Transfer of learning

Introduction

Recent advances in the study of the brain have enabled us to enhance our understanding of the way that active engagement with music influences other development. Although our knowledge of the way the brain works is still in its infancy some of the fundamental processes involved in learning have been established. The human brain contains approximately 100 billion neurons a considerable proportion of which are active simultaneously. Information processing is undertaken largely through interactions between them, each having approximately a thousand connections with other neurons. When we learn there are changes in the growth of axons and dendrites and the number of synapses connecting neurons, a process known as parthenogenesis.

When an event is important enough or is repeated sufficiently often synapses and neurons fire repeatedly indicating that this event is worth remembering (Fields, 2005). In this way changes in the efficacy of existing connections are made. As learning continues and particular activities are engaged with over time myelinisation takes place. This involves an increase in the coating of the axon of each neuron which improves insulation and makes the established connections more efficient. Pruning also occurs, a process which reduces the number of synaptic connections, enabling fine-tuning of functioning. Through combinations of these processes, which occur over different time scales, the cerebral cortex self-organises in response to external stimuli and the individual’s learning activities. Extensive active engagement with music induces cortical re-organisation producing functional changes in how the brain processes information. If this occurs early in development the alterations may become hard-wired and produce permanent changes in the way information is processed (e.g. Schlaug et al., 1995).

Permanent and substantial reorganisation of brain functioning takes considerable time. Long years of active engagement with particular musical activities in Western classical musicians are associated with an increase in neuronal representation specific for the processing of the tones of the musical scale, the largest cortical representations being found in musicians playing instruments for the longest periods of time (Pantev et al., 2003). Changes are also specific to the particular musical learning undertaken (Munte et al., 2003). Processing of pitch in string players is characterised by longer surveillance and more frontally distributed event-related brain potentials attention. Drummers generate more complex memory traces of the temporal organisation of musical sequences and conductors demonstrate greater surveillance of auditory space (Munte et al., 2003). Compared with non-musicians, string players have greater somatosensory representations of finger activity, the amount of increase depending on the age of starting to play (Pantev et al., 2003). Clearly, the brain develops in very specific ways in response to particular learning activities and the extent of change depends on the length of time engaged with learning. The extent of musical engagement and its nature will be important factors in the extent to which transfer can occur to non-musical activities.

The ways that we learn are also reflected in specific brain activity. When students (aged 13- 15) were taught to judge symmetrically structured musical phrases as balanced or unbalanced using traditional instructions about the differences (including verbal explanations, visual aids, notation, verbal rules, playing of musical examples), or participating in musical experiences (singing, playing, improvising or performing examples from the musical literature), activity in different brain areas was observed (Altenmuller et al., 1997). The tools and practices utilised to support the acquisition of particular musical skills have a direct influence on brain development and preferred approaches to undertaking musical tasks, also influencing approaches to tasks outside music. Musicians with similar observable skills may have developed different approaches to developing them which may or may not facilitate transfer to other tasks.

Each individual has a specific ‘learning biography’ which is reflected in the way the brain processes information (Altenmuller, 2003:349). As individuals engage with different musical activities over long periods of time permanent changes occur in the brain. These changes reflect what has been learned and how it has been learned. They will also influence the extent to which developed skills are able to transfer to other activities.

Transfer of learning

share cognitive processes. Transfer can be near or far and is stronger and more likely to occur if it is near. Salomon and Perkins (1989) refer to low and high road transfer. Low road transfer depends on automated skills and is relatively spontaneous and automatic, for instance, processing of music and language, using the same skills to read different pieces of music or text. High road transfer requires reflection and conscious processing, for instance, adopting similar skills in solving very different kinds of problems. Some musical skills are more likely to transfer than others. For instance, the musical skills more likely to transfer are those concerned with perceptual processing of sound (temporal, pitch, and rule governed grouping information), fine motor skills, emotional sensitivity, conceptions of relationships between written materials and sound (reading music and text), and memorisation of extended information (music and text) (Schellenberg, 2003; Norton et al., 2005). The aim of this paper is to consider what we know about the ways that transfer can occur in relation to the skills developed through active engagement with music and how they may impact on the intellectual, social and personal development of children and young people. The paper synthesises indicative research findings and considers the implications for education.

