Display options
Share it on

Mol Psychiatry. 2021 Sep;26(9):5320-5333. doi: 10.1038/s41380-020-0803-8. Epub 2020 Jun 24.

Precision weighting of cortical unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis.

Molecular psychiatry

J Haarsma, P C Fletcher, J D Griffin, H J Taverne, H Ziauddeen, T J Spencer, C Miller, T Katthagen, I Goodyer, K M J Diederen, G K Murray

Affiliations

  1. Department of Psychiatry, University of Cambridge, Cambridge, UK.
  2. Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK.
  3. Cambridgeshire and Peterborough NHS Trust, Cambridge, UK.
  4. Department of Psychosis studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
  5. Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.
  6. Department of Psychiatry, University of Cambridge, Cambridge, UK. [email protected].
  7. Department of Psychosis studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. [email protected].
  8. Department of Psychiatry, University of Cambridge, Cambridge, UK. [email protected].
  9. Cambridgeshire and Peterborough NHS Trust, Cambridge, UK. [email protected].

PMID: 32576965 PMCID: PMC8589669 DOI: 10.1038/s41380-020-0803-8

Abstract

Recent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of prediction errors plays a key role in learning and decision-making, is controlled by dopamine and contributes to the pathogenesis of psychosis. To test these hypotheses, we studied learning with variable outcome-precision in healthy individuals after dopaminergic modulation with a placebo, a dopamine receptor agonist bromocriptine or a dopamine receptor antagonist sulpiride (dopamine study n = 59) and in patients with early psychosis (psychosis study n = 74: 20 participants with first-episode psychosis, 30 healthy controls and 24 participants with at-risk mental state attenuated psychotic symptoms). Behavioural computational modelling indicated that precision weighting of prediction errors benefits learning in health and is impaired in psychosis. FMRI revealed coding of unsigned prediction errors, which signal surprise, relative to their precision in superior frontal cortex (replicated across studies, combined n = 133), which was perturbed by dopaminergic modulation, impaired in psychosis and associated with task performance and schizotypy (schizotypy correlation in 86 healthy volunteers). In contrast to our previous work, we did not observe significant precision-weighting of signed prediction errors, which signal valence, in the midbrain and ventral striatum in the healthy controls (or patients) in the psychosis study. We conclude that healthy people, but not patients with first-episode psychosis, take into account the precision of the environment when updating beliefs. Precision weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of psychosis.

© 2020. The Author(s).

References

  1. Science. 2008 Feb 29;319(5867):1264-7 - PubMed
  2. Front Hum Neurosci. 2011 May 02;5:39 - PubMed
  3. Schizophr Bull. 2019 Sep 11;45(5):1092-1100 - PubMed
  4. Nat Neurosci. 1999 Jan;2(1):79-87 - PubMed
  5. Sci Rep. 2017 Jul 6;7(1):4762 - PubMed
  6. J Psychopharmacol. 2007 May;21(3):238-52 - PubMed
  7. Neuron. 2003 Apr 24;38(2):329-37 - PubMed
  8. Behav Brain Sci. 2013 Jun;36(3):181-204 - PubMed
  9. Nat Rev Neurosci. 2019 Dec;20(12):763-778 - PubMed
  10. Brain. 2007 Sep;130(Pt 9):2387-400 - PubMed
  11. Trends Neurosci. 1991 Jan;14(1):21-7 - PubMed
  12. J Neurosci. 2017 Feb 15;37(7):1708-1720 - PubMed
  13. PLoS Comput Biol. 2015 Nov 04;11(11):e1004567 - PubMed
  14. Trends Cogn Sci. 2006 Jul;10(7):294-300 - PubMed
  15. Mol Psychiatry. 2008 Mar;13(3):239, 267-76 - PubMed
  16. Science. 2017 Aug 11;357(6351):596-600 - PubMed
  17. J Neurosci. 1987 Jan;7(1):279-90 - PubMed
  18. Neuropsychopharmacology. 2018 Jul;43(8):1691-1699 - PubMed
  19. Neuroimage. 2014 Apr 1;89:171-80 - PubMed
  20. Science. 2004 Apr 16;304(5669):452-4 - PubMed
  21. Nature. 2006 Aug 31;442(7106):1042-5 - PubMed
  22. PLoS Comput Biol. 2018 Aug 10;14(8):e1006319 - PubMed
  23. Nat Rev Neurosci. 2009 Jan;10(1):48-58 - PubMed
  24. NPJ Schizophr. 2016 Jun 15;2:16020 - PubMed
  25. J Neurosci. 2011 Mar 16;31(11):4178-87 - PubMed
  26. J Neurophysiol. 2015 Sep;114(3):1628-40 - PubMed
  27. J Neurosci. 2013 Jan 30;33(5):2039-47 - PubMed
  28. Neuron. 2016 Sep 21;91(6):1374-1389 - PubMed
  29. Cereb Cortex. 2013 Feb;23(2):477-87 - PubMed
  30. Cereb Cortex. 2004 Aug;14(8):872-80 - PubMed
  31. Trends Cogn Sci. 2009 Jul;13(7):293-301 - PubMed
  32. Neuron. 2016 Jun 1;90(5):1127-38 - PubMed
  33. Aust N Z J Psychiatry. 2005 Nov-Dec;39(11-12):964-71 - PubMed
  34. Nat Rev Neurosci. 2001 Jun;2(6):417-24 - PubMed
  35. Neuron. 2012 Nov 21;76(4):695-711 - PubMed
  36. Biol Psychiatry. 2018 Nov 1;84(9):634-643 - PubMed
  37. Front Psychiatry. 2013 May 30;4:47 - PubMed
  38. Nat Neurosci. 2001 Oct;4(10):1043-8 - PubMed
  39. Schizophr Bull. 2009 May;35(3):549-62 - PubMed
  40. Hum Brain Mapp. 2018 Jul;39(7):2887-2906 - PubMed
  41. Science. 1997 Mar 14;275(5306):1593-9 - PubMed

Publication Types

Grant support