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Serotonin reduces belief stickiness

Nature Mental Health (2026)Cite this article

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Abstract

Serotonin fosters cognitive flexibility, but how, exactly, remains unclear. We developed a computational theory that proposes that serotonin reduces belief stickiness: the tendency to get ‘stuck’ in a belief about the state of the world despite incoming contradicting evidence. We tested this theory in a randomized, double-blind, placebo-controlled study using a single dose of the selective serotonin reuptake inhibitor escitalopram. In the escitalopram group, higher escitalopram plasma levels reduced belief stickiness more, resulting in better inference about the state of the world. Moreover, participants with sufficiently high escitalopram plasma levels had less belief stickiness, and therefore better state inference, than participants on placebo. We also propose that obsessions may result from excessive belief stickiness. Indeed, participants with more obsessions had greater belief stickiness, and therefore worse state inference. The opposite relations of escitalopram and obsessions with belief stickiness may explain the therapeutic effect of selective serotonin reuptake inhibitors in obsessive–compulsive disorder.

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Fig. 1: The shell task (see ‘Task’ in the Methods).
The alternative text for this image may have been generated using AI.
Fig. 2: Models were divided into two families: S–R learning (S–R models, panel a) and S–R learning augmented with state inference (S–S–R models, panel b).
The alternative text for this image may have been generated using AI.
Fig. 3: Behavior was best explained by a model that extended S–R learning with state inference.
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Fig. 4: The winning model, S–S–R–8, replicates participants’ (n = 44) group-level behavior.
The alternative text for this image may have been generated using AI.
Fig. 5: Escitalopram levels and obsessive symptoms had opposite effects on belief stickiness.
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Fig. 6: Escitalopram levels and obsessive symptoms had opposite effects on state inference.
The alternative text for this image may have been generated using AI.

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Data availability

All data analyzed in this article are available via Zenodo at https://zenodo.org/records/10066829 (ref. 138).

Code availability

The code implementing all computational models and analyses in this article is available via GitHub at https://github.com/vaconceicao/serotonin-reduces-belief-stickiness.

References

  1. Clarke, H. F., Dalley, J. W., Crofts, H. S., Robbins, T. W. & Roberts, A. C. Cognitive inflexibility after prefrontal serotonin depletion. Science 304, 878–880 (2004).

    Article  PubMed  Google Scholar 

  2. Clarke, H. F., Walker, S., Dalley, J., Robbins, T. & Roberts, A. Cognitive inflexibility after prefrontal serotonin depletion is behaviorally and neurochemically specific. Cereb. Cortex 17, 18–27 (2007).

    Article  PubMed  Google Scholar 

  3. Nilsson, S. R. O., Phillips, B. U., Axelsson, S. F. A. & Alsiö, J. in The Serotonin System (eds Tricklebank, M. D. & Daly, E.) 133–154 (Elsevier, 2019).

  4. Maia, T. V. Reinforcement learning, conditioning, and the brain: successes and challenges. Cogn. Affect. Behav. Neurosci. 9, 343–364 (2009).

    Article  PubMed  Google Scholar 

  5. Yin, H. H. & Knowlton, B. J. The role of the basal ganglia in habit formation. Nat. Rev. Neurosci. 7, 464–476 (2006).

    Article  PubMed  Google Scholar 

  6. Collins, A. G. E. & Frank, M. J. Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive. Psychol. Rev. 121, 337–366 (2014).

    Article  PubMed  Google Scholar 

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

  8. Behrens, T. E. J., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).

    Article  PubMed  Google Scholar 

  9. Grossman, C. D., Bari, B. A. & Cohen, J. Y. Serotonin neurons modulate learning rate through uncertainty. Curr. Biol. 32, 586–599.e7 (2022).

