Ethnicity versus early environment: Comment on ‘Early Childhood Music Education and Predisposition to Absolute Pitch: Teasing Apart Genes and Environment’ by Peter K. Gregersen, Elena Kowalsky, Nina Kohn, and Elizabeth West Marvin [2000]
Bibliographic record
Abstract
Absolute pitch (AP)—the ability to identify or produce a musical note in the absence of a reference note—is very rare and is the subject of considerable speculation [cf. Ward, 1999]. In a widely quoted study, Gregersen et al. 2000 reported findings indicating a higher prevalence of AP among Asians than Caucasians, and they argued from their findings that ethnicity is a predisposing factor in the acquisition of AP. We here present a reanalysis of the data obtained by Gregersen et al., and argue that their conclusion concerning ethnicity is unwarranted. Instead, taking those respondents with early childhood in the North American Continent, we found no significant difference between East Asians1 and Caucasians in the prevalence of AP. Further, for East Asians with early childhood in East Asia, the prevalence of AP was significantly higher than for both East Asians and Caucasians with early childhood in the North American Continent. This pattern of results argues strongly that some environmental factor is responsible for the differences in the prevalence of AP that were obtained in their survey. Gregersen et al. 2000 surveyed music students who were enrolled in music theory classes in the United States. They state: ‘There was a marked increase in the rate of AP among Asian students (42/802; 47.5%) compared with Caucasian students (75/834; 9.0%). The relatively higher rate in Asians was present among all the major ethnic subgroups—Japanese (26% AP+), Korean (37% AP+), and Chinese (65% AP+)…’ adding that there was no significant difference between the Asian and Caucasian respondents in prevalence of early musical training. Their report has been widely interpreted as demonstrating a higher prevalence of AP among individuals of Asian descent, based on genetic factors. For example, Zatorre 2003, citing this report as evidence, wrote: ‘The second hint of a genetic factor is that AP may be differentially distributed across different human populations, with persons of Asian descent, for example, having a much greater incidence of AP than those of other backgrounds… the higher incidence has been reported among Asian-Americans who often speak only English.’ However, Gregersen et al. omitted to state that the large majority of the Asian respondents had designated an Asian country as their ‘country of early music education,’ and so had presumably spent their early childhood in Asia. In our reanalysis we grouped separately those respondents who had designated their ethnicity as Caucasian on the one hand, and Chinese, Japanese, or Korean (hereafter referred to as East Asian) on the other, and we analyzed separately the data from those with early childhood in the U.S. or Canada (hereafter referred to as the North American Continent) on the one hand, and in East Asia on the other. Table I displays the numbers of respondents in each of these groups and subgroups, together with the numbers of those who stated that they possessed AP. Considering respondents with early childhood in the North American Continent, we noted that there was no significant difference between East Asians and Caucasians in prevalence of AP (P > 0.10) (All tests were Fisher exact Probability Tests (two-tailed)). This lack of significance persisted when comparison was made between the East Asians with early childhood in the North American Continent and the other Caucasian respondents; that is, including those with early childhood in Europe, Australia, and elsewhere (P > 0.10). However, East Asians with early childhood in East Asia had a significantly higher prevalence of AP than did Caucasians with early childhood in the North American Continent (P < 0.0001). Further, this difference was significant for each of the East Asian subgroups taken separately (Asian Chinese vs. North American Caucasians (P < 0.0001); Asian Japanese vs. North American Caucasians (P = 0.02); Asian Koreans vs. North American Caucasians (P < 0.0001)). In addition, East Asians with early childhood in East Asia had a significantly higher prevalence of AP than did East Asians with early childhood in the North American Continent (P < 0.02). The question then arises that why the East Asians with early childhood in East Asia showed a higher prevalence of AP than did the other groups. As Gregersen et al. point out, type of early music education might be a factor; however, in comparing respondents with early musical education of the fixed do type rather than moveable do type (the factor most likely to influence the possession of AP), the authors showed that this could not explain their pattern of results. We have argued elsewhere [Deutsch, 2002; Deutsch et al., 2004, 2006] that exposure to tone language in infancy, such as Mandarin and Cantonese, can predispose the individual to acquire AP. In tone languages, words take on entirely different meanings depending on the pitches in which they are enunciated [Yip, 2002]. It is interesting to note that in the study of Gregersen et al. 2000, the prevalence of AP was higher as a trend among the Chinese groups than among the Korean or Japanese groups. However, it should also be noted that the Japanese language and certain dialects of the Korean language (specifically those from the Kyengsang and Hamkyeng provinces) are pitch accent languages, in which pitch also plays a role in attributing lexical meaning [Tsujimura, 1996; Sohn, 1999]. Exposure to these languages in infancy could therefore also play a role in creating a predisposition to acquire AP. In summary, on re-examining the data from Gregersen et al. 2000, we found that, among East Asians with early childhood in the North American Continent, the prevalence of AP did not differ significantly from its prevalence among Caucasians with early childhood in the North American Continent, or among all Caucasian respondents taken together. However, there was a much larger prevalence of AP among East Asians with early childhood in East Asia relative to the other groups. Based on this reanalysis, the conclusion by Gregersen et al. that ethnicity is a predisposing factor in the acquisition of AP is unwarranted; instead their data point to an environmental factor as a strong determinant of the predisposition to acquire AP. We thank Elizabeth Marvin for providing us with the data on which the present reanalysis is based.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.043 | 0.042 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".