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Perceptual learning alters post-sensory processing in human decision-making

Nature Human Behaviour volume 1, Article number: 0035 (2017) Cite this article

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Abstract

An emerging view in perceptual learning is that improvements in perceptual sensitivity are not only due to enhancements in early sensory representations but also due to changes in post-sensory decision-processing. In humans, however, direct neurobiological evidence of the latter remains scarce. Here, we trained participants on a visual categorization task over three days and used multivariate pattern analysis of the electroencephalogram to identify two temporally specific components encoding sensory (‘Early’) and decision (‘Late’) evidence, respectively. Importantly, the single-trial amplitudes of the Late, but not the Early component, were amplified in the course of training, and these enhancements predicted the behavioural improvements on the task. Correspondingly, we modelled these improvements with a reinforcement learning mechanism, using a reward prediction error signal to strengthen the readout of sensory evidence used for the decision. We validated this mechanism through a robust association between the model’s decision variables and the amplitudes of our Late component that encode decision evidence.

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Figure 1: Experimental design and behaviour.
Figure 2: Post-sensory effects of perceptual learning.
Figure 3: Enhanced readout of post-sensory decision evidence.
Figure 4: Reinforcement learning model for perceptual choices.
Figure 5: Electrophysiological correlates of prediction error (PE).

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References

  1. Gilbert, C. D., Sigman, M. & Crist, R. E. The neural basis of perceptual learning. Neuron 31, 681–697 (2001).

    Article  CAS  PubMed  Google Scholar 

  2. Goldstone, R. L. Perceptual learning. Annu. Rev. Psychol. 49, 585–612 (1998).

    Article  CAS  PubMed  Google Scholar 

  3. Ball, K. & Sekuler, R. Direction-specific improvement in motion discrimination. Vision Res. 27, 953–965 (1987).

    Article  CAS  PubMed  Google Scholar 

  4. Crist, R. E., Kapadia, M. K., Westheimer, G. & Gilbert, C. D. Perceptual learning of spatial localization: specificity for orientation, position, and context. J. Neurophysiol. 78, 2889–2894 (1997).

    Article  CAS  PubMed  Google Scholar 

  5. Fahle, M. & Edelman, S. Long-term learning in vernier acuity: effects of stimulus orientation, range and of feedback. Vis. Res. 33, 397–412 (1993).

    Article  CAS  PubMed  Google Scholar 

  6. Fiorentini, A. & Berardi, N. Perceptual learning specific for orientation and spatial frequency. Nature 287, 43–44 (1980).

    Google Scholar 

  7. Karni, A. & Sagi, D. Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proc. Natl Acad. Sci. USA 88, 4966–4970 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Poggio, T., Fahle, M. & Edelman, S. Fast perceptual learning in visual hyperacuity. Science 256, 1018–1021 (1992).

    Article  CAS  PubMed  Google Scholar 

  9. Sagi, D. & Tanne, D. Perceptual learning: learning to see. Curr. Opin. Neurobiol. 4, 195–199 (1994).

    Article  CAS  PubMed  Google Scholar 

  10. Ahissar, M. & Hochstein, S. Task difficulty and the specificity of perceptual learning. Nature 387, 401–406 (1997).

    Article  CAS  PubMed  Google Scholar 

  11. Mollon, J. D. & Danilova, M. V. Three remarks on perceptual learning. Spat. Vis. 10, 51–58 (1996).

    Article  CAS  PubMed  Google Scholar 

  12. Ahissar, M. & Hochstein, S. Attentional control of early perceptual learning. Proc. Natl Acad. Sci. USA 90, 5718–5722 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Law, C.-T. & Gold, J. I. Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11, 505–513 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Law, C.-T. & Gold, J. I. Reinforcement learning can account for associative and perceptual learning on a visual-decision task. Nat. Neurosci. 12, 655–663 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Baldassarre, A. et al. Individual variability in functional connectivity predicts performance of a perceptual task. Proc. Natl Acad. Sci. USA 109, 3516–3521 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Chen, N. H. et al. Sharpened cortical tuning and enhanced cortico-cortical communication contribute to the long-term neural mechanisms of visual motion perceptual learning. NeuroImage 115, 17–29 (2015).

    Article  PubMed  Google Scholar 

  17. Kahnt, T., Grueschow, M., Speck, O. & Haynes, J.-D. Perceptual learning and decision-making in human medial frontal cortex. Neuron 70, 549–559 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L. & Corbetta, M. Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl Acad. Sci. USA 106, 17558–17563 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Philiastides, M. G., Ratcliff, R. & Sajda, P. Neural representation of task difficulty and decision making during perceptual categorization: a timing diagram. J. Neurosci. 26, 8965–8975 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Philiastides, M. G. & Sajda, P. Temporal characterization of the neural correlates of perceptual decision making in the human brain. Cereb. Cortex 16, 509–518 (2006).

    Article  PubMed  Google Scholar 

  21. Philiastides, M. G. & Sajda, P. Causal influences in the human brain during face discrimination: a short-window directed transfer function approach. IEEE Trans. Biomed. Eng. 53, 2602–2605 (2006).