    Article  PubMed  Google Scholar 

  10. Gershman, S. J., Blei, D. M. & Niv, Y. Context, learning, and extinction. Psychol. Rev. 117, 197–209 (2010).

    Article  PubMed  Google Scholar 

  11. Niv, Y. Learning task-state representations. Nat. Neurosci. 22, 1544–1553 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Redish, A. D., Jensen, S., Johnson, A. & Kurth-Nelson, Z. Reconciling reinforcement learning models with behavioral extinction and renewal: implications for addiction, relapse, and problem gambling. Psychol. Rev. 114, 784–805 (2007).

    Article  PubMed  Google Scholar 

  13. Wilson, R. C., Takahashi, Y. K., Schoenbaum, G. & Niv, Y. Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hornak, J. et al. Reward-related reversal learning after surgical excisions in orbito-frontal or dorsolateral prefrontal cortex in humans. J. Cogn. Neurosci. 16, 463–478 (2004).

    Article  PubMed  Google Scholar 

  15. Izquierdo, A., Brigman, J. L., Radke, A. K., Rudebeck, P. H. & Holmes, A. The neural basis of reversal learning: an updated perspective. Neuroscience 345, 12–26 (2017).

    Article  PubMed  Google Scholar 

  16. Remijnse, P. L. et al. Reduced orbitofrontal-striatal activity on a reversal learning task in obsessive-compulsive disorder. Arch. Gen. Psychiatry 63, 1225–1236 (2006).

    Article  PubMed  Google Scholar 

  17. Tezcan, D., Tumkaya, S. & Bora, E. Reversal learning in patients with obsessive-compulsive disorder (OCD) and their unaffected relatives: is orbitofrontal dysfunction an endophenotype of OCD? Psychiatry Res. 252, 231–233 (2017).

    Article  PubMed  Google Scholar 

  18. Fellows, L. K. The role of orbitofrontal cortex in decision making. Ann. N. Y. Acad. Sci. 1121, 421–430 (2007).

    Article  PubMed  Google Scholar 

  19. Maia, T. V. & McClelland, J. L. The somatic marker hypothesis: still many questions but no answers. Trends Cogn. Sci. 9, 162–164 (2005).

    Article  Google Scholar 

  20. Rudebeck, P. H., Saunders, R. C., Prescott, A. T., Chau, L. S. & Murray, E. A. Prefrontal mechanisms of behavioral flexibility, emotion regulation and value updating. Nat. Neurosci. 16, 1140–1145 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bradfield, L. A., Dezfouli, A., van Holstein, M., Chieng, B. & Balleine, B. W. Medial orbitofrontal cortex mediates outcome retrieval in partially observable task situations. Neuron 88, 1268–1280 (2015).

    Article  PubMed  Google Scholar 

  22. Schuck, N. W., Cai, M. B., Wilson, R. C. & Niv, Y. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Schuck, N. W., Wilson, R. & Niv, Y. in Goal-Directed Decision Making: Computations and Neural Circuits (eds Morris, R. et al.) 259–278 (Academic Press, 2018).

  24. Stalnaker, T. A., Cooch, N. K. & Schoenbaum, G. What the orbitofrontal cortex does not do. Nat. Neurosci. 18, 620–627 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Wikenheiser, A. M. & Schoenbaum, G. Over the river, through the woods: cognitive maps in the hippocampus and orbitofrontal cortex. Nat. Rev. Neurosci. 17, 513–523 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Clarke, H. F. et al. Prefrontal serotonin depletion affects reversal learning but not attentional set shifting. J. Neurosci. 25, 532–538 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Kanen, J. W. et al. Serotonin depletion impairs both Pavlovian and instrumental reversal learning in healthy humans. Mol. Psychiatry 26, 7200–7210 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Matias, S., Lottem, E., Dugué, G. P. & Mainen, Z. F. Activity patterns of serotonin neurons underlying cognitive flexibility. eLife 6, e20552 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Roberts, C., Sahakian, B. J. & Robbins, T. W. Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: lessons from the serotonergic modulation of action selection, learning, affect, and social cognition. Neurosci. Biobehav. Rev. 119, 138–167 (2020).

