Advertisement

Perceptual dimensions influence auditory category learning

  • Casey L. Roark
  • Lori L. HoltEmail author
Perceptual/Cognitive Constraints on the Structure of Speech Communication: In Honor of Randy Diehl

Abstract

Human category learning appears to be supported by dual learning systems. Previous research indicates the engagement of distinct neural systems in learning categories that require selective attention to dimensions versus those that require integration across dimensions. This evidence has largely come from studies of learning across perceptually separable visual dimensions, but recent research has applied dual system models to understanding auditory and speech categorization. Since differential engagement of the dual learning systems is closely related to selective attention to input dimensions, it may be important that acoustic dimensions are quite often perceptually integral and difficult to attend to selectively. We investigated this issue across artificial auditory categories defined by center frequency and modulation frequency acoustic dimensions. Learners demonstrated a bias to integrate across the dimensions, rather than to selectively attend, and the bias specifically reflected a positive correlation between the dimensions. Further, we found that the acoustic dimensions did not equivalently contribute to categorization decisions. These results demonstrate the need to reconsider the assumption that the orthogonal input dimensions used in designing an experiment are indeed orthogonal in perceptual space as there are important implications for category learning.

Keywords

Categorization Perceptual categorization and identification Audition 

Notes

Author Note

This research was supported by the National Institutes of Health (R01DC004674, T32-DC011499). The authors thank Christi Gomez for support in testing human participants.

