INTRODUCTION
IN this study, Gardner and Fiedler (2026) address an area not often investigated in harmony literature, focusing on loop-based chord progressions, an important staple of many popular music genres. They tested the ability of participants to recognize chord progressions by having them listen to a twice-repeated four-chord sequence, followed by four melodies with accompanying chords, tasking participants to select the melody-chord combination matching the originally heard sequence. In a second experiment this method was inverted, with participants first hearing the chords with melody and tasked with matching the corresponding chord sequence. The majority of participants performed well in each experiment, with musicians performing better than non-musicians (defined by any musical training).
MUSICIANSHIP
In the first experiment, although an ANOVA revealed a difference between self-identified participant groups and score identifying chord sequences, precise differences between these three groups (professional, amateur, non-musician) are not specified. Additional t-tests may provide further insight into whether this is just a difference between professional and non-musicians, or whether there are also differences between other comparisons. Figure 3 shows that professional and amateur musicians score relatively close to each other and differences would likely be non-significant (at least given this study and sample size), and there is a larger difference between amateurs and non-musicians. If this comparison between amateur and non-musicians were significant, it suggests that the ability to recognize chord sequences is acquired early on in music learning and may not necessarily require extensive studying.
Differences in results between the two experiments regarding performance of self-identified musicianship levels could be due to variability within a small sample size or (as the authors suggested) a difference in difficulty between the two tasks. This could be due to chord recognition being more difficult when the first progression is heard with a melody, possibly due to greater attention being given to the top-voice/melody than other voices/chords (Palmer & Holleran, 1994; Trainor et al., 2014). Participants first hearing chord+melody stimuli may be diverting cognitive resources to processing melody lines, even if they are consciously attempting not to, causing chord sequence recognition to be more difficult.
Additionally, it could be the case that self-identified musicianship does not accurately reflect one’s ability as much as one would prefer due to biases participants may hold toward or against their own abilities, or that participants may have a wide range of what they consider amateur vs. professional (Hallam & Prince, 2003). For example, a very accomplished musician may not consider themselves a professional due to not earning income through actively performing and teaching. Inversely, some may consider themselves professional since they earn a living through music but lack the precise abilities for the particular task of recognizing chord sequences.
Beyond self-reported musicianship, measuring one’s musical ability can also be quite difficult due to the many factors contributing to what we define as musicianship (Zhang et al., 2020). Musicians are not only incredibly varied from each other but can also be considered extremely varied between factors contributing to their own individual level of musicianship. A musician may be very skilled in a particular aspect of music but lack (or have no desire or need for) others, such as a player who is very skilled technically but lacks compositional skills or vice versa. In this experiment, players who have undergone formal ear training or a more informal learning (whether through playing a chordal instrument or high exposure to particular genres) of chords and chord sequences may perform better on the tasks than others. This might be measured through the Goldsmiths Musical Sophistication Index (Müllensiefen et al., 2014), particularly the “perceptual abilities” subscale.
GENRE SPECIALIZATION
This brings us to the interesting idea of genre specialization influencing performance on the tasks in these experiments. Participants that listen to music containing a greater variety of chord types and progressions (certain subgenres of jazz and classical music for example) may have more in-depth implicit knowledge about harmonic information compared to a listener who listens to songs containing less harmonic variation. Research has shown that harmonic expectancies can be influenced by genres (Vuvan & Hughes, 2019), so it is not unreasonable to speculate that those who listen to music with more harmonic content may outperform those who frequently listen to music where harmony is a less important factor. It could also be the case that among professional musicians, performance could vary greatly depending on the genre of music a participant is an expert in. In this case, not only is there implicit knowledge at work, but also explicit knowledge demands that come with specific subgenres. For example, certain jazz pianists and guitarists may outperform other professional musicians on these tasks due to the increased demand for harmonic knowledge within this genre. Neurophysiological studies have suggested that musicians of different genres do perceive and process chord sequences in different ways (Bianco et al., 2018; Przysinda et al., 2017), and these differences may contribute to varying performance on the tasks in this current set of experiments.
IMPLICIT LEARNING
One of the things I find most interesting about this article is that while musicians tended to perform better than non-musicians, the non-musicians still performed quite well on both tasks. This highlights the importance of implicit learning in understanding music. While non-musicians may not have the explicit knowledge to name which notes belong to a chord or the chords in each sequence, they are still able to recognize familiar sequences and differentiate between sequences to degrees well above chance, similar to how children learn to speak their native language by listening through daily exposure as opposed to learning grammar and explicit rules of language (Hannon & Trainor, 2007). The songs we hear in day-to-day life do indeed contribute to our learning and understanding of musical structure, even if this learning occurs subconsciously (Bigand & Poulin-Charronnat, 2006).
FUTURE DIRECTIONS
The current study uses a variety of chord progressions as stimuli and is a great starting point for this methodology. Future iterations of this paradigm could include a greater variety of chord types, chord progressions, difficulty levels, and investigation into performance as a function of genre specialization. It would be interesting to see how participants respond to progressions containing 7th chords, further extensions, and less commonly heard chord types. It could be that the performance gap between musicians and non-musicians widens as stimuli contain less conventional chord types.
Similarly, increasing the number and variety of chord progressions may also produce a larger difference between participant groups. With expanding progressions, an important aspect may be to maintain consistency in chord type alphabets so that participants don’t use an outlier chord as a cue for the correct response. For example, a first-heard consonant progression followed by three sequences of dissonant progressions makes it obvious to the listener that the single consonant progression is the only reasonable answer (this would also be true in reverse, with a dissonant progression followed by consonant progressions). Keeping chord type alphabets and progressions somewhat similar in each trial also allows for more difficult trials, such that the progression options participants choose from can be made quite similar to each other with minimal differences (for example, a I-vi-ii-V progression with an incorrect option of I-vi-IV-V may yield additional incorrect responses since there is only a 1 note difference between these two progressions). Increased difficulty in some trials may reduce the near-ceiling effect we see in professional musician responses, though this could be heavily dependent on the genre of music a particular musician specializes in.
NOTES
[1] Correspondence can be addressed to: Matthew Eitel, TEMPO Lab and Laboratory for Infant Studies, University of Toronto Scarborough, 1265 Military Trail, HW403A Toronto, Ontario M1C 1A4 Canada, matt.eitel@utoronto.ca.
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