    Article  PubMed  Google Scholar 

  30. Zhukovsky, P. et al. Perseveration in a spatial-discrimination serial reversal learning task is differentially affected by MAO-A and MAO-B inhibition and associated with reduced anxiety and peripheral serotonin levels. Psychopharmacology 234, 1557–1571 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Barlow, R. L. et al. Markers of serotonergic function in the orbitofrontal cortex and dorsal raphé nucleus predict individual variation in spatial-discrimination serial reversal learning. Neuropsychopharmacology 40, 1619–1630 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Groman, S. M. et al. Monoamine levels within the orbitofrontal cortex and putamen interact to predict reversal learning performance. Biol. Psychiatry 73, 756–762 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Maia, T. V. & Cano-Colino, M. The role of serotonin in orbitofrontal function and obsessive-compulsive disorder. Clin. Psychol. Sci. 3, 460–482 (2015).

    Article  Google Scholar 

  34. Roberts, A. C. The importance of serotonin for orbitofrontal function. Biol. Psychiatry 69, 1185–1191 (2011).

    Article  PubMed  Google Scholar 

  35. Alsiö, J. et al. Serotonergic innervations of the orbitofrontal and medial-prefrontal cortices are differentially involved in visual discrimination and reversal learning in rats. Cereb. Cortex 31, 1090–1105 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Masaki, D. et al. Relationship between limbic and cortical 5-HT neurotransmission and acquisition and reversal learning in a go/no-go task in rats. Psychopharmacology 189, 249–258 (2006).

    Article  PubMed  Google Scholar 

  37. Chan, S. C. Y., Niv, Y. & Norman, K. A. A probability distribution over latent causes, in the orbitofrontal cortex. J. Neurosci. 36, 7817–7828 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Huys, Q. J. M. & Petzschner, F. H. Failure modes of the will: from goals to habits to compulsions? Am. J. Psychiatry 172, 216–218 (2015).

    Article  PubMed  Google Scholar 

  39. Rigoux, L., Stephan, K. E. & Petzschner, F. H. Beliefs, compulsive behavior and reduced confidence in control. PLoS Comput. Biol. 20, e1012207 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Maia, T. V., Cooney, R. E. & Peterson, B. S. The neural bases of obsessive–compulsive disorder in children and adults. Dev. Psychopathol. 20, 1251 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Menzies, L. et al. Integrating evidence from neuroimaging and neuropsychological studies of obsessive-compulsive disorder: the orbitofronto-striatal model revisited. Neurosci. Biobehav. Rev. 32, 525–549 (2008).

    Article  PubMed  Google Scholar 

  42. Piras, F. et al. Widespread structural brain changes in OCD: a systematic review of voxel-based morphometry studies. Cortex 62, 89–108 (2015).

    Article  PubMed  Google Scholar 

  43. Rotge, J.-Y. et al. Provocation of obsessive-compulsive symptoms: a quantitative voxel-based meta-analysis of functional neuroimaging studies. J. Psychiatry Neurosci. 33, 405–412 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Rotge, J.-Y. et al. Meta-analysis of brain volume changes in obsessive-compulsive disorder. Biol. Psychiatry 65, 75–83 (2009).

    Article  PubMed  Google Scholar 

  45. Thorsen, A. L. et al. Emotional processing in obsessive-compulsive disorder: a systematic review and meta-analysis of 25 functional neuroimaging studies. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 3, 563–571 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. Whiteside, S. P., Port, J. D. & Abramowitz, J. S. A meta-analysis of functional neuroimaging in obsessive-compulsive disorder. Psychiatry Res. 132, 69–79 (2004).

    Article  PubMed  Google Scholar 

  47. Maia, T. V. & McClelland, J. L. A neurocomputational approach to obsessive-compulsive disorder. Trends Cogn. Sci. 16, 14–15 (2012).

