ArticlePloS one2020
Revisiting discrete versus continuous models of human behavior: The case of absolute pitch.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Conceptual coherence but methodological mayhem: A systematic review of absolute pitch phenotyping.Behavior research methods · 2025Pooled it
- Short-term pitch memory predicts both incidentally and intentionally acquired absolute pitch categories.Memory & cognition · 2025Article
- Learning fast and accurate absolute pitch judgment in adulthood.Psychonomic bulletin & review · 2025Article
- Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence.Communications psychology · 2025Article
- Article
- Absolute pitch judgments of familiar melodies generalize across timbre and octave.Memory & cognition · 2023Article
- Generalizing across tonal context, timbre, and octave in rapid absolute pitch training.Attention, perception & psychophysics · 2023Article
- Use of explicit priming to phenotype absolute pitch ability.PloS one · 2022Article
- Individual differences in human frequency-following response predict pitch labeling ability.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many human behaviors are discussed in terms of discrete categories. Quantizing behavior in this fashion may provide important traction for understanding the complexities of human experience, but it also may bias understanding of phenomena and associated mechanisms. One example of this is absolute pitch (AP), which is often treated as a discrete trait that is either present or absent (i.e., with easily identifiable near-perfect "genuine" AP possessors and at-chance non-AP possessors) despite emerging evidence that pitch-labeling ability is not all-or-nothing. We used a large-scale online assessment to test the discrete model of AP, specifically by measuring how intermediate performers related to the typically defined "non-AP" and "genuine AP" populations. Consistent with prior research, individuals who performed at-chance (non-AP) reported beginning musical instruction much later than the near-perfect AP participants, and the highest performers were more likely to speak a tonal language than were the lowest performers (though this effect was not as statistically robust as one would expect from prior research). Critically, however, these developmental factors did not differentiate the near-perfect AP performers from the intermediate AP performers. Gaussian mixture modeling supported the existence of two performance distributions-the first distribution encompassed both the intermediate and near-perfect AP possessors, whereas the second distribution encompassed only the at-chance participants. Overall, these results provide support for conceptualizing intermediate levels of pitch-labeling ability along the same continuum as genuine AP-level pitch labeling ability-in other words, a continuous distribution of AP skill among all above-chance performers rather than discrete categories of ability. Expanding the inclusion criteria for AP makes it possible to test hypotheses about the mechanisms that underlie this ability and relate this ability to more general cognitive mechanisms involved in other abilities.
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