The Effect of Musical Learning on Cognitive Aptitude or Ability

Hetland’s review considers 15 independent experimental studies (701 participants) and was carried out in order to separate the distinct effects of passive musical listening (the so-called ‘Mozart Effect’) from active participation in musical instruction. These studies tested the hypothesis that learning to make music enhances spatial reasoning, as an extra-musical effect in pre- and elementary school students.

The first analysis included children aged three to twelve years who had been learning music for between six weeks and two years. This analysis included measures of spatial-temporal ability that relied on mental rotation without a visual or physical model, for example the Object Assembly subtest of the Wechsler Preschool and Primary Scale of Intelligence-Revised (Wechsler, 1989). Other tests of spatial-temporal ability, for example, Kaufman’s Assessment Battery for Children (Kaufman & Kaufman, 1983) and McCarthy’s Scales of Children’s Abilities; Puzzle Solving (McCarthy, 1972) were also included. According to Hetland (2000) the results of his meta-analysis showed “that

active music instruction lasting two years or less leads to dramatic improvements in performance on spatial-temporal measures” (p. 203). He remarks that the effect size

across studies is remarkably consistent (r = .37, equivalent d = .79) but suggests that the low reliability of the measures used7 _{means that the results should be interpreted with} caution. It appears Hetland is referring to the use of the discrete analysis of subtest data rather than the use of composite standardised scores. Hetland noted that the effect was stronger with younger children (aged three to five years; r = .44) than for older children (aged six to twelve years; r = .27). Hetland found that musical learning has an equal effect on spatial-temporal ability for both low and high socio-economic status (SES) groups and that the length of programme (of musical instruction) did not seem to affect the results. He further noted that one-to-one music lessons might lead to stronger spatial skills, but that group instruction was also effective. Similarly, musical notation learning also contributed to performance but was not essential for improvements on tasks of spatial- temporal ability. This is important because good performance on the Wechsler Object Assembly subtest (which the majority of studies used) relies on an ability to assemble coherently whole pictures from elements of the pictures (like a jigsaw). Some researchers

7 _{Here Hetland refers to his first analysis of 15 studies. He describes the measures specifically as}

spatial-temporal tasks requiring mental rotation, “and/or multiple solution steps for two or three- dimensional figures in the absence of a physical model” (Hetland, 2000, p. 183). These were measured in 9 studies using the Objects Assembly subtest of the WPPSI-R, the STAR-EP in two studies, the McCarthy Scales of Children’s Abilities – Puzzle Solving in one study, the Kaufman Assessment Battery for Children (Magic Window) in one study, the Developing Cognitive Abilities Test (Spatial subtests) in one study and finally the WRAMA-Matching subtest in one further study.

have suggested the mechanism underpinning far transfer effect reflects the learning of musical notation (Rauscher & Zupan, 2000) and/or the proximity of the brain regions responsible for music and spatial processing (Leng & Shaw, 1991; Shaw, 2000). Finally Hetland reported that his meta-analysis failed to reveal effects of parental involvement or type of instrument learned.

The second analysis included five studies (694 participants) using Raven’s Standard Progressive Matrices (RSPM; Raven, 1976). These tasks require the individual to fill in a matrix by figuring out how best to complete the pattern. They are regarded as measures of non-verbal intelligence rather than of spatial-temporal ability, which is why Hetland separated them for his analyses. This meta-analysis shows “in striking contrast” (Hetland, 2000, p. 218) to the first analysis, that music instruction does not lead to improvement on non-verbal intelligence as measured using RSPM (median r = .06, equivalent d = .11).

The third analysis included eight studies (655 participants) with “more loosely

defined” (Hetland, 2000, p. 219) concepts of spatial abilities and included aspects of

memory, visualisation and perception as a result of using tests such as the Stanford-Binet Bead Memory task (Thorndike, Hagen & Sattler, 1986), which requires spatial memory. Hetland reported that the results strongly suggested an effect of music instruction of other types of spatial tasks (median r = .31, equivalent d = .66).

Overall, Hetland’s meta-analyses provides strong evidence that music instruction increases some, but not all types of spatial ability and does not improve logical thinking associated with general non-verbal intelligence measured by RSPM. Hetland regards this as a “solid finding” and states that the “effect cannot be explained away by a Hawthorne

effect, nonequivalence of experimental groups, experimenter bias, or study quality”

(Hetland, 2000, p. 220). These meta-analyses formed the basis of The Handbook of Research and Policy in Art Education (Hetland & Winner, 2004).