    Article  PubMed  Google Scholar 

  22. Philiastides, M. G. & Sajda, P. EEG-informed fMRI reveals spatiotemporal characteristics of perceptual decision making. J. Neurosci. 27, 13082–13091 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ratcliff, R., Philiastides, M. G. & Sajda, P. Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG. Proc. Natl Acad. Sci. USA 106, 6539–6544 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rushworth, M. F., Mars, R. B. & Summerfield, C. General mechanisms for making decisions? Curr. Opin. Neurobiol. 19, 75–83 (2009).

    Article  CAS  PubMed  Google Scholar 

  25. Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

    Article  CAS  PubMed  Google Scholar 

  26. Guggenmos, M., Wilbertz, G., Hebart, M. N. & Sterzer, P. Mesolimbic confidence signals guide perceptual learning in the absence of external feedback. eLife 5, e13388 (2016).

  27. Parra, L. C., Spence, C. D., Gerson, A. D. & Sajda, P. Recipes for the linear analysis of EEG. NeuroImage 28, 326–341 (2005).

    Article  PubMed  Google Scholar 

  28. Sajda, P., Philiastides, M. G. & Parra, L. C. Single-trial analysis of neuroimaging data: inferring neural networks underlying perceptual decision-making in the human brain. IEEE Rev. Biomed. Eng. 2, 97–109 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ratcliff, R. & Smith, P. L. Perceptual discrimination in static and dynamic noise: the temporal relation between perceptual encoding and decision making. J. Exp. Psychol. Gen. 139, 70–94 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ratcliff, R., Smith, P. L. & McKoon, G. Modeling regularities in response time and accuracy data with the diffusion model. Curr. Dir. Psychol. Sci. 24, 458–470 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Smith, P. L. & Ratcliff, R. Psychology and neurobiology of simple decisions. Trends Neurosci. 27, 161–168 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Petrov, A. A., Dosher, B. A. & Lu, Z.-L. The dynamics of perceptual learning: an incremental reweighting model. Psychol. Rev. 112, 715 (2005).

    Article  PubMed  Google Scholar 

  33. Fouragnan, E., Retzler, C., Mullinger, K. & Philiastides, M. G. Two spatiotemporally distinct value systems shape reward-based learning in the human brain. Nat. Commun. 6, 8107 (2015).

    Article  PubMed  Google Scholar 

  34. Philiastides, M. G., Biele, G., Vavatzanidis, N., Kazzer, P. & Heekeren, H. R. Temporal dynamics of prediction error processing during reward-based decision making. NeuroImage 53, 221–232 (2010).

    Article  PubMed  Google Scholar 

  35. Lou, B., Li, Y., Philiastides, M. G. & Sajda, P. Prestimulus alpha power predicts fidelity of sensory encoding in perceptual decision making. NeuroImage 87, 242–251 (2014).

    Article  PubMed  Google Scholar 

  36. Kelly, S. P. & O'Connell, R. G. Internal and external influences on the rate of sensory evidence accumulation in the human brain. J. Neurosci. 33, 19434–19441 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. O'Connell, R. G., Dockree, P. M. & Kelly, S. P. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat. Neurosci. 15, 1729–1735 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Philiastides, M. G., Heekeren, H. R. & Sajda, P. Human scalp potentials reflect a mixture of decision-related signals during perceptual choices. J. Neurosci. 34, 16877–16889 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 1998).

    Google Scholar 

  40. O'Doherty, J. P., Dayan, P., Friston, K., Critchley, H. & Dolan, R. J. Temporal difference models and reward-related learning in the human brain. Neuron 38, 329–337 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Pessiglione, M., Seymour, B., Flandin, G., Dolan, R. J. & Frith, C. D. Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans. Nature 442, 1042–1045 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Fahle, M. Perceptual learning: a case for early selection. J. Vis. 4, 879–890 (2004).

    Article  PubMed  Google Scholar 

  43. Fahle, M. Perceptual learning: specificity versus generalization. Curr. Opin. Neurobiol. 15, 154–160 (2005).

    Article  CAS  PubMed  Google Scholar 

  44. Schwartz, S., Maquet, P. & Frith, C. Neural correlates of perceptual learning: a functional MRI study of visual texture discrimination. Proc. Natl Acad. Sci. USA 99, 17137–17142 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Furmanski, C. S., Schluppeck, D. & Engel, S. A. Learning strengthens the response of primary visual cortex to simple patterns. Curr. Biol. 14, 573–578 (2004).

    Article  CAS  PubMed  Google Scholar 

  46. Jehee, J. F., Ling, S., Swisher, J. D., van Bergen, R. S. & Tong, F. Perceptual learning selectively refines orientation representations in early visual cortex. J. Neurosci. 32, 16747–16753 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bao, M., Yang, L., Rios, C., He, B. & Engel, S. A. Perceptual learning increases the strength of the earliest signals in visual cortex. J. Neurosci. 30, 15080–15084 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pourtois, G., Rauss, K. S., Vuilleumier, P. & Schwartz, S. Effects of perceptual learning on primary visual cortex activity in humans. Vis. Res. 48, 55–62 (2008).