    Article  PubMed  Google Scholar 

  48. Rolls, E. T., Loh, M. & Deco, G. An attractor hypothesis of obsessive–compulsive disorder. Eur. J. Neurosci. 28, 782–793 (2008).

    Article  PubMed  Google Scholar 

  49. Chamberlain, S. R. et al. Orbitofrontal dysfunction in patients with obsessive-compulsive disorder and their unaffected relatives. Science 321, 421–422 (2008).

    Article  PubMed  Google Scholar 

  50. Freyer, T. et al. Frontostriatal activation in patients with obsessive-compulsive disorder before and after cognitive behavioral therapy. Psychol. Med. 41, 207–216 (2011).

    Article  PubMed  Google Scholar 

  51. Remijnse, P. L. et al. Differential frontal-striatal and paralimbic activity during reversal learning in major depressive disorder and obsessive-compulsive disorder. Psychol. Med. 39, 1503–1518 (2009).

    Article  PubMed  Google Scholar 

  52. Apergis-Schoute, A. M. et al. Perseveration and shifting in obsessive-compulsive disorder as a function of uncertainty, punishment, and serotonergic medication. Biol. Psychiatry Glob. Open Sci. 4, 326–335 (2024).

    Article  PubMed  Google Scholar 

  53. Hirschtritt, M. E., Bloch, M. H. & Mathews, C. A. Obsessive-compulsive disorder: advances in diagnosis and treatment. JAMA 317, 1358–1367 (2017).

    Article  PubMed  Google Scholar 

  54. Sanchez, C., Reines, E. H. & Montgomery, S. A. A comparative review of escitalopram, paroxetine, and sertraline: are they all alike? Int. Clin. Psychopharmacol. 29, 185–196 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Saxena, S. & Rauch, S. L. Functional neuroimaging and the neuroanatomy of obsessive-compulsive disorder. Psychiatr. Clin. North Am. 23, 563–586 (2000).

    Article  PubMed  Google Scholar 

  56. Foa, E. B. et al. The Obsessive-Compulsive Inventory: development and validation of a short version. Psychol. Assess. 14, 485–496 (2002).

    Article  PubMed  Google Scholar 

  57. Clark, D. A. et al. A question of perspective: the association between intrusive thoughts and obsessionality in 11 countries. J. Obsessive Compuls. Relat. Disord. 3, 292–299 (2014).

    Article  Google Scholar 

  58. Fullana, M. A. et al. Obsessions and compulsions in the community: prevalence, interference, help-seeking, developmental stability, and co-occurring psychiatric conditions. Am. J. Psychiatry 166, 329–336 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Abramowitz, J. S. et al. The relevance of analogue studies for understanding obsessions and compulsions. Clin. Psychol. Rev. 34, 206–217 (2014).

    Article  PubMed  Google Scholar 

  60. Draper, C. F. et al. Menstrual cycle rhythmicity: metabolic patterns in healthy women. Sci. Rep. 8, 1–15 (2018).

    Article  Google Scholar 

  61. Jovanovic, H. et al. A PET study of 5-HT1A receptors at different phases of the menstrual cycle in women with premenstrual dysphoria. Psychiatry Res. 148, 185–193 (2006).

    Article  PubMed  Google Scholar 

  62. Frank, M. J., Moustafa, A. A., Haughey, H. M., Curran, T. & Hutchison, K. E. Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proc. Natl Acad. Sci. USA 104, 16311–16316 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Guitart-Masip, M. et al. Go and no-go learning in reward and punishment: interactions between affect and effect. NeuroImage 62, 154–166 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Conceição, V. A. & Maia, T. V. in Computational Psychiatry: A Primer (ed. Seriès, P.) 205–246 (MIT Press, 2020).

