Attention, Perception, & Psychophysics

, Volume 81, Issue 1, pp 344–357 | Cite as

Inducing musical-interval learning by combining task practice with periods of stimulus exposure alone

  • David F. Little
  • Henry H. Cheng
  • Beverly A. Wright


A key component of musical proficiency is the ability to discriminate between and identify musical intervals, or fixed ratios between pitches. Acquiring these skills requires training, but little is known about how to best arrange the trials within a training session. To address this issue, learning on a musical-interval comparison task was evaluated for two four-day training regimens that employed equal numbers of stimulus presentations per day. A regimen of continuous practice yielded no learning, but a regimen that combined practice and stimulus exposure alone generated clear improvement. Learning in the practice-plus-exposure regimen was due to the combination of the two experiences, because two control groups who received only either the practice or the exposure from that regimen did not learn. Posttest performance suggested that this improvement in comparison learning generalized to an untrained stimulus type and an untrained musical-interval identification task. Naïve comparison performance, but not learning, was better for larger pitch-ratio differences and for individuals with more musical experience. The reported benefits of the practice-plus-exposure regimen mirror the outcomes for fine-grained discrimination and speech tasks, suggesting that a general learning principle is involved. In practical terms, it appears that combining practice and stimulus exposure alone is a particularly effective configuration for improving musical-interval perception.