Ruthsatz and colleagues (2008) challenged Hetland’s report, suggesting there was evidence of a positive association between musical ability and general intelligence. They cited Lynn and Gaults’ 1986 study, which showed a significant positive correlation between Wing’s Standardized Tests of Musical Intelligence (Wing, 1968) and Spearman’s g (as measured using Raven’s Progressive Matrices) in 93 children aged nine to eleven years. Lynn and Gault (1986) reported positive correlations for g and chord analysis (r = .27), pitch change (r = .40), and pitch memory (r = .37). Ruthsatz and colleagues also cited a review of 65 independent studies by Shuter (1968) that showed a

correlation between general intelligence and musical achievement of .35. Ruthsatz and colleagues (2008) claimed that the slightly older, higher-level child musicians in their study provided evidence of “significantly higher mean levels on innate characteristics

such as general intelligence and music audiation, in addition to higher levels of accumulated practice” (Ruthsatz et al., 2008, p. 330). They used the Raven’s tests and

Gordon’s Advanced Measures of Music Audiation (Gordon, 1989) as well as a questionnaire measuring practice times and found a significant relationship between those three independent variables. This led them to posit what they describe as a multi-factor view of the acquisition of skills underpinning musical expertise. However, their sample of 178 high school band members does not include a control comparison group. Nevertheless, they summarise their argument by recounting Seashore’s (1919) quote:

“It is possible for a person, strong in other capacities, but with relatively low intellectual power, to assume fairly important roles in music within restricted fields of activity; but the great musician is always a person of great intellect”.

(Ruthsatz et al., 2008, p. 331)

Whilst this serves to illustrate one particular standpoint or view of the debate, there are several studies that have investigated changes in IQ scores in children that are the same age as the participants in this study. The first of these (Costa-Giomi, 1999) was a longitudinal study following 117 nine-year-old children over a period of three years. The study utilised random allocation of instrumental teaching in its design, although the recruitment process specified that the sample only included children and families who had never learned a musical instrument before and did not have a piano in the home. The ethical agenda of the study also specified a criteria that family income was lower than $40,000 (Canadian)8_{. Only 78 children completed all the cognitive abilities tests, which} included five standardised tests. These were described as Level E of the Developing Cognitive Abilities Tests (Wick, 1990), the tonal and rhythmic audiation tests of the Musical Aptitude Profile (Gordon, 1965; 1988), the fine motor subtests of the Bruinicks- Oseretsky Test of Motor Proficiency (Bruinicks & Oseretsky 1978), the language and mathematics subtests Level 14 of the Canadian Achievement Test (CAT2; 1992), and the long form of the Coopersmith Self-Esteem Inventories (Coopersmith, 1981). Measures were taken before the programme of musical learning began and at the end of the first, second and third years of instruction. Instruction involved individual weekly lessons (30

8 _{The ethical reasoning was based on a study by Duke et al., (1997), which found that children in} the US learning piano were from a privileged environment. Specifically they found 80% Caucasian, 84% lived at home with both parents, 70% were female and only 18% had a family income under $40,000 USD.

minutes for the first two years and 45 minutes in the final year) either during lunchtime or after school based on a traditional curriculum including basic techniques and both popular and classical repertoires. Analyses were carried out on the “total cognitive abilities

score” (Costa-Giomi, 1999, p. 203). No rationale is given for this although analyses were

carried out on what is described as “verbal, quantitative and spatial” (Costa-Giomi, 1999, p. 204) aspects of the tests. After two years of piano training, children in this active group showed improved general cognitive abilities and spatial abilities with a small effect size (d = .2) in comparison to the no-treatment control group. However, no differences between groups were found following the third year, suggesting any inferred advantage might have been temporary. Costa-Giomi explains the conflicting evidence (between years) by proposing that “the positive effects of the treatment were dependent upon

children’s dedication to learning piano” (Costa-Giomi, 1999, p. 208) which she deduced

from qualitative progress reports produced by the teachers. She also explains this evidence led her to believe that there was an initial enthusiasm for the project which may have waned, especially as participants reached puberty. Although she suggests that hormonal changes may have influenced the results from the adolescents, unfortunately no biological data (such as testosterone which has been suggested is related to artistic talent, see Hassler et al., 1985; Hassler, 1992) were taken. Furthermore, no analyses investigating differences between sexes were undertaken.

