Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Learning the microstructure of successful behavior

Nature Neuroscience volume 14, pages 373–380 (2011)Cite this article

Subjects

Abstract

Reinforcement signals indicating success or failure are known to alter the probability of selecting between distinct actions. However, successful performance of many motor skills, such as speech articulation, also requires learning behavioral trajectories that vary continuously over time. Here, we investigated how temporally discrete reinforcement signals shape a continuous behavioral trajectory, the fundamental frequency of adult Bengalese finch song. We provided reinforcement contingent on fundamental frequency performance only at one point in the song. Learned changes to fundamental frequency were maximal at this point, but also extended both earlier and later in the fundamental frequency trajectory. A simple principle predicted the detailed structure of learning: birds learned to produce the average of the behavioral trajectories associated with successful outcomes. This learning rule accurately predicted the structure of learning at a millisecond timescale, demonstrating that the nervous system records fine-grained details of successful behavior and uses this information to guide learning.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The microstructure of learning from precisely timed reinforcement.
Figure 2: The microstructure of successful variation predicts learning.
Figure 3: The microstructure of successful variation predicts learning with a complex reinforcement contingency.
Figure 4: The specific history of reinforcement determines the structure of learning.
Figure 5: Inter-syllable differences in the structure of variation predict differences in learning.
Figure 6: The range of variation constrains learning.

Similar content being viewed by others

References

  1. Thorndike, E.L. Animal Intelligence (Macmillan, New York, 1911).

  2. Skinner, B.F. Science and Human Behavior (Macmillan, New York, 1953).

  3. Balleine, B.W. & Ostlund, S.B. Still at the choice-point: action selection and initiation in instrumental conditioning. Ann. NY Acad. Sci. 1104, 147–171 (2007).

    Article  PubMed  Google Scholar 

  4. Skinner, B.F. Superstition in the pigeon. J. Exp. Psychol. 38, 168–172 (1948).

    Article  CAS  PubMed  Google Scholar 

  5. Schoppik, D., Nagel, K.I. & Lisberger, S.G. Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses. Neuron 58, 248–260 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hira, R. et al. Transcranial optogenetic stimulation for functional mapping of the motor cortex. J. Neurosci. Methods 179, 258–263 (2009).

    Article  PubMed  Google Scholar 

  7. Fee, M.S., Kozhevnikov, A.A. & Hahnloser, R.H. Neural mechanisms of vocal sequence generation in the songbird. Ann. NY Acad. Sci. 1016, 153–170 (2004).

    Article  PubMed  Google Scholar 

  8. Tumer, E.C. & Brainard, M.S. Performance variability enables adaptive plasticity of 'crystallized' adult birdsong. Nature 450, 1240–1244 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Tchernichovski, O., Mitra, P.P., Lints, T. & Nottebohm, F. Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291, 2564–2569 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Franz, M. & Goller, F. Respiratory units of motor production and song imitation in the zebra finch. J. Neurobiol. 51, 129–141 (2002).

    Article  PubMed  Google Scholar 

  11. Méndez, J.M., Dall'asén, A.G., Cooper, B.G. & Goller, F. Acquisition of an acoustic template leads to refinement of song motor gestures. J. Neurophysiol. 104, 984–993 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ashmore, R.C., Wild, J.M. & Schmidt, M.F. Brainstem and forebrain contributions to the generation of learned motor behaviors for song. J. Neurosci. 25, 8543–8554 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Glaze, C.M. & Troyer, T.W. Temporal structure in zebra finch song: implications for motor coding. J. Neurosci. 26, 991–1005 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Glaze, C.M. & Troyer, T.W. Behavioral measurements of a temporally precise motor code for birdsong. J. Neurosci. 27, 7631–7639 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tchernichovski, O., Lints, T.J., Deregnaucourt, S., Cimenser, A. & Mitra, P.P. Studying the song development process: rationale and methods. Ann. NY Acad. Sci. 1016, 348–363 (2004).