  65. Collins, A. G. E. & Frank, M. J. Cognitive control over learning: creating, clustering, and generalizing task-set structure. Psychol. Rev. 120, 190–229 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Collins, A. G. E. & Frank, M. J. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning. Cognition 152, 160–169 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Babayan, B. M., Uchida, N. & Gershman, S. J. Belief state representation in the dopamine system. Nat. Commun. 9, 1891 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Gershman, S. J. & Uchida, N. Believing in dopamine. Nat. Rev. Neurosci. 20, 703–714 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Starkweather, C. K., Babayan, B. M., Uchida, N. & Gershman, S. J. Dopamine reward prediction errors reflect hidden-state inference across time. Nat. Neurosci. 20, 581–589 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Lartillot, N. & Philippe, H. Computing Bayes factors using thermodynamic integration. Syst. Biol. 55, 195–207 (2006).

    Article  PubMed  Google Scholar 

  71. Sengupta, B., Friston, K. J. & Penny, W. D. Gradient-free MCMC methods for dynamic causal modelling. NeuroImage 112, 375–381 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Daunizeau, J., Adam, V. & Rigoux, L. VBA: a probabilistic treatment of nonlinear models for neurobiological and behavioural data. PLoS Comput. Biol. 10, e1003441 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Hampton, A. N., Bossaerts, P. & O’Doherty, J. P. The role of the ventromedial prefrontal cortex in abstract state-based inference during decision making in humans. J. Neurosci. 26, 8360–8367 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Schlagenhauf, F. et al. Striatal dysfunction during reversal learning in unmedicated schizophrenia patients. NeuroImage 89, 171–180 (2014).

    Article  PubMed  Google Scholar 

  75. Kanen, J. W., Ersche, K. D., Fineberg, N. A., Robbins, T. W. & Cardinal, R. N. Computational modelling reveals contrasting effects on reinforcement learning and cognitive flexibility in stimulant use disorder and obsessive-compulsive disorder: remediating effects of dopaminergic D2/3 receptor agents. Psychopharmacology 236, 2337–2358 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Marzuki, A. A. et al. Association of environmental uncertainty with altered decision-making and learning mechanisms in youths with obsessive-compulsive disorder. JAMA Netw. Open 4, e2136195 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Rygula, R. et al. Role of central serotonin in anticipation of rewarding and punishing outcomes: effects of selective amygdala or orbitofrontal 5-HT depletion. Cereb. Cortex 25, 3064–3076 (2015).

    Article  PubMed  Google Scholar 

  78. Luo, Q. et al. Comparable roles for serotonin in rats and humans for computations underlying flexible decision-making. Neuropsychopharmacology 49, 600–608 (2024).

    Article  PubMed  Google Scholar 

  79. Robbins, T. W. & Cardinal, R. N. Computational psychopharmacology: a translational and pragmatic approach. Psychopharmacology 236, 2295–2305 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Langley, C. et al. Chronic escitalopram in healthy volunteers has specific effects on reinforcement sensitivity: a double-blind, placebo-controlled semi-randomised study. Neuropsychopharmacology 48, 664–670 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Gardier, A., Malagié, I., Trillat, A., Jacquot, C. & Artigas, F. Role of 5-HT1A autoreceptors in the mechanism of action of serotoninergic antidepressant drugs: recent findings from in vivo microdialysis studies. Fundam. Clin. Pharmacol. 10, 16–27 (1996).

    Article  PubMed  Google Scholar 

  82. Altman, D. G. & Bland, J. M. Statistics notes: absence of evidence is not evidence of absence. BMJ 311, 485 (1995).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Cohen, J. & Nee, J. C. M. Estimators for two measures of association for set correlation. Educ. Psychol. Meas. 44, 907–917 (1984).

    Article  Google Scholar 

  84. Guitart-Masip, M. et al. Differential, but not opponent, effects of l-DOPA and citalopram on action learning with reward and punishment. Psychopharmacology 231, 955–966 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Cools, R., Nakamura, K. & Daw, N. D. Serotonin and dopamine: unifying affective, activational, and decision functions. Neuropsychopharmacology 36, 98–113 (2011).