References

  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.Google Scholar
  2. Ashby, F. G. (1992a). Multidimensional models of categorization. In Multidimensional models of perception and cognition (pp. 449–483). Retrieved from http://psycnet.apa.org/psycinfo/1992-98026-016
  3. Ashby, F. G. (1992b). Multivariate probability distributions. In Multidimensional models of perception and cognition (pp. 1–34).Google Scholar
  4. Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M. (1998). A neuropsychological theory of multiple systems in category learning. Psychological Review, 105(3), 442–481.  https://doi.org/10.1037/0033-295X.105.3.442 Google Scholar
  5. Ashby, F. G., & Maddox, W. T. (1990). Integrating information from separable psychological dimensions. Journal of Experimental Psychology: Human Perception and Performance, 16(3), 598–612.  https://doi.org/10.1037/0096-1523.16.3.598 Google Scholar
  6. Ashby, F. G., & Maddox, W. T. (1992). Complex decision rules in categorization: Contrasting novice and experienced performance. Journal of Experimental Psychology: Human Perception and Performance, 18(1), 50–71.  https://doi.org/10.1037/0096-1523.18.1.50 Google Scholar
  7. Ashby, F. G., & Maddox, W. T. (1993). Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology, 37, 372–400.  https://doi.org/10.1006/jmps.1993.1023 Google Scholar
  8. Ashby, F. G., & Maddox, W. T. (2005). Human category learning. Annual Review of Psychology, 56, 149–178.  https://doi.org/10.1146/annurev.psych.56.091103.070217 Google Scholar
  9. Ashby, F. G., & Maddox, W. T. (2011). Human category learning 2.0. Annals of the New York Academy of Sciences, 1224, 147–61.  https://doi.org/10.1111/j.1749-6632.2010.05874.x Google Scholar
  10. Ashby, F. G., Paul, E. J., & Maddox, W. T. (2011). COVIS. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches in categorization. Google Scholar
  11. Ashby, F. G., Queller, S., & Berretty, P. M. (1999). On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics, 61(6), 1178–1199.  https://doi.org/10.3758/BF03207622 Google Scholar
  12. Ashby, F. G., & Townsend, J. T. (1986). Varieties of perceptual independence. Psychological Review, 93(2), 154–179.Google Scholar
  13. Best, C. T., McRoberts, G. W., & Sithole, N. M. (1988). Examination of perceptual reorganization for nonnative speech contrasts: Zulu click discrimination by english-speaking adults and infants. Journal of Experimental Psychology: Human Perception and Performance, 14(3), 345–360.  https://doi.org/10.1037/0096-1523.14.3.345 Google Scholar
  14. Carré, R. (2009). Dynamic properties of an acoustic tube: Prediction of vowel systems. Speech Communication, 51(1), 26–41.  https://doi.org/10.1016/j.specom.2008.05.015 Google Scholar
  15. Chandrasekaran, B., Koslov, S. R., & Maddox, W. T. (2014). Toward a dual-learning systems model of speech category learning. Frontiers in Psychology, 5(July), 1–17.  https://doi.org/10.3389/fpsyg.2014.00825 Google Scholar
  16. Chandrasekaran, B., Yi, H.-G., & Maddox, W. T. (2014). Dual-learning systems during speech category learning. Psychonomic Bulletin & Review, 21, 488–95.  https://doi.org/10.3758/s13423-013-0501-5 Google Scholar
  17. Diehl, R. L. (2008). Acoustic and auditory phonetics: The adaptive design of speech sound systems. Philosophical Transactions of the Royal Society, B: Biological Sciences, 363(1493), 965–978.  https://doi.org/10.1098/rstb.2007.2153 Google Scholar
  18. Diehl, R. L., & Kluender, K. R. (1989). On the objects of speech perception. Ecological Psychology, 1(2), 121–144.  https://doi.org/10.1207/s15326969eco0102_2 Google Scholar
  19. Ell, S. W., Ashby, F. G., & Hutchinson, S. (2012). Unsupervised category learning with integral-dimension stimuli. The Quarterly Journal of Experimental Psychology, 65(8), 1537–1562.Google Scholar
  20. Fowler, C. A. (1989). Real objects of speech perception: A commentary on Diehl and Kluender. Ecological Psychology, 1(2), 145–160.  https://doi.org/10.1207/s15326969eco0102 Google Scholar
  21. Francis, A. L., Baldwin, K., & Nusbaum, H. C. (2000). Effects of training on attention to acoustic cues. Perception & Psychophysics, 62(8), 1668–1680.  https://doi.org/10.3758/BF03212164 Google Scholar
  22. Francis, A. L., & Nusbaum, H. C. (2002). Selective attention and the acquisition of new phonetic categories. Journal of Experimental Psychology: Human Perception and Performance, 28(2), 349–366.  https://doi.org/10.1037//0096-1523.28.2.349 Google Scholar
  23. Garner, W. R. (1974). The processing of information and structure. Hillsdale, NJ: Erlbaum.Google Scholar
  24. Garner, W. R. (1976). Interaction of stimulus dimensions in concept and choice processes. Cognitive Psychology, 8(1), 98–123.  https://doi.org/10.1016/0010-0285(76)90006-2 Google Scholar
  25. Garner, W. R. (1978). Selective attention to attributes and to stimuli. Journal of Experimental Psychology. General, 107(3), 287–308.  https://doi.org/10.1037/0096-3445.107.3.287 Google Scholar
  26. Goldstone, R. L. (1993). Feature distribution and biased estimation of visual displays. Journal of Experimental Psychology: Human Perception and Performance, 19(3), 564–579.  https://doi.org/10.1037/0096-1523.19.3.564 Google Scholar
  27. Goldstone, R. L. (1994). Influences of categorization on perceptual discrimination. Journal of Experimental Psychology: General, 123(2), 178–200.Google Scholar
  28. Goudbeek, M., Cutler, A., & Smits, R. (2008). Supervised and unsupervised learning of multidimensionally varying non-native speech categories. Speech Communication, 50(2), 109–125.  https://doi.org/10.1016/j.specom.2007.07.003 Google Scholar
  29. Goudbeek, M., Swingley, D., & Smits, R. (2009). Supervised and unsupervised learning of multidimensional acoustic categories. Journal of Experimental Psychology: Human Perception and Performance, 35(6), 1913–1933.  https://doi.org/10.1037/a0015781 Google Scholar
  30. Grau, J. W., & Kemler Nelson, D. G. (1988). The distinction between integral and separable dimensions: Evidence for the integrality of pitch and loudness. Journal of Experimental Psychology: General, 117(4), 347–370.Google Scholar
  31. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York: Wiley.Google Scholar
  32. Hillenbrand, J., Getty, L. A., Clark, M. J., & Wheeler, K. (1995). Acoustic characteristics of American English vowels. The Journal of the Acoustical Society of America, 97(5 Pt 1), 3099–3111.  https://doi.org/10.1121/1.411872 Google Scholar
  33. Holt, L. L., & Lotto, A. J. (2006). Cue weighting in auditory categorization: Implications for first and second language acquisition. The Journal of the Acoustical Society of America, 119(5), 3059.  https://doi.org/10.1121/1.2188377 Google Scholar
  34. Huang-Pollock, C. L., Maddox, W. T., & Karalunas, S. L. (2011). Development of implicit and explicit category learning. Journal of Experimental Child Psychology, 109(3), 321–35.  https://doi.org/10.1016/j.jecp.2011.02.002 Google Scholar
  35. Kemler, D. G., & Smith, L. B. (1979). Accessing similarity and dimensional relations: Effects of integrality and separability on the discovery of complex concepts. Journal of Experimental Psychology. General, 108(2), 133–150.  https://doi.org/10.1037/0096-3445.108.2.133 Google Scholar
  36. Kingston, J., Diehl, R. L., Kirk, C. J., & Castleman, W. A. (2008). On the internal perceptual structure of distinctive features: The [voice] contrast. Journal of Phonetics, 36(1), 28–54.  https://doi.org/10.1016/j.wocn.2007.02.001 Google Scholar
  37. Kuhl, P. K. (1991). Human adults and human infants show a “perceptual magnet effect” for the prototypes of speech categories, monkeys do not. Perception & Psychophysics, 50(2), 93–107.Google Scholar
  38. Liu, R., & Holt, L. L. (2015). Dimension-based statistical learning of vowels. Journal of Experimental Psychology: Human Perception and Performance, 41(6), 1783–1798.  https://doi.org/10.1037/xhp0000092 Google Scholar
  39. Macmillan, N. A., Kingston, J., Thorburn, R., Walsh Dickey, L., & Bartels, C. (1999). Integrality of nasalization and F1. II. Basic sensitivity and phonetic labeling measure distinct sensory and decision-rule interactions. The Journal of the Acoustical Society of America, 106(5), 2913–32.  https://doi.org/10.1121/1.428113 Google Scholar
  40. Maddox, W. T., & Ashby, F. G. (1993). Comparing decision bound and exemplar models of categorization. Perception & Psychophysics, 53(1), 49–70.  https://doi.org/10.3758/BF03211715 Google Scholar
  41. Maddox, W. T., & Ashby, F. G. (2004). Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioural Processes, 66(3), 309–32.  https://doi.org/10.1016/j.beproc.2004.03.011 Google Scholar
  42. Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29(4), 650–662.  https://doi.org/10.1037/0278-7393.29.4.650 Google Scholar
  43. Maddox, W. T., & Chandrasekaran, B. (2014). Tests of a dual-systems model of speech category learning. Bilingualism: Language and Cognition, 17(4), 709–728.  https://doi.org/10.1016/j.biotechadv.2011.08.021.Secreted Google Scholar
  44. Maddox, W. T., Chandrasekaran, B., Smayda, K., & Yi, H.-G. (2013). Dual systems of speech category learning across the lifespan. Psychology and Aging, 28(4), 1042–56.  https://doi.org/10.1037/a0034969 Google Scholar
  45. Maddox, W. T., & Dodd, J. L. (2003). Separating perceptual and decisional attention processes in the identification and categorization of integral-dimension stimuli. Journal of Experimental Psychology. Learning, Memory, and Cognition, 29(3), 467–480.  https://doi.org/10.1037/0278-7393.29.3.467 Google Scholar
  46. Maddox, W. T., Filoteo, J. V., Lauritzen, J. S., Connally, E., & Hejl, K. D. (2005). Discontinuous categories affect information-integration but not rule-based category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(4), 654–69. : https://doi.org/10.1037/0278-7393.31.4.654 Google Scholar
  47. Maddox, W. T., Molis, M. R., & Diehl, R. L. (2002). Generalizing a neuropsychological model of visual categorization to auditory categorization of vowels. Perception & Psychophysics, 64(4), 584–597.  https://doi.org/10.3758/BF03194728 Google Scholar
  48. McKinley, S. C., & Nosofsky, R. M. (1996). Selective attention and the formation of linear decision boundaries. Journal of Experimental Psychology. Human Perception and Performance, 22(2), 294–317.  https://doi.org/10.1037/0096-1523.24.1.339 Google Scholar
  49. McMurray, B., Aslin, R. N., & Toscano, J. C. (2009). Statistical learning of phonetic categories: Insights from a computational approach. Developmental Science, 12(3), 369–378.  https://doi.org/10.1111/j.1467-7687.2009.00822.x Google Scholar
  50. Melara, R. D., & Marks, L. E. (1990). Interaction among auditory dimensions: Timbre, pitch, and loudness. Perception & Psychophysics, 48(2), 169–178.  https://doi.org/10.3758/BF03207084 Google Scholar
  51. Morrison, R. G., Reber, P. J., Bharani, K. L., & Paller, K. A. (2015). Dissociation of category-learning systems via brain potentials. Frontiers in Human Neuroscience, 9(July), 1–11.  https://doi.org/10.3389/fnhum.2015.00389 Google Scholar
  52. Neuhoff, J. G. (2004). Ecological Psychoacoustics. (J. G. Neuhoff, Ed.). New York: Academic Press.Google Scholar
  53. Newell, B. R., Dunn, J. C., & Kalish, M. (2011). Systems of category learning. Fact or Fantasy? In Psychology of learning and motivation - Advances in research and theory (1st ed., Vol. 54, pp. 167–215). Elsevier Inc. 10.1016/B978-0-12-385527-5.00006-1Google Scholar
  54. Nosofsky, R. M. (1986). Attention, similarity, and the identification-categorization relationship. Journal of Experimental Psychology: General, 115(1), 39–57.Google Scholar
  55. Roark, C. L., & Holt, L. L. (2018). Task and distribution sampling affect auditory category learning. Attention, Perception & Psychophysics, 80(7), 1804–1822.  https://doi.org/10.3758/s13414-018-1552-5 Google Scholar
  56. Scharinger, M., Henry, M. J., & Obleser, J. (2013). Prior experience with negative spectral correlations promotes information integration during auditory category learning. Memory & Cognition, 41, 752–68.  https://doi.org/10.3758/s13421-013-0294-9 Google Scholar
  57. Smith, E. E., & Grossman, M. (2008). Multiple systems of category learning. Neuroscience and Biobehavioral Reviews, 32(2), 249–64.  https://doi.org/10.1016/j.neubiorev.2007.07.009 Google Scholar
  58. Smith, J. D., Beran, M. J., Crossley, M. J., Boomer, J., & Ashby, F. G. (2010). Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes, 36(1), 54–65.  https://doi.org/10.1037/a0015892 Google Scholar
  59. Stilp, C. E., Rogers, T. T., & Kluender, K. R. (2010). Rapid efficient coding of correlated complex acoustic properties. Proceedings of the National Academy of Sciences of the United States of America, 107(50), 21914–9.  https://doi.org/10.1073/pnas.1009020107 Google Scholar
  60. Wade, T., & Holt, L. L. (2005). Incidental categorization of spectrally complex non-invariant auditory stimuli in a computer game task. The Journal of the Acoustical Society of America, 118, 2618–2633.  https://doi.org/10.1121/1.2011156 Google Scholar
  61. Wickens, T. D. (1982). Models for behavior: Stochastic processes in psychology. San Francisco, CA: W. H. Freeman.Google Scholar

Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of PsychologyCarnegie Mellon University, and the Center for the Neural Basis of CognitionPittsburghUSA

Personalised recommendations