Perceptual and language skills

Music has long been argued to provide effective experiences for children to develop listening skills in mainstream schools and those for children with learning difficulties (HirtMannheimer, 1995; Wolf, 1992; Humpal and Wolf, 2003). Research is now able to offer explanations as to why this might occur. When we listen to music or speech we process an enormous amount of information rapidly without our conscious awareness (Blakemore and Frith, 2000). The ease with which we do this depends on our prior musical and linguistic experiences. This knowledge is implicit, learned through exposure to particular environments, and is applied automatically whenever we listen to music or speech. Speech and music share some processing systems. Musical experiences which enhance processing can therefore impact on the perception of language which in turn impacts on reading. Musical training sharpens the brain’s early encoding of sound leading to enhanced performance (Tallal and Gaab, 2006; Patel and Iverson, 2007) improving the ability to distinguish between rapidly changing sounds (Gaab et al. 2005), and enhancing auditory discrimination (Schlaug et al.,2005). This has an impact on the cortical processing of linguistic pitch patterns (Schon et al., 2004; Magne et al, 2006). The influence of musical training emerges quickly. Eight year old children with just 8 weeks of musical training differed from controls in their cortical event related potentials (ERPs) (Moreno and Besson, 2006). Flohr et al. (2000) provided music training for 25 minutes for 7 weeks for children aged 4-6 and compared measured brain activity with controls. Those children who had received musical training produced EEG frequencies associated with increased cognitive processing.

Playing a musical instrument triggers changes in the brainstem not only the cortex (Musacchia et al., 2007). Musicians have been found to have earlier brainstem responses to the onset of a syllable than non-musicians and those playing since the age of 5 have quicker responses and increased activity of neurons in the brain to both music and speech sounds. Musicians also have high-functioning peripheral auditory systems. The quality of sensory encoding is related to the amount of musical training (Wong et al., 2007).

Early studies found correlations between the performance of first grade children on tests of phonemic and musical pitch awareness. The ability to perceive slight differences in phonemes seemed to depend on the ability to extract information about the frequencies of the speech sounds (Lamb and Gregory, 1993). Recent studies have confirmed that having musical skills predicts the ability to perceive and produce subtle phonetic contrasts in a second language (Slevc and Miyake, 2006) and the reading abilities of children in their first language (Anvari et al., 2002). It also enhances the ability to interpret affective speech rhythms (Thompson et al. 2004). Speech makes extensive use of structural auditory patterns not based on pitch but timbre based differences between phonemes. Musical training seems to develop these skills.

Studies with pre-school children have found relationships between musical skills, the manipulation of speech sounds (Peynircioglu et al., 2002), and phonological awareness and reading development (Anvari et al., 2002). Gromko (2005) studied kindergarten children who received 4 months of music instruction for 30 minutes once per week. The instruction included active music-making and kinaesthetic movements to emphasise steady beat, rhythm and pitch as well as the association of sounds with symbols. The children who received the music instruction showed significantly greater gains in phonemic awareness when compared to the control group. Learning to discriminate differences between tonal and rhythmic patterns and to associate their perceptions with visual symbols seems to have transferred to improved phonemic awareness. Humans are able to recognise a melody transposed in frequency easily. This skill may be related to its importance in spoken intonation.

aA listener needs to be able to hear the similarity of intonation patterns when spoken in different pitch registers. Speech processing requires similar processing to melodic contour and is one of the first aspects of music to be discriminated by infants (Trehub et al., 1984).

The two seem to be processed by the same brain mechanisms (see Patel, 2009). Magne et al. (2006) compared 8 year old children who had musical training with those who did not and found that the musicians outperformed nonmusicians on music and language tests. The study showed that in the neural basis of development of prosodic and melodic processing pitch processing seemed to be earlier in music than in language. The authors concluded that there were positive effects of music lessons for linguistic abilities in children.

Overall, the evidence suggests that engagement with music plays a major role in developing perceptual processing systems which facilitate the encoding and identification of speech sounds and patterns, the earlier the exposure to active music participation and the greater the length of participation the greater the impact. Transfer of these skills is automatic and contributes not only to language development but also to literacy.