of the note), but includes intervening cursor movement, which makes up most of these peaks. This behaviour corresponds to the entering of notes in near-realtime, specific examples of which were found in the video study and logs of other experienced users. The absence of similar peaks for reViSiT Experts might be explained by the more varied use of tempo, but may also reflect a skill associated with longer term mastery, not yet widespread in the younger program.

controlling time _{Compared to live recording in the sequencer, the technique}

effectively extends a user’s command of the creative environment to the direct control of time. In terms of flow, the individual benefits from a greater sense of control, as the musical input rate can either be slowed to facilitate more complicated input, or accelerated to increase throughput. In this way, a user effectively self-regulates the balance of challenge and ability, allowing them to work at a natural pace that preserves a degree of musical continuity, without depending on realtime performance skills. Furthermore, the learning curve associated with tracking can be seen to reflect computer, rather than musical, literacy.

analysis presented here has been adapted to identify and filter typematic repeats from log data, producing a more accurate profile of physical user activity. The conclusions of the original paper, however, are still supported by the revised profile (Figure 2), in which the peaks remain evident.

3

Neurology research (Wickens, 2003) has linked high levels of dopamine in both motor activation and reward-mediated learning, contributing to an individual’s ability to maintain focus. As such, this habit in tracker users may represent an unconscious effort to self-regulate their level of engagement.