On the Relationship between Working Memory and Musical Performance Under Delayed Auditory Feedback

by Daniel Xu, Gauri Ramsoekh, Oskar Kruse, & Yadav Permalloo
3185 words




           Data Analysis


           Musical Performance
           DAF Effect on Musical Performance
           Working Memory
           Working Memory and DAF Effect

Discussion and Conclusion




            Twenty-three (9 male, 14 female) participants were recruited from the student community of Erasmus University College. Mean age was 21.87 (SD = 4.07). Sixteen participants reported playing an instrument; 12 reported piano as their primary instrument (mean years of instruction = 6.07; SD = 3.28). Twenty participants were right handed, and none reported any hearing impairment.


Musical Performance Task

            This part of the experiment involved multiple performances of an excerpt of music composed by one of the authors (DX), shown in Figure 1. The excerpt contained 22 notes and was designed to be easy to learn and repeatable by participants without prior musical instruction. The excerpt was a single line melody with no dynamic or expressive instructions, performed by the right hand only without requiring any changes to hand position. Directions for fingering were indicated under the musical notation, and the keyboard keys were marked correspondingly.

Figure 1
Musical Excerpt

            Participants performed on an M-Audio Oxygen 25 keyboard. Auditory output from the keyboard was delayed using a Black Arts delay device, and amplified with an Onyx Black-Jack USB recording interface. Participants heard auditory feedback through Bose QC-15 noise cancelling headphones at a listening level considered comfortable by the participant. Keypress responses and auditory output were recorded by a MacBook Pro 13’ 2018 laptop using the Ableton digital audio workstation software.

Working Memory Task

            Working memory capacity was measured using an N-back (2-back) task, available at psytoolkit.org. The N-back task is popular amongst researchers and easily administrable, with strong face validity (Owen et al., 2005). The task asks participants to determine whether the currently presented stimulus is the same as the stimulus that was presented N (in our case, 2) items previously. The stimulus set of the N-back task consisted of 15 alphanumeric letters; each stimulus was presented maximally for 760 ms, with an intertrial interval of 2000 ms.

Figure 2
N-Back Task example where the current presented stimulus is the same as the stimulus N(2) items previously.

Note. Created with PsyToolkit. http://psytoolkit.org/experiment-library/experiment_nback2.html

            Participants performed the N-back task on a MacBook Pro 13’ 2018 laptop and were asked to indicate a positive response with a touchpad tap. Auditory feedback (‘good’ or ‘bad’) was provided after each tap through headphones. Participant instructions and a practice block were built into the psytoolkit programme.


            Participants were randomised as to whether they performed the music task or working memory task first; there was a 5-minute rest in-between tasks to minimise fatigue effects.

Musical Performance Task

            To familiarise the participant with the music, each participant practiced the musical excerpt under normal feedback conditions until they could play it comfortably and accurately, i.e. they felt comfortable playing at uniform tempo and without error.

            For the experimental trials, participants were instructed to play at a uniform tempo without expressive variation; they were instructed to keep going if they made a mistake or speed up if they slowed down. A metronome speed was chosen that approached the maximum comfortable play speed of the participant; the same speed was used for all trials of that participant to provide a consistent reference tempo. The metronome played for eight beats prior to each trial; the metronome stopped and participants were instructed to start playing on the following beat whilst maintaining this tempo. A metronome playing throughout the trial was considered inappropriate due to its disorientating effect in the delay conditions.

            To generate a base performance for comparison, each participant played one trial under normal (no delay) feedback conditions. Participants then performed one trial each of three feedback conditions: 150 ms, 250 ms, and 350 ms delay. The order of feedback delay for each participant was randomised.

Working Memory Task

            Our WM task was provided by psytoolkit.org which includes participant instructions. Briefly, participants were informed that they would see a sequence of letters and to respond with a mouse click if they saw the same letter two trials ago. There was a practice block of 25 trials to familiarise participants with the task, followed by two experimental blocks of 25 trials each. A full overview of the procedure is detailed on their website.

Data Analysis

            Data were managed in Excel (Microsoft) and analysed using Python and SPSS (IBM). Working memory capacity was quantified using two measures from the N-back task: mean reaction time (milliseconds) and accuracy (percentage correct); participants made a mistake when they failed to respond to a match, or responded to a non-match. Faster reaction time and higher accuracy are both indicative of greater working memory capacity (Owen et al., 2005).

            We introduce a novel method of quantifying musical performance. Previous studies on musical performance under DAF have tended to quantify musical performance by counting discrete error events, e.g. the number of note deletions, additions, or substitutions (Finney, 1997), or by measuring the total time taken to play the musical excerpt (e.g. Gates & Bradshaw, 1974). The former method does not account for participants slowing down which would reduce the number of note errors, and the latter method does not account for fast but erroneous playing. In the present study, we quantify musical performance by generating note-time maps from the MIDI file of each performance, which we compared against note-time maps of perfect performance at the participant’s reference tempo. Musical performance was quantified as the percentage time of non-concordance between the two mappings, or put more simply, the percentage time of erroneous playing (%TEP). In contrast to previously used methods, %TEP generates a single continuous measure of musical performance that accounts for all error types. Table 1 demonstrates this procedure using simple fictional values.

Table 1
Quantification of Musical Performance

Perfect note-time map at participant reference tempoParticipant note-time mapDifference (ms)
NoteDuration (ms)NoteDuration (ms)
Percentage time of erroneous playing (%TEP) = 1500/4500 = 33%