Department of Music, Federal University of Santa Maria, Santa Maria, RS, Brazil.
School of Public Health, The University of Hong Kong, Hong Kong, SAR, China.
PLoS One. 2021 Feb 22;16(2):e0247473. doi: 10.1371/journal.pone.0247473. eCollection 2021.
Absolute Pitch (AP) is commonly defined as a rare ability that allows an individual to identify any pitch by name. Most researchers use classificatory tests for AP which tracks the number of isolated correct answers. However, each researcher chooses their own procedure for what should be considered correct or incorrect in measuring this ability. Consequently, it is impossible to evaluate comparatively how the stimuli and criteria classify individuals in the same way. We thus adopted a psychometric perspective, approaching AP as a latent trait. Via the Latent Variable Model, we evaluated the consistency and validity for a measure to test for AP ability. A total of 783 undergraduate music students participated in the test. The test battery comprised 10 isolated pitches. All collected data were analyzed with two different rating criteria (perfect and imperfect) under three Latent Variable Model approaches: continuous (Item Response Theory with two and three parameters), categorical (Latent Class Analysis), and the Hybrid model. According to model fit information indices, the perfect approach (only exact pitch responses as correct) measurement model had a better fit under the trait (continuous) specification. This contradicts the usual assumption of a division between AP and non-AP possessors. Alternatively, the categorical solution for the two classes demonstrated the best solution for the imperfect approach (exact pitch responses and semitone deviations considered as correct).
绝对音高(AP)通常被定义为一种罕见的能力,使个体能够通过名称识别任何音高。大多数研究人员使用分类测试来追踪孤立正确答案的数量,以评估绝对音高。然而,每个研究人员在测量这种能力时,都有自己选择正确或错误的标准。因此,不可能以相同的方式比较评估刺激和标准对个体的分类。因此,我们采用心理计量学的观点,将绝对音高视为潜在特征。通过潜在变量模型,我们评估了测试绝对音高能力的测量方法的一致性和有效性。共有 783 名音乐专业大学生参与了测试。测试包括 10 个孤立的音高。根据两种不同的评分标准(完美和不完美),在三种潜在变量模型方法(连续模型、二参数和三参数的项目反应理论、类别模型、混合模型)下,对所有收集的数据进行了分析。根据模型拟合信息指标,在特质(连续)规格下,完美方法(仅将精确音高反应视为正确)的测量模型具有更好的拟合度。这与将绝对音高和非绝对音高拥有者区分开来的通常假设相矛盾。相反,对于两个类别的分类解决方案,对于不完美方法(将精确音高反应和半音偏差视为正确),最佳解决方案是类别解决方案。