    Article  PubMed  Google Scholar 

  86. Daw, N. D., Kakade, S. & Dayan, P. Opponent interactions between serotonin and dopamine. Neural Netw. 15, 603–616 (2002).

    Article  PubMed  Google Scholar 

  87. Dayan, P. & Huys, Q. J. M. Serotonin in affective control. Annu. Rev. Neurosci. 32, 95–126 (2009).

    Article  PubMed  Google Scholar 

  88. Bloch, M. H., McGuire, J., Landeros-Weisenberger, A., Leckman, J. F. & Pittenger, C. Meta-analysis of the dose-response relationship of SSRI in obsessive-compulsive disorder. Mol. Psychiatry 15, 850–855 (2010).

    Article  PubMed  Google Scholar 

  89. Pittenger, C. & Bloch, M. H. Pharmacological treatment of obsessive-compulsive disorder. Psychiatr. Clin. North Am. 37, 375–391 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Furr, A., Lapiz-Bluhm, M. D. & Morilak, D. A. 5-HT2A receptors in the orbitofrontal cortex facilitate reversal learning and contribute to the beneficial cognitive effects of chronic citalopram treatment in rats. Int. J. Neuropsychopharmacol. 15, 1295–1305 (2012).

    Article  PubMed  Google Scholar 

  91. Walker, S. C., Robbins, T. W. & Roberts, A. C. Differential contributions of dopamine and serotonin to orbitofrontal cortex function in the marmoset. Cereb. Cortex 19, 889–898 (2009).

    Article  PubMed  Google Scholar 

  92. Bari, A. et al. Serotonin modulates sensitivity to reward and negative feedback in a probabilistic reversal learning task in rats. Neuropsychopharmacology 35, 1290–1301 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Blier, P., Pineyro, G., El Mansari, M., Bergeron, R. & Montigny, C. Role of somatodendritic 5-HT autoreceptors in modulating 5-HT neurotransmission. Ann. N. Y. Acad. Sci. 861, 204–216 (1998).

    Article  PubMed  Google Scholar 

  94. Hjorth, S. Serotonin 5-HT1A autoreceptor blockade potentiates the ability of the 5-HT reuptake inhibitor citalopram to increase nerve terminal output of 5-HT in vivo: a microdialysis study. J. Neurochem. 60, 776–779 (1993).

    Article  PubMed  Google Scholar 

  95. Beyer, C. E. & Cremers, T. I. F. H. Do selective serotonin reuptake inhibitors acutely increase frontal cortex levels of serotonin? Eur. J. Pharmacol. 580, 350–354 (2008).

    Article  PubMed  Google Scholar 

  96. Nord, M., Finnema, S. J., Halldin, C. & Farde, L. Effect of a single dose of escitalopram on serotonin concentration in the non-human and human primate brain. Int. J. Neuropsychopharmacol. 16, 1577–1586 (2013).

    Article  PubMed  Google Scholar 

  97. Invernizzi, R., Belli, S. & Samanin, R. Citalopram’s ability to increase the extracellular concentrations of serotonin in the dorsal raphe prevents the drug’s effect in the frontal cortex. Brain Res. 584, 322–324 (1992).

    Article  PubMed  Google Scholar 

  98. Hirano, K. et al. Relationship between brain serotonin transporter binding, plasma concentration and behavioural effect of selective serotonin reuptake inhibitors. Br. J. Pharmacol. 144, 695–702 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Meyer, J. H. et al. Serotonin transporter occupancy of five selective serotonin reuptake inhibitors at different doses: an [11C]DASB positron emission tomography study. Am. J. Psychiatry 161, 826–835 (2004).

    Article  PubMed  Google Scholar 

  100. Chamberlain, S. R. et al. Neurochemical modulation of response inhibition and probabilistic learning in humans. Science 311, 861–863 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Skandali, N. et al. Dissociable effects of acute SSRI (escitalopram) on executive, learning and emotional functions in healthy humans. Neuropsychopharmacology 43, 2645 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Fritze, S., Spanagel, R. & Noori, H. R. Adaptive dynamics of the 5-HT systems following chronic administration of selective serotonin reuptake inhibitors: a meta-analysis. J. Neurochem. 142, 747–755 (2017).

    Article  PubMed  Google Scholar 

  103. Cools, R., Roberts, A. C. & Robbins, T. W. Serotoninergic regulation of emotional and behavioural control processes. Trends Cogn. Sci. 12, 31–40 (2008).

    Article  PubMed  Google Scholar 

  104. Boureau, Y.-L. & Dayan, P. Opponency revisited: competition and cooperation between dopamine and serotonin. Neuropsychopharmacology 36, 74–97 (2011).

    Article  PubMed  Google Scholar 

  105. Michely, J., Eldar, E., Erdman, A., Martin, I. M. & Dolan, R. J. Serotonin modulates asymmetric learning from reward and punishment in healthy human volunteers. Commun. Biol. 5, 812 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Loosen, A. M. & Hauser, T. U. Towards a computational psychiatry of juvenile obsessive-compulsive disorder. Neurosci. Biobehav. Rev. 118, 631–642 (2020).

    Article  PubMed  Google Scholar 

  107. Fradkin, I., Adams, R. A., Parr, T., Roiser, J. P. & Huppert, J. D. Searching for an anchor in an unpredictable world: a computational model of obsessive compulsive disorder. Psychol. Rev. 127, 672–699 (2020).

    Article  PubMed  Google Scholar 

  108. Fradkin, I., Ludwig, C., Eldar, E. & Huppert, J. D. Doubting what you already know: uncertainty regarding state transitions is associated with obsessive compulsive symptoms. PLoS Comput. Biol. 16, e1007634 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  109. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (American Psychiatric Association, 2013).

  110. Foa, E. B. et al. DSM-IV field trial: obsessive-compulsive disorder. Am. J. Psychiatry 152, 90–96 (1995).

    PubMed  Google Scholar 

  111. Jakubovski, E. et al. Dimensional correlates of poor insight in obsessive-compulsive disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 35, 1677–1681 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Vaghi, M. M. et al. Compulsivity reveals a novel dissociation between action and confidence. Neuron 96, 348–354.e4 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  113. White, J., Tannenbaum, C., Klinge, I., Schiebinger, L. & Clayton, J. The integration of sex and gender considerations into biomedical research: lessons from international funding agencies. J. Clin. Endocrinol. Metab. 106, 3034–3048 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Hall, E. & Steiner, M. Serotonin and female psychopathology. Women's Health 9, 85–97 (2013).

    Article  PubMed  Google Scholar 

  115. Thorne, B. N., Ellenbroek, B. A. & Day, D. J. Sex bias in the serotonin transporter knockout model: implications for neuropsychiatric disorder research. Neurosci. Biobehav. Rev. 134, 104547 (2022).

    Article  PubMed  Google Scholar 

  116. Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals (International Committee of Medical Journal Editors (ICMJE), 2025); https://www.icmje.org/icmje-recommendations.pdf

  117. Gönner, S., Leonhart, R. & Ecker, W. The Obsessive–Compulsive Inventory-Revised (OCI-R): validation of the German version in a sample of patients with OCD, anxiety disorders, and depressive disorders. J. Anxiety Disord. 22, 734–749 (2008).

    Article  PubMed  Google Scholar 

  118. Watkins, C. J. C. H. Learning from Delayed Rewards. PhD thesis, Univ. Cambridge (1989).

  119. Maia, T. V. & Conceição, V. A. The roles of phasic and tonic dopamine in tic learning and expression. Biol. Psychiatry 82, 401–412 (2017).

