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Absolute pitch (AP), the rare ability to name any musical note without the aid of a reference note, is thought to depend on an early critical period of development. Although recent research has shown that adults can improve AP performance in a single training session, the best learners still did not achieve note classification levels comparable to performance of a typical, "genuine" AP possessor. Here, we demonstrate that these "genuine" levels of AP performance can be achieved within eight weeks of training for at least some adults, with the best learner passing all measures of AP ability after training and retaining this knowledge for at least four months after training. Alternative explanations of these positive results, such as improving accuracy through adopting a slower, relative pitch strategy, are not supported based on joint analyses of response time and accuracy. The results also did not appear to be driven by extreme familiarity with a single instrument or octave range, as the post-training AP assessments used eight different timbres and spanned over seven octaves. Yet, it is also important to note that a majority of the participants only exhibited modest improvements in performance, suggesting that adult AP learning is difficult and that near-perfect levels of AP may only be achievable by subset of adults. Overall, these results demonstrate that explicit perceptual training in some adults can lead to AP performance that is behaviorally indistinguishable from AP that manifests within a critical period of development. Implications for theories of AP acquisition are discussed.
Pubmed ID: 31550277
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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