    Article  PubMed  Google Scholar 

  49. Censor, N., Bonneh, Y., Arieli, A. & Sagi, D. Early-vision brain responses which predict human visual segmentation and learning. J. Vis. 9, 12. 1–9 (2009).

    Google Scholar 

  50. Ghose, G. M., Yang, T. & Maunsell, J. H. Physiological correlates of perceptual learning in monkey V1 and V2. J. Neurophysiol. 87, 1867–1888 (2002).

    Article  PubMed  Google Scholar 

  51. Schoups, A., Vogels, R., Qian, N. & Orban, G. Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549–553 (2001).

    Article  CAS  PubMed  Google Scholar 

  52. Yan, Y. et al. Perceptual training continuously refines neuronal population codes in primary visual cortex. Nat. Neurosci. 17, 1380–1387 (2014).

    Article  CAS  PubMed  Google Scholar 

  53. Dosher, B. A. & Lu, Z. L. Mechanisms of perceptual learning. Vis. Res. 39, 3197–3221 (1999).

    Article  CAS  PubMed  Google Scholar 

  54. Lu, Z.-L., Liu, J. & Dosher, B. A. Modeling mechanisms of perceptual learning with augmented Hebbian re-weighting. Vis. Res. 50, 375–390 (2010).

    Article  PubMed  Google Scholar 

  55. Kuai, S.-G., Levi, D. & Kourtzi, Z. Learning optimizes decision templates in the human visual cortex. Curr. Biol. 23, 1799–1804 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Li, S., Mayhew, S. D. & Kourtzi, Z. Learning shapes the representation of behavioral choice in the human brain. Neuron 62, 441–452 (2009).

    Article  CAS  PubMed  Google Scholar 

  57. Shibata, K., Watanabe, T., Sasaki, Y. & Kawato, M. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334, 1413–1415 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Shibata, K., Sagi, D. & Watanabe, T. Two-stage model in perceptual learning: toward a unified theory. Ann. NY Acad. Sci. 1316, 18–28 (2014).

    Article  PubMed  Google Scholar 

  59. Watanabe, T. & Sasaki, Y. Perceptual learning: toward a comprehensive theory. Annu. Rev. Psychol. 66, 197–221 (2015).

    Article  PubMed  Google Scholar 

  60. Li, W., Piech, V. & Gilbert, C. D. Perceptual learning and top-down influences in primary visual cortex. Nat. Neurosci. 7, 651–657 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Perna, A., Tosetti, M., Montanaro, D. & Morrone, M. C. BOLD response to spatial phase congruency in human brain. J. Vis. 8, 15. 1–15 (2008).

    Google Scholar 

  62. Troje, N. F. & Bìlthoff, H. H. Face recognition under varying poses: the role of texture and shape. Vis. Res. 36, 1761–1771 (1996).

    Article  CAS  PubMed  Google Scholar 

  63. Dakin, S. C., Hess, R. F., Ledgeway, T. & Achtman, R. L. What causes non-monotonic tuning of fMRI response to noisy images? Curr. Biol. 12, R476–R477; author reply R478 (2002).

    Article  CAS  PubMed  Google Scholar 

  64. Peirce, J. W. PsychoPy — psychophysics software in Python. J. Neurosci. Methods 162, 8–13 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Gherman, S. & Philiastides, M. G. Neural representations of confidence emerge from the process of decision formation during perceptual choices. NeuroImage 106, 134–143 (2015).

    Article  PubMed  Google Scholar 

  66. Duda, R. O. & Hart, P. E. Pattern Classification and Scene Analysis Vol. 3 (Wiley, 1973).

    Google Scholar 

  67. Baayen, R. H., Davidson, D. J. & Bates, D. M. Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390–412 (2008).

    Article  Google Scholar 

  68. Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2006).

    Book  Google Scholar 

  69. Stephan, K. E. et al. Ten simple rules for dynamic causal modeling. NeuroImage 49, 3099–3109 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A. & Friston, K. J. Comparing hemodynamic models with DCM. NeuroImage 38, 387–401 (2007).

    Article  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the Economic and Social Research Council (ESRC; grant ES/L012995/1 to M.G.P.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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  1. Institute of Neuroscience and Psychology, University of Glasgow, 58 Hillhead Street, Glasgow, G12 8QB, UK

    Jessica A. Diaz, Filippo Queirazza & Marios G. Philiastides

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  1. Jessica A. Diaz
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Contributions

J.A.D. and M.G.P. designed the experiments. J.A.D. performed the experiments. J.A.D., F.Q. and M.G.P. analysed the data and wrote the paper.

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Correspondence to Marios G. Philiastides.

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Diaz, J., Queirazza, F. & Philiastides, M. Perceptual learning alters post-sensory processing in human decision-making. Nat Hum Behav 1, 0035 (2017). https://doi.org/10.1038/s41562-016-0035

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