    Article  CAS  PubMed  Google Scholar 

  16. Chi, Z. & Margoliash, D. Temporal precision and temporal drift in brain and behavior of zebra finch song. Neuron 32, 899–910 (2001).

    Article  CAS  PubMed  Google Scholar 

  17. Andalman, A.S. & Fee, M.S. A basal ganglia-forebrain circuit in the songbird biases motor output to avoid vocal errors. Proc. Natl. Acad. Sci. USA 106, 12518–12523 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gardner, T.J. & Magnasco, M.O. Sparse time-frequency representations. Proc. Natl. Acad. Sci. USA 103, 6094–6099 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Todorov, E. Optimality principles in sensorimotor control. Nat. Neurosci. 7, 907–915 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Leonardo, A. & Konishi, M. Decrystallization of adult birdsong by perturbation of auditory feedback. Nature 399, 466–470 (1999).

    Article  CAS  PubMed  Google Scholar 

  21. Mauk, M.D. & Buonomano, D.V. The neural basis of temporal processing. Annu. Rev. Neurosci. 27, 307–340 (2004).

    Article  CAS  PubMed  Google Scholar 

  22. Cohen, J.D., McClure, S.M. & Yu, A.J. Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Phil. Trans. R. Soc. Lond. B 362, 933–942 (2007).

    Article  Google Scholar 

  23. Staddon, J.E.R. Adaptive Behavior and Learning (Cambridge Univ. Press, Cambridge, UK, 1983).

  24. Catania, A.C. Learning 2nd edn. (Prentice Hall, Englewood Cliffs, New Jersey, USA, 1984).

  25. Churchland, M.M., Afshar, A. & Shenoy, K.V. A central source of movement variability. Neuron 52, 1085–1096 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Faisal, A.A., Selen, L.P. & Wolpert, D.M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Todorov, E. & Jordan, M.I. Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Elemans, C.P., Spierts, I.L., Müller, U.K., Van Leeuwen, J.L. & Goller, F. Bird song: superfast muscles control dove's trill. Nature 431, 146 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Elemans, C.P., Mead, A.F., Rome, L.C. & Goller, F. Superfast vocal muscles control song production in songbirds. PLoS ONE 3, e2581 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sober, S.J., Wohlgemuth, M.J. & Brainard, M.S. Central contributions to acoustic variation in birdsong. J. Neurosci. 28, 10370–10379 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. van Beers, R.J. The sources of variability in saccadic eye movements. J. Neurosci. 27, 8757–8770 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. van Beers, R.J., Haggard, P. & Wolpert, D.M. The role of execution noise in movement variability. J. Neurophysiol. 91, 1050–1063 (2004).

    Article  PubMed  Google Scholar 

  33. Fetz, E.E. & Finocchio, D.V. Operant conditioning of specific patterns of neural and muscular activity. Science 174, 431–435 (1971).

    Article  CAS  PubMed  Google Scholar 

  34. Rosenfeld, J.P. & Hetzler, B.E. Operant-controlled evoked responses: discrimination of conditioned and normally occurring components. Science 181, 767–770 (1973).

    Article  CAS  PubMed  Google Scholar 

  35. Rosenfeld, J.P. & Fox, S.S. Operant control of a brain potential evoked by a behavior. Physiol. Behav. 7, 489–493 (1971).

    Article  CAS  PubMed  Google Scholar 

  36. Bottjer, S.W., Miesner, E.A. & Arnold, A.P. Forebrain lesions disrupt development but not maintenance of song in passerine birds. Science 224, 901–903 (1984).

    Article  CAS  PubMed  Google Scholar 

  37. Scharff, C. & Nottebohm, F. A comparative study of the behavioral deficits following lesions of various parts of the zebra finch song system: implications for vocal learning. J. Neurosci. 11, 2896–2913 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jarvis, E.D., Scharff, C., Grossman, M.R., Ramos, J.A. & Nottebohm, F. For whom the bird sings: context-dependent gene expression. Neuron 21, 775–788 (1998).

