Components of Event-Related Potentials in studies of perceptual learning

442

Abstract

Perceptual learning is defined by increased effectiveness of completing perceptual tasks as a result of experience or training. This review presents the analysis of changes in the components of event-related potentials (ERPs) after visual and auditory perceptual learning in humans. The use of the EEG method, which has a high temporal resolution, makes it possible to trace the spatio-temporal dynamics of changes in the functioning of the brain during learning, which remains hidden in behavioral experimental studies. A review of neurophysiological studies indicates that perceptual learning induces changes across all levels of cortical hierarchy, starting with the early sensory components of ERPs (C1) and ending with the later integrative components (N170, MMN, P2). We also analyzed the short-term and long-term effects of learning. The reviewed neurophysiological data can serve as the basis for the development of new approaches of effective learning, as well as for the objective evaluation of existing methodics by assessing neuronal dynamics at different stages of stimuli processing.

General Information

Keywords: perceptual learning, event-related potentials, N1, N170, MMN, P2, expertise

Journal rubric: Cognitive Pedagogy

Article type: review article

DOI: https://doi.org/10.17759/jmfp.2020090203

Funding. The reported study was funded by Russian Foundation for Basic Research (RFBR), project number 19-313-51039.

For citation: Kleeva D.F., Rebreikina A.B., Sysoeva O.V. Components of Event-Related Potentials in studies of perceptual learning [Elektronnyi resurs]. Sovremennaia zarubezhnaia psikhologiia = Journal of Modern Foreign Psychology, 2020. Vol. 9, no. 2, pp. 34–45. DOI: 10.17759/jmfp.2020090203. (In Russ., аbstr. in Engl.)

References

  1. Ivanitskii A. Sintez informatsii v klyuchevykh otdelakh kory kak osnova sub"ektivnykh perezhivanii [Synthesis of information in key sections of the cortex as the basis of subjective experiences]. Zhurnal vysshei nervnoi deyatel'nosti = [Journal of Higher Nervous Activity], 1997. Vol. 47, no. 2, pp. 209–225. (In Russ.).
  2. Rourke L. et al. A neural marker of medical visual expertise: implications for training. Advances in Health Sciences Education, 2016. Vol. 21, no. 5, pp. 953–966. DOI:10.1007/s10459-016-9712-7
  3. Alain C., Campeanu S., Tremblay K. Changes in Sensory Evoked Responses Coincide with Rapid Improvement in Speech Identification Performance. Journal of Cognitive Neuroscience, 2010. Vol. 22, no. 2, pp. 392–403. DOI:10.1162/jocn.2009.21279
  4. Song Y. et al. An event-related potential study on perceptual learning in grating orientation discrimination. NeuroReport, 2007. Vol. 18, no. 9, pp. 945–948. DOI:10.1097/WNR.0b013e3281527795
  5. Atienza M., Cantero J.L., Dominguez-Marin E. The time course of neural changes underlying auditory perceptual learning. Learning & Memory, 2002. Vol. 9, no. 3, pp. 138–150. DOI:10.1101/lm.46502
  6. Wisniewski M.G. et al. Auditory detection learning is accompanied by plasticity in the auditory evoked potential. Neuroscience Letters, 2020. Vol. 721, 5 p. DOI:10.1016/j.neulet.2020.134781
  7. Baumann S., Meyer M., Jäncke L. Enhancement of Auditory-evoked Potentials in Musicians Reflects an Influence of Expertise but not Selective Attention. Journal of Cognitive Neuroscience, 2008. Vol. 20, no. 12, pp. 2238–2249. DOI:10.1162/jocn.2008.20157
  8. Bosnyak D.J., Eaton R.A., Roberts L.E. Distributed Auditory Cortical Representations Are Modified When Non-musicians Are Trained at Pitch Discrimination with 40 Hz Amplitude Modulated Tones. Cerebral Cortex, 2004. Vol. 14, no. 10, pp. 1088–1099. DOI:10.1093/cercor/bhh068
  9. Busey T.A., Vanderkolk J.R. Behavioral and electrophysiological evidence for configural processing in fingerprint experts. Vision Research, 2005. Vol. 45, no. 4, pp. 431–448. DOI:10.1016/j.visres.2004.08.021
  10. Dering B., Hoshino N., Theirry G. N170 modulation is expertise driven: evidence from word-inversion effects in speakers of different languages. Future trends in the biology of language, 2013. 16 p.
  11. Opitz B. et al. Differential Contribution of Frontal and Temporal Cortices to Auditory Change Detection: fMRI and ERP Results. NeuroImage, 2002. Vol. 15, no. 1, pp. 167–174. DOI:10.1006/nimg.2001.0970
  12. Eimer M. The face-sensitive N170 component of the event-related brain potential. In Calder A. [et al.] (eds.), The Oxford handbook of face perception. OUP Oxford, 2011, pp. 329–344.
  13. Shahin A. et al. Enhancement of Neuroplastic P2 and N1c Auditory Evoked Potentials in Musicians. The Journal of Neuroscience, 2003. Vol. 23, no. 13, pp. 5545–5552. DOI:10.1523/JNEUROSCI.23-13-05545.2003
  14. Zhang G.-L. et al. ERP C1 is top-down modulated by orientation perceptual learning. Journal of Vision, 2015. Vol. 15, no. 10, pp. 1–11 DOI:10.1167/15.10.8
  15. Orduña I. et al. Evoked-potential changes following discrimination learning involving complex sounds. Clinical Neurophysiology, 2012. Vol. 123, no. 4, pp. 711–719. DOI:10.1016/j.clinph.2011.08.019
  16. Gauthier I., Tarr M.J. Becoming a “Greeble” Expert: Exploring Mechanisms for Face Recognition. Vision Research, 1997. Vol. 37, no. 12, pp. 1673–1682. DOI:10.1016/S0042-6989(96)00286-6
  17. van Zuijen T.L. et al. Grouping of Sequential Sounds—An Event-Related Potential Study Comparing Musicians and Nonmusician. Journal of Cognitive Neuroscience, 2004. Vol. 16, no. 2, pp. 331–338. DOI:10.1162/089892904322984607
  18. Gottselig J.M. et al. Human Central Auditory Plasticity Associated With Tone Sequence Learning. Learning & Memory, 2004. Vol. 11, no. 2, pp. 162–171. DOI:10.1101/lm.63304
  19. Irvine D.R.F. et al. Irvine D.R.F. Auditory perceptual learning and changes in the conceptualization of auditory cortex. Hearing Research, 2018. Vol. 366, pp. 3–16. DOI:10.1016/j.heares.2018.03.011
  20. Tremblay K.L. et al. Is the auditory evoked P2 response a biomarker of learning? Frontiers in Systems Neuroscience, 2014. Vol. 8, article ID 28, 13 p. DOI:10.3389/fnsys.2014.00028
  21. Koelsch S., Schröger E., Tervaniemi M. Superior pre-attentive auditory processing in musicians. Neuroreport, 1999. Vol. 10, no. 6, pp. 1309–1313. DOI:10.1097/00001756-199904260-00029
  22. Kuriki S., Kanda S., Hirata Y. Effects of Musical Experience on Different Components of MEG Responses Elicited by Sequential Piano-Tones and Chords. Journal of Neuroscience, 2006. Vol. 26, no. 15, pp. 4046–4053. DOI:10.1523/JNEUROSCI.3907-05.2006
  23. Lütkenhöner B., Seither-Preisler A., Seither S. Piano tones evoke stronger magnetic fields than pure tones or noise, both in musicians and non-musicians. NeuroImage, 2006. Vol. 30, no. 3, pp. 927–937. DOI:10.1016/j.neuroimage.2005.10.034
  24. Mankel K., Bidelman G.M. Inherent auditory skills rather than formal music training shape the neural encoding of speech. Proceedings of the National Academy of Sciences, 2018. Vol. 115, no. 51, pp. 13129–13134. DOI:10.1073/pnas.1811793115
  25. Maurer U., Brandeis D., McCandliss B.D. Fast, visual specialization for reading in English revealed by the topography of the N170 ERP response. Behavioral and Brain Functions, 2005. Vol. 1, no. 13. 12 p. DOI:10.1186/1744-9081-1-13
  26. Shahin A. et al. Modulation of P2 auditory-evoked responses by the spectral complexity of musical sounds. NeuroReport, 2005. Vol. 16, no. 16, pp. 1781–1785. DOI:10.1097/01.wnr.0000185017.29316.63
  27. Fujioka T. et al. Musical Training Enhances Automatic Encoding of Melodic Contour and Interval Structure. Journal of Cognitive Neuroscience, 2004. Vol. 16, no. 6, pp. 1010–1021. DOI:10.1162/0898929041502706
  28. Kühnis J. et al. Musicianship boosts perceptual learning of pseudoword-chimeras: an electrophysiological approach. Brain Topography, 2013. Vol. 26, no. 1, pp. 110–125. DOI:10.1007/s10548-012-0237-y
  29. Virtala P. et al. Musicianship facilitates the processing of Western music chords—An ERP and behavioral study. Neuropsychologia, 2014. Vol. 61, pp. 247–258. DOI:10.1016/j.neuropsychologia.2014.06.028
  30. Nikjeh D.A., Lister J.J., Frisch S.A. Preattentive Cortical-Evoked Responses to Pure Tones, Harmonic Tones, and Speech: Influence of Music Training. Ear and Hearing, 2009. Vol. 30, no. 4, pp. 432–446. DOI:10.1097/AUD.0b013e3181a61bf2
  31. Civile C. et al. Perceptual learning and inversion effects: Recognition of prototype-defined familiar checkerboards. Journal of Experimental Psychology: Animal Learning and Cognition, 2014. Vol. 40, no. 2, pp. 144–161. DOI:10.1037/xan0000013
  32. Wu D. et al. Perceptual Learning at Higher Trained Cutoff Spatial Frequencies Induces Larger Visual Improvements. Frontiers in Psychology, 2020. Vol. 11, article ID 265, 9 p. DOI:10.3389/fpsyg.2020.00265
  33. Bao M. et al. Perceptual Learning Increases the Strength of the Earliest Signals in Visual Cortex. Journal of Neuroscience, 2010. Vol. 30, no. 45, pp. 15080–15084. DOI:10.1523/JNEUROSCI.5703-09.2010
  34. Ahmadi M. et al. Perceptual learning induces changes in early and late visual evoked potentials. Vision Research, 2018. Vol. 152, pp. 101–109. DOI:10.1016/j.visres.2017.08.008
  35. Reinke K.S. et al. Perceptual learning modulates sensory evoked response during vowel segregation. Cognitive Brain Research, 2003. Vol. 17, no. 3, pp. 781–791. DOI:10.1016/S0926-6410(03)00202-7
  36. Qu Z., Song Y., Ding Y. ERP evidence for distinct mechanisms of fast and slow visual perceptual learning. Neuropsychologia, 2010. Vol. 48, no. 6, pp. 1869–1874. DOI:10.1016/j.neuropsychologia.2010.01.008
  37. Ritter W., Simson R., Vaughan Jr H.G. Event-Related Potential Correlates of Two Stages of Information Processing in Physical and Semantic Discrimination Tasks. Psychophysiology, 1983. Vol. 20, no. 2, pp. 168–179. DOI:10.1111/j.1469-8986.1983.tb03283.x
  38. Rossion B., Curran T., Gauthier I. A defense of the subordinate-level expertise account for the N170 component. Cognition, 2002. Vol. 85, no. 2, pp. 189–196. DOI:10.1016/S0010-0277(02)00101-4
  39. Sheehan K.A., McArthur G.M., Bishop D.V.M. Is discrimination training necessary to cause changes in the P2 auditory event-related brain potential to speech sounds? Cognitive Brain Research, 2005. Vol. 25, no. 2, pp. 547–553. DOI:10.1016/j.cogbrainres.2005.08.007
  40. Shiu L., Pashler H. Improvement in line orientation discrimination is retinally local but dependent on cognitive set. Perception & Psychophysics, 1992. Vol. 52, no. 5, pp. 582–588. DOI:10.3758/bf03206720
  41. Alain C. et al. Sleep-dependent neuroplastic changes during auditory perceptual learning. Neurobiology of Learning and Memory, 2015. Vol. 118, pp. 133–142. DOI:10.1016/j.nlm.2014.12.001
  42. Ding Y. et al. Specificity and generalization of visual perceptual learning in humans: an event-related potential study. NeuroReport, 2003. Vol. 14, no. 4, pp. 587–590. DOI:10.1097/00001756-200303240-00012
  43. Schneider P. et al. Structural and functional asymmetry of lateral Heschl’s gyrus reflects pitch perception preference. Nature neuroscience, 2005. Vol. 8, no. 9, pp. 1241–1247. DOI:10.1038/nn1530
  44. Su J., Tan Q., Fang F. Neural correlates of face gender discrimination learning. Experimental brain research, 2013. Vol. 225, no. 4, pp. 569–578. DOI:10.1007/s00221-012-3396-x
  45. Tanaka J. W., Curran T. A Neural Basis for Expert Object Recognition. Psychological Science, 2001. Vol. 12, no. 1, pp. 43–47. DOI:10.1111/1467-9280.00308
  46. Fort A. et al. Task-dependent activation latency in human visual extrastriate cortex. Neuroscience Letters, 2005. Vol. 379, no. 2, pp. 144–148. DOI:10.1016/j.neulet.2004.12.076
  47. Kühnis J. et al. The encoding of vowels and temporal speech cues in the auditory cortex of professional musicians: An EEG study. Neuropsychologia, 2013. Vol. 51, no. 8, pp. 1608–1618. DOI:10.1016/j.neuropsychologia.2013.04.007
  48. Rossion B. et al. The N170 occipito-temporal component is delayed and enhanced to inverted faces but not to inverted objects: an electrophysiological account of face-specific processes in the human brain [Elektronnyi resurs]. Neuroreport, 2000. Vol. 11, no. 1, pp. 69–72. URL: http://files.face-categorization-lab.webnode.com/200000651-6e24a6f1c7/Rossion_2000_Neuroreport.pdf (Accessed 15.06.2020).
  49. Tong Y., Melara R.D., Rao A. P2 enhancement from auditory discrimination training is associated with improved reaction times. Brain Research, 2009. Vol. 1297, pp. 80–88. DOI:10.1016/j.brainres.2009.07.089
  50. Doniger G.M. et al. Visual Perceptual Learning in Human Object Recognition Areas: A Repetition Priming Study Using High-Density Electrical Mapping. NeuroImage, 2001. Vol. 13, no. 2, pp. 305–313. DOI:10.1006/nimg.2000.0684
  51. Yin R.K. Looking at upside-down faces. Journal of Experimental Psychology, 1969. Vol. 81, no. 1, pp. 141–145. DOI:10.1037/h0027474

Information About the Authors

Daria F. Kleeva, Research assistant, Sirius University of Science and Technology, Research assistant, Institute of Cognitive Neuroscience, National Research University «Higher School of Economics», Moscow, Russia, ORCID: https://orcid.org/0000-0002-6040-2154, e-mail: dkleeva@gmail.com

Anna B. Rebreikina, PhD in Biology, Researcher, Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS, Researcher, Laboratory of Human Higher Nervous Activity, Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia, ORCID: https://orcid.org/0000-0001-5714-2040, e-mail: anna.rebreikina@gmail.com

Olga V. Sysoeva, PhD in Psychology, Leading Researcher, Sirius University of Science and Technology, Head of the Laboratory, Scientific Center for Cognitive Research, Federal territory "Sirius", Russia, ORCID: https://orcid.org/0000-0002-4005-9512, e-mail: olga.v.sysoeva@gmail.com

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