Building on this earlier research, Schellenberg proposed that the combination of different experiences music lesson provides could have “collateral benefits that extend to

non-musical areas of cognition” (Schellenberg, 2004, p. 511). In his study 132

participants were randomly allocated into (1) standard keyboard, (2) Kodàly voice, (3) drama or (4) no lesson groups. Six year old children were selected because “music and

drama instructors consider children of this age to be sufficiently mature for formal lessons, and because plasticity declines in older children” (Schellenberg, 2004, p. 512).

Schellenberg cites the acquisition of perfect pitch before the age of seven as his primary justification for this observation. Participants were recruited via an advert in the local community newspaper offering free arts lessons and then randomly allocated group assignment. Lessons were taught for a period of 36 weeks, facilitated by a number of certified music and drama teachers associated with the Royal Conservatory of Music in Toronto. This ensured group sizes of no more than six children per group.

The measures used were Wechsler’s Intelligence Scale for Children (WISC-III, Wechsler, 1991), Kaufman Test of Educational Achievement (K-TEA: Kaufman & Kaufman, 1985), and the Parent Rating Scale of the Behavioural Assessment System for

Children (BASC9_{, Reynolds & Kamphaus, 1992). The WISC-III provided a full-scale IQ} score and four index scores including verbal comprehension, perceptual organisation, freedom from distractibility and processing speed. The twelve subtests used were picture completion, information, coding, similarities, picture arrangement, arithmetic, block design, vocabulary, object assembly, comprehension, symbol search and digit span. The K-TEA comprised of five subtests, mathematical applications, reading decoding, spelling, reading comprehension and mathematical computation.

Schellenberg reported finding “relatively modest but widespread intellectual

benefits from taking music lessons” (Schellenberg, 2004, p. 513) with an average increase

of 7 IQ points (SD 8.3) for the musical training group (MT) in comparison with an increase of 4.3 (SD 7.3) IQ points for the control groups. The increase for the MT group is reported as being significantly larger than that of the control group with an effect size between small and medium (d = .35). The description of analyses included a justification for increasing power by collapsing the data from both the musical training (Kodàly voice and keyboard) and control (drama or no extra lesson) groups. According to Schellenberg the groups showed similar levels of IQ increase and this justified the collapse of the data from the groups.

Schellenberg also reports that some index scores (freedom from distractibility and processing speed) showed higher increases than others (verbal comprehension and perceptual organization) and that these increases were significantly larger for the MT group than for the control group. Similarly, for all but two of the twelve subtests (arithmetic and information), larger increases were reported for the MT group in comparison to the control group, though these data are not reported specifically. In the discussion section of this paper and in a later paper (2006), Schellenberg argues that whilst the results from his 2004 study did provide evidence for a far transfer effect, methodological limitations in the study meant that effects from other extra-curricular activities could not be fully ruled out. He suggests that extra-curricular activities (such as chess), which requires focused attention, memorisation, and the progressive mastery of a technical skill, essentially functions as extra schooling or intellectual enrichment (Ceci & Williams, 1997). In a personal communication, that took place between this author and Dr. Schellenberg at the International Conference for Music Perception and Cognition (2012), Schellenberg reiterated his belief that the results from his studies reflected the socio-economic advantage of the participants, who were recruited via a network of highly educated academics (see also Corrigall & Schellenberg, 2015). For this reason, great care

9 _{The BASC-2 will be discussed in Chapter Five as it forms part of the battery of tests used in this}

was taken to ensure that the children in the current study were drawn from a range of schools and that SES was as balanced as possible within the sample. Furthermore, data on the children’s extra-curricular activities, such as sports clubs, hobbies and arts and crafts as well as music were collected in order to control for these potential confounds.