    Article  CAS  PubMed  Google Scholar 

  39. Hessler, N.A. & Doupe, A.J. Social context modulates singing-related neural activity in the songbird forebrain. Nat. Neurosci. 2, 209–211 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. Doya, K. & Sejnowski, T.J. A computational model of avian song learning. in The New Cognitive Neurosciences (ed. Gazzaniga, M.S.) 469–482 (MIT Press, Cambridge, Massachusetts, USA, 2000).

  41. Kao, M.H., Doupe, A.J. & Brainard, M.S. Contributions of an avian basal ganglia-forebrain circuit to real-time modulation of song. Nature 433, 638–643 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Olveczky, B.P., Andalman, A.S. & Fee, M.S. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLoS Biol. 3, e153 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fiete, I.R., Fee, M.S. & Seung, H.S. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. J. Neurophysiol. 98, 2038–2057 (2007).

    Article  PubMed  Google Scholar 

  44. Aronov, D., Andalman, A.S. & Fee, M.S. A specialized forebrain circuit for vocal babbling in the juvenile songbird. Science 320, 630–634 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Hampton, C.M., Sakata, J.T. & Brainard, M.S. An avian basal ganglia-forebrain circuit contributes differently to syllable versus sequence variability of adult Bengalese finch song. J. Neurophysiol. 101, 3235–3245 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Mauk, M.D. & Ruiz, B.P. Learning-dependent timing of Pavlovian eyelid responses: differential conditioning using multiple interstimulus intervals. Behav. Neurosci. 106, 666–681 (1992).

    Article  CAS  PubMed  Google Scholar 

  47. Medina, J.F., Carey, M.R. & Lisberger, S.G. The representation of time for motor learning. Neuron 45, 157–167 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Leonardo, A. & Fee, M.S. Ensemble coding of vocal control in birdsong. J. Neurosci. 25, 652–661 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. House, D. Tonal Perception in Speech (Lund Univ. Press, Lund, Sweden, 1990).

  50. Hermes, D.J. Stylization of pitch contours. in Methods in Empirical Prosody Research (ed. Sudhoff, S.) 29–62 (Walter de Gruyter, Berlin, 2006).

    Google Scholar 

Download references

Acknowledgements

We thank P. Sabes, S. Sober, L. Frank, A. Doupe and S. Lisberger for discussion and comments on the manuscript and J. Wong and C. Brown for animal care. This work was supported by US National Institutes of Health R01 and P50 grants. E.C.T. was supported by a National Research Service Award postdoctoral fellowship from the US National Institute on Deafness and Other Communication Disorders and by the Sloan-Swartz Foundation. J.D.C. and T.L.W. were supported by US National Science Foundation graduate fellowships.

Author information

Authors and Affiliations

  1. W. M. Keck Center for Integrative Neuroscience, University of California, San Francisco, California, USA

    Jonathan D Charlesworth, Evren C Tumer, Timothy L Warren & Michael S Brainard

  2. Department of Physiology, University of California, San Francisco, California, USA

    Jonathan D Charlesworth, Evren C Tumer, Timothy L Warren & Michael S Brainard

  3. Neuroscience Graduate Program, University of California, San Francisco, California, USA

    Jonathan D Charlesworth, Timothy L Warren & Michael S Brainard

Authors
  1. Jonathan D Charlesworth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Evren C Tumer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Timothy L Warren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael S Brainard
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to designing the experiments. E.C.T. developed the song acquisition and reinforcement delivery software. T.L.W., E.C.T. and J.D.C. performed the experiments and collected the data. J.D.C. developed the modeling and analysis techniques and prepared the manuscript, with assistance from T.L.W. and M.S.B.

Corresponding author

Correspondence to Jonathan D Charlesworth.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figure 1 (PDF 422 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Charlesworth, J., Tumer, E., Warren, T. et al. Learning the microstructure of successful behavior. Nat Neurosci 14, 373–380 (2011). https://doi.org/10.1038/nn.2748

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.2748

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing