About the theory: Multiple Memory Music Learning (MMML)

Multiple Memory Music Learning (MMML) is a framework that brings together the memory and cognition model, Multiple Memory Systems (MMS), and music learning.

Musicians perform numerous tasks at once, recruiting different areas of the brain to do so. How is that the case, when it is now commonly understood that multitasking is an impossibility (Madore & Wagner, 2019; Powell, 2016)?

While the research is nascent, there exists at least a proof-of-concept that potentially, playing a musical instrument allows for a fairly quick adaptation to the implicit memory system, thus freeing up the explicit memory system to perform decision-making tasks while carrying out physical music performance.

There are two crucial aspects with this. First of all, the notion that the implicit memory systems can learn quickly to the point of automaticity in physical performance is in opposition to the commonly-held view that automaticity takes a significant amount of practice - many thousands of hours.

Secondly, automaticity is not necessarily a good thing. For anyone who has learnt and/or is teaching music, you would have likely encountered the phenomenon of mistakes being made on a first run-through of music… only for the exact same mistake to happen again later, even after you have worked on it for some time (or your student).

Data from a grounded theory study involving beginner instrumental music students (aged 12-13) showed the following associations in relation to MMS:

MULTIPLE MEMORY MUSIC LEARNING:
MAIN THEMES

Automatic Music Learning
The phenomenon of short-term automaticity in instrumental music learning.

Contextual Music Learning
How various learning behaviors manifest in different learning environments

Music Learning Sequencing and Attentional Behaviors
The association between attentional behaviors and sequencing of learning events in instrumental music learning.

The Study

International Society of Music Educators (ISME) Preconference Presentation, Espoo, Finland, 2024

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