It is unfortunate that that the original Schellenberg study did not include a measure of musical aptitude. However, questions concerning whether pre-existing differences might potentially afford an advantage for musical learning, and consequently affect training effects, were addressed by Norton and colleagues in 2005. In a cross- sectional design, these authors studied 70 children aged between five and seven years using structural magnetic resonance imaging scans. They compared participants at baseline who were about to start learning either stringed instruments or piano, with a group of children who only studied music in class lessons. The behavioural measures probed cognitive, motor and auditory skills using object assembly, block design, vocabulary, Gordon’s PMMA (Gordon, 1986), Raven’s Matrices (Raven, 1976a and b; 1998) and an auditory analysis test developed by Rosner and Simon (1971). This auditory analysis test required children to repeat back 40 words that had a missing syllable. For example, the child was asked to say smell without the ‘m’ or cowboy without the boy. Children also completed a right and left finger-tapping test (Peters & Durding, 1979) where they were asked to make as many taps possible in 20 seconds. Of the 70 children recruited, 39 were about to begin individual extracurricular music lessons (30 minutes per week) on either a keyboard or a stringed instrument in addition to their classroom based group music lessons. The remaining 31 children would receive the classroom based group music lessons only. The first aim of this study by Norton and colleagues (2005) was to investigate whether any pre-existing differences were observable in the brain structures of the participating children, bearing in mind their intention regarding music lessons. Searches based on previous evidence regarding neural adaptations associated with musical training found no voxel-by-voxel differences between white or grey matter concentrations in the brain. No pre-existing differences were found between the participants and they were therefore considered as a typically developing group of children. Initial analyses showed that the musical training group participants were slightly older and also had a higher reported mean SES. Following the musical training period, the results showed that age significantly affected performance with higher scores observed on all outcomes except the object assembly and block design scores. SES appeared to only affect the vocabulary tests where higher scores were observed in the higher SES group. The oldest children in the instrumental group were then removed from further analyses to create an age-matched sample. SES remained a factor but was controlled for in analyses though neither ANOVA nor ANCOVAs subsequently revealed

any significant difference between groups. After correcting for multiple comparisons, only the Raven’s Matrices and Auditory Analysis test were correlated with Gordon’s PMMA. However the authors only reported the composite score so the relationship between specific cognitive and musical variables are not specified. No other correlations were found between performances on the cognitive, behavioural or auditory measures. As this study was published in 2005, it was not included in the meta-analysis performed by Hetland in 2000.

Forgeard et al. (2008) studied 59 children aged between eight and eleven years old, divided into three experimental groups. The first group included 21 children who had received a minimum of three years musical instruction on a mixture of instruments. All the children in this group received instruction in reading musical notation. The second group of 20 children had learned via the Suzuki method. This way of teaching and learning emphasises playing ‘by ear’ initially and musical notation reading is only introduced at later stages. The third group of 18 children received only the statutory music class lessons (which all participants also received). This group was classed as an active control group. Initial analyses revealed that the children in the instrumental groups were older than the children in the control groups so this factor was co-varied in the analyses. However, no differences were revealed between groups for SES or sex, or between outcomes for the two different types of instrumental groups. Therefore the two musical learning groups were collapsed into one instrumental group for further analyses. The outcome measures used in the study included Gordon’s Intermediate Measure of Musical Aptitude (IMMA; Gordon, 1986) as well as other melodic and rhythmic discrimination tasks and Rosner & Simons’ (1971), auditory analysis task, motor learning task (as used in Norton et al., 2005) and cognitive tasks such as block design and object assembly from WISC-III (Wechsler, 1991) and Raven’s progressive matrices (1976a). The findings showed that the instrumental group outperformed the control group on two of the near transfer tasks, specifically the left and right hand motor tapping task and the tonal component of the IMMA. Similarly, the collapsed instrumental group outperformed the active control group on the three of the ‘distantly associated’ (far transfer) tasks. These were the tests of vocabulary (WISC-III; Wechsler, 1991) and Raven’s standard matrices (Raven, 1976a) and advanced matrices (Raven, Raven & Court, 1998). However, the instrumental and control groups did not differ on measures of rhythm discrimination, object assembly, block design or Raven’s coloured matrices (Raven, 1976b), nor on the measure of auditory analysis.

In order to determine the effects of training duration, a series of multiple regressions were carried out where the control group were entered as zero weeks training.

The results provided a model whereby, when controlling for age, training duration could predict the outcome for motor learning with the left and right hands, for Gordon’s IMMA tonal tests and the melodic discrimination test as measure of near transfer. The model could also predict the outcomes for vocabulary (partial r2 _{= .09, p = .02) and Raven’s} advanced progressive matrices (having removed one outlier whose scored 2 SD below mean on all the Raven’s tests) as significant for Raven’s coloured (partial r2 = .13, p = .01), standard (partial r2 _{= .10, p = .02) and advanced (partial r}2 _{= .12, p = .01) as far} transfer outcomes. Forgeard and colleagues had criticised research that suggests an effect of far transfer when no measure of near transfer, for example, in the parent domain, has been included. Their study was the first to provide evidence of a relationship between the two.

Whilst the Forgeard and colleagues’ study provided evidence regarding the inter- related development of cognitive and behavioural skills due to musical learning, these results were reported as being due to the effects of three years of training on two types of musical instrument (strings and keyboards) in a cross-sectional rather than longitudinal study. However, Hyde et al., (2009) reported the results of a longitudinal study in which structural brain changes that correlated with improvement in musically relevant motor