Perceptual learning Music cognition Sound recognition Psychoacoustics 


  1. Aberg, K. C., & Herzog, M. H. (2009). Interleaving bisection stimuli—randomly or in sequence—does not disrupt perceptual learning, it just makes it more difficult. Vision Research, 49, 2591–2598. doi: CrossRefGoogle Scholar
  2. Ahissar, M., & Hochstein, S. (2004). The reverse hierarchy theory of visual perceptual learning. Trends in Cognitive Sciences, 8, 457–464. doi: CrossRefGoogle Scholar
  3. Arenson, M. A. (1984). Computer-based instruction in musicianship training: Some issues and answers. Computers and the Humanities, 18, 157–163.CrossRefGoogle Scholar
  4. Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178. doi: CrossRefGoogle Scholar
  5. Banai, K., Ortiz, J. A., Oppenheimer, J. D., & Wright, B. A. (2010). Learning two things at once: Differential constraints on the acquisition and consolidation of perceptual learning. Neuroscience, 165, 436–444. doi: CrossRefGoogle Scholar
  6. Burns, E. M., & Campbell, S. L. (1994). Frequency and frequency-ratio resolution by possessors of absolute and relative pitch: Examples of categorical perception? Journal of the Acoustical Society of America, 96, 2704–2719.CrossRefGoogle Scholar
  7. Burns, E. M., & Ward, W. D. (1978). Categorical perception—phenomenon or epiphenomenon: Evidence from experiments in the perception of melodic musical intervals. Journal of the Acoustical Society of America, 63, 456–468.CrossRefGoogle Scholar
  8. Cleland, K. D., & Dobrea-Grindahl, M. (2013). Developing musicianship through aural skills: A holistic approach to sight singing and ear training. New York: Routledge.CrossRefGoogle Scholar
  9. Fahle, M., & Poggio, T. (2002). Perceptual learning. Cambridge: MIT Press.CrossRefGoogle Scholar
  10. Flege, J. E. (1995). Two procedures for training a novel second language phonetic contrast. Applied Psycholinguistics, 16, 425–442.Google Scholar
  11. Foxton, J. M., Brown, A. C. B., Chambers, S., & Griffiths, T. D. (2004). Training improves acoustic pattern perception. Current Biology, 14, 322–325. doi: CrossRefGoogle Scholar
  12. Fu, Q.-J., Galvin, J., Wang, X., & Nogaki, G. (2005). Moderate auditory training can improve speech performance of adult cochlear implant patients. Acoustics Research Letters Online, 6, 106–111.CrossRefGoogle Scholar
  13. Furby, V. J. (2016). The effects of peer tutoring on the aural skills performance of undergraduate music majors. Update: Applications of Research in Music Education, 34, 33–39.Google Scholar
  14. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 1, 515–534.CrossRefGoogle Scholar
  15. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models (Vol. 1). New York: Cambridge University Press.Google Scholar
  16. Gelman, A., Hill, J., & Yajima, M. (2012). Why We (Usually) Don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness, 5, 189–211.CrossRefGoogle Scholar
  17. Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y.-S. (2008). A weakly informative default prior distribution for logistic and other regression models. Annals of Applied Statistics, 2, 1360–1383. doi: CrossRefGoogle Scholar
  18. Gelman, A., Meng, X.-L., & Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica Sinica, 6, 733–760.Google Scholar
  19. Gelman, A., & Tuerlinckx, F. (2000). Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational Statistics, 15, 373–390.CrossRefGoogle Scholar
  20. Gilbert, C. D., & Sigman, M. (2007). Brain states: Top-down influences in sensory processing. Neuron, 54, 677–696. doi: CrossRefGoogle Scholar
  21. Hoffman, M. D., & Gelman, A. (2014). The no-U-turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15, 1593–1623.Google Scholar
  22. Karpinski, G. S. (2000). Aural skills acquisition: The development of listening, reading, and performing skills in college-level musicians. Oxford: Oxford University Press.Google Scholar
  23. Maidment, D. W., Kang, H., Gill, E. C., & Amitay, S. (2015). Acquisition versus consolidation of auditory perceptual learning using mixed-training regimens. PLoS ONE, 10, e0121953. doi: CrossRefGoogle Scholar
  24. McDermott, J. H., Keebler, M. V., Micheyl, C., & Oxenham, A. J. (2010). Musical intervals and relative pitch: Frequency resolution, not interval resolution, is special. Journal of the Acoustical Society of America, 128, 1943–1951. doi: CrossRefGoogle Scholar
  25. Moore, D. R., Rosenberg, J. F., & Coleman, J. S. (2005). Discrimination training of phonemic contrasts enhances phonological processing in mainstream school children. Brain and Language, 94, 72–85.CrossRefGoogle Scholar
  26. Naqib, F., Sossin, W. S., & Farah, C. A. (2012). Molecular determinants of the spacing effect. Neural Plasticity, 2012, 581291:1–8. doi: CrossRefGoogle Scholar
  27. Parkosadze, K., Otto, T. U., Malania, M., Kezeli, A., & Herzog, M. H. (2008). Perceptual learning of bisection stimuli under roving: Slow and largely specific. Journal of Vision, 8(1), 5. doi: CrossRefGoogle Scholar
  28. Pavlik, P. I., & Anderson, J. R. (2005). Practice and forgetting effects on vocabulary memory: An activation-based model of the spacing effect. Cognitive Science, 29, 559–586.CrossRefGoogle Scholar
  29. Pisoni, D. B., Aslin, R. N., Perey, A. J., & Hennessy, B. L. (1982). Some effects of laboratory training on identification and discrimination of voicing contrasts in stop consonants. Journal of Experimental Psychology: Human Perception and Performance, 8, 297–314. doi: Google Scholar
  30. Russo, F. A., & Thompson, W. F. (2005). The subjective size of melodic intervals over a two-octave range. Psychonomic Bulletin & Review, 12, 1068–1075. doi: CrossRefGoogle Scholar
  31. Seitz, A., & Watanabe, T. (2005). A unified model for perceptual learning. Trends in Cognitive Sciences, 9, 329–334. doi: CrossRefGoogle Scholar
  32. Seitz, A. R., & Dinse, H. R. (2007). A common framework for perceptual learning. Current Opinion in Neurobiology, 17, 148–153.CrossRefGoogle Scholar
  33. Seitz, A. R., Yamagishi, N., Werner, B., Goda, N., Kawato, M., & Watanabe, T. (2005). Task-specific disruption of perceptual learning. Proceedings of the National Academy of Sciences, 102, 14895–14900.CrossRefGoogle Scholar
  34. Strange, W., & Dittmann, S. (1984). Effects of discrimination training on the perception of /r–l/ by Japanese adults learning English. Perception & Psychophysics, 36, 131–145. doi: CrossRefGoogle Scholar
  35. Szpiro, S. F. A., Wright, B. A., & Carrasco, M. (2014). Learning one task by interleaving practice with another task. Vision Research, 101, 118–124.CrossRefGoogle Scholar
  36. Thompson, W. F. (2013). 4—Intervals and scales. In D. Deutsch (Ed.), The psychology of music (3rd) (pp. 107–140). Amsterdam: Academic Press.CrossRefGoogle Scholar
  37. Wayland, R. P., & Li, B. (2008). Effects of two training procedures in cross-language perception of tones. Journal of Phonetics, 36, 250–267.CrossRefGoogle Scholar
  38. Wright, B. A., Baese-Berk, M. M., Marrone, N., & Bradlow, A. R. (2015). Enhancing speech learning by combining task practice with periods of stimulus exposure without practice. Journal of the Acoustical Society of America, 138, 928–937. doi: CrossRefGoogle Scholar
  39. Wright, B. A., & Sabin, A. T. (2007). Perceptual learning: How much daily training is enough? Experimental Brain Research, 180, 727–736. doi: CrossRefGoogle Scholar
  40. Wright, B. A., Sabin, A. T., Zhang, Y., Marrone, N., & Fitzgerald, M. B. (2010). Enhancing perceptual learning by combining practice with periods of additional sensory stimulation. Journal of Neuroscience, 30, 12868–12877. doi: CrossRefGoogle Scholar
  41. Wright, B. A., & Zhang, Y. (2009). A review of the generalization of auditory learning. Philosophical Transactions of the Royal Society B, 364, 301–311.CrossRefGoogle Scholar
  42. Yi, H. G., & Chandrasekaran, B. (2016). Auditory categories with separable decision boundaries are learned faster with full feedback than with minimal feedback. Journal of the Acoustical Society of America, 140, 1332–1335.CrossRefGoogle Scholar
  43. Yotsumoto, Y., Chang, L.-H., Watanabe, T., & Sasaki, Y. (2009). Interference and feature specificity in visual perceptual learning. Vision Research, 49, 2611–2623. doi: CrossRefGoogle Scholar

Copyright information

© The Psychonomic Society, Inc. 2018

Authors and Affiliations

  1. 1.Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Communication Sciences and DisordersNorthwestern UniversityEvanstonUSA
  3. 3.Communication Sciences and Disorders, Knowles Hearing Center, Northwestern Institute for NeuroscienceNorthwestern UniversityEvanstonUSA

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