Perceptual-learning evidence for inter-onset-interval- and frequency-specific processing of fast rhythms
Rhythm is fundamental to music and speech, yet little is known about how even simple rhythmic patterns are processed. Here we investigated the processing of isochronous rhythms in the short inter-onset-interval (IOI) range (IOIs < 250–400 ms) using a perceptual-learning paradigm. Trained listeners (n=8) practiced anisochrony detection with a 100-ms IOI marked by 1-kHz tones, 720 trials per day for 7 days. Between pre- and post-training tests, trained listeners improved significantly more than controls (no training; n=8) on the anisochrony-detection condition that the trained listeners practiced. However, the learning on anisochrony detection did not generalize to temporal-interval discrimination with the trained IOI (100 ms) and marker frequency (1 kHz) or to anisochrony detection with an untrained marker frequency (4 kHz or variable frequency vs. 1 kHz), and generalized negatively to anisochrony detection with an untrained IOI (200 ms vs. 100 ms). Further, pre-training thresholds were correlated among nearly all of the conditions with the same IOI (100-ms IOIs), but not between conditions with different IOIs (100-ms vs. 200-ms IOIs). Thus, it appears that some task-, IOI-, and frequency-specific processes are involved in fast-rhythm processing. These outcomes are most consistent with a holistic rhythm-processing model in which a holistic “image” of the stimulus is compared to a stimulus-specific template.
KeywordsTemporal processing Perceptual learning Psychoacoustics
We thank Paul Reber and Sazzad Nassir for their helpful comments on a preliminary draft of this paper. This work was sponsored in part by NIH/NIDCD, by the Defense Advanced Research Projects Agency (DARPA) Biological Technologies Office (BTO) TNT program under the auspices of Dr. Doug Weber and Tristan McClure-Begley through the Space and Naval Warfare Systems Center, Pacific Grant/Contract No. N66001-17-2-4011, and by a Northwestern University Undergraduate Summer Research Grant.
Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA) Biological Technologies Office (BTO).
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.Google Scholar
- Bhatara, A., Tirovolas, A. K., Duan, L. M., Levy, B., & Levitin, D. J. (2011). Perception of emotional expression in musical performance. Journal of Experimental Psychology: Human Perception and Performance, 37(3), 921–934. https://doi.org/10.1037/a0021922
- Grahn, J. A. (2012). Neural mechanisms of rhythm perception: Current findings and future perspectives. Topics in Cognitive Science, 4(4), 585–606. https://doi.org/10.1111/j.1756-8765.2012.01213.x
- Henry, M. J., & McAuley, J. D. (2009). Evaluation of an imputed pitch velocity model of the auditory kappa effect. Journal of Experimental Psychology: Human Perception and Performance, 35(2), 551–564. https://doi.org/10.1037/0096-1522.214.171.1241
- Hibi, S. (1983). Rhythm perception in repetitive sound sequence. Journal of the Acoustical Society of Japan (E), 4(2), 83–95. https://doi.org/10.1250/ast.4.83
- Juslin, P. N., & Laukka, P. (2003). Emotional expression in speech and music. Annals of the New York Academy of Sciences, 1000(1), 279–282. https://doi.org/10.1196/annals.1280.025
- Kohno, M. (1992). Two mechanisms of processing sound sequences. In Speech Perception, Production and Linguistic Structure (pp. 287–293). Amsterdam, the Netherlands: IOS Press.Google Scholar
- London, J. (2012). Hearing in time: Psychological aspects of musical meter. Oxford, UK: Oxford University Press.Google Scholar
- Ravignani, A., & Madison, G. (2017). The paradox of isochrony in the evolution of human rhythm. Frontiers in Psychology, 8, 1820. https://doi.org/10.3389/fpsyg.2017.01820
- Ritter, F. E., & Schooler, L. J. (2001). The learning curve. In International Encyclopedia of the Social and Behavioral Sciences (Vol.13, pp. 8602–8605). Amsterdam, the Netherlands: Pergamon.Google Scholar
- Sussman, E. S., & Gumenyuk, V. (2005). Organization of sequential sounds in auditory memory. Neuroreport, 16(13), 1519–1523. https://doi.org/10.1097/01.wnr.0000177002.35193.4c CrossRefGoogle Scholar
- Thaut, M. (2013). Rhythm, Music, and the Brain: Scientific Foundations and Clinical Applications. New York, NY: Routledge. https://doi.org/10.4324/9780203958827
- Torchiano, M. (2017). effsize: Efficient effect size computation (R package version 0.7.1). Retrieved from https://CRAN.R-project.org/package=effsize
- Treisman, M. (1963). Temporal discrimination and the indifference interval: Implications for a model of the internal clock. Psychological Monographs: General and Applied, 77(13), 1–31. https://doi.org/10.1037/h0093864
- Wright, B. A., Buonomano, D. V., Mahncke, H. W., & Merzenich, M. M. (1997). Learning and generalization of auditory temporal-interval discrimination in humans. Journal of Neuroscience, 17(10), 3956–3963. https://doi.org/10.1523/JNEUROSCI.17-10-03956.1997 CrossRefGoogle Scholar