    Article  PubMed  Google Scholar 

  120. Clarke, H. F. et al. Orbitofrontal dopamine depletion upregulates caudate dopamine and alters behavior via changes in reinforcement sensitivity. J. Neurosci. 34, 7663–7676 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Daw, N. D. in Decision Making, Affect, and Learning: Attention and Performance XXIII (eds Delgado, M. R., Phelps, E. A. & Robbins, T. W.) 3–38 (Oxford Univesity Press, 2011).

  122. Robert, C. & Casella, G. Monte Carlo Statistical Methods (Springer, 2013).

  123. Penny, W. D. Comparing dynamic causal models using AIC, BIC and free energy. NeuroImage 59, 319–330 (2012).

    Article  PubMed  Google Scholar 

  124. Frässle, S. et al. TAPAS: an open-source software package for translational neuromodeling and computational psychiatry. Front. Psychiatry 12, 680811 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Aponte, E. A. et al. mpdcm: a toolbox for massively parallel dynamic causal modeling. J. Neurosci. Methods 257, 7–16 (2016).

    Article  PubMed  Google Scholar 

  126. Aponte, E. A., Schöbi, D., Stephan, K. E. & Heinzle, J. The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades. PLoS Comput. Biol. 13, e1005692 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  127. Calderhead, B. & Girolami, M. Estimating Bayes factors via thermodynamic integration and population MCMC. Comput. Stat. Data Anal. 53, 4028–4045 (2009).

    Article  Google Scholar 

  128. Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian Data Analysis Vol. 2 (Chapman & Hall/CRC, 2014).

  129. Rigoux, L., Stephan, K. E., Friston, K. J. & Daunizeau, J. Bayesian model selection for group studies—revisited. NeuroImage 84, 971–985 (2014).

    Article  PubMed  Google Scholar 

  130. Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J. & Friston, K. J. Bayesian model selection for group studies. NeuroImage 46, 1004–1017 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Winkler, A. M., Renaud, O., Smith, S. M. & Nichols, T. E. Permutation inference for canonical correlation analysis. NeuroImage 220, 117065 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  132. McGarigal, K., Stafford, S. & Cushman, S. Multivariate Statistics for Wildlife and Ecology Research (Springer, 2000).

  133. Sherry, A. & Henson, R. K. Conducting and interpreting canonical correlation analysis in personality research: a user-friendly primer. J. Pers. Assess. 84, 37–48 (2005).

    Article  PubMed  Google Scholar 

  134. RStudio Team. RStudio: integrated development environment for R. http://www.rstudio.com/ (2020).

  135. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

  136. The jamovi project. jamovi (version 1.6.23) [Computer Software]. https://www.jamovi.org (2020).

  137. Hohl, K. & Dolcos, S. Measuring cognitive flexibility: a brief review of neuropsychological, self-report, and neuroscientific approaches. Front. Hum. Neurosci. 18, 1331960 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich. Dataset from a probabilistic Go/NoGo reversal-learning task. Zenodo https://doi.org/10.5281/zenodo.10066829 (2024).

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Acknowledgements

We are very grateful to K. Stephan for his generous guidance and support of this project. We gratefully acknowledge support by the Clinical Research Priority Program ‘Molecular Imaging’ at UZH, Fundação para a Ciência e a Tecnologia, Portugal (PhD fellowship PD/BD/105852/2014 to V.A.C.), the Tourette Association of America (T.V.M.) and the Brainstorm Program at the Robert J. & Nancy D. Carney Institute for Brain Science (F.H.P.). V.A.C. and T.V.M. also acknowledge their participation in a twinning project (SynaNet) from the European Union Horizon 2020 Programme (project number 692340). We also acknowledge the valued study support of our colleagues J. Heinzle, T. Baumgartner, K. Treiber, C. Schnyder, A. Diaconescu, S. Iglesias, H. Haker and Q. Huys. Finally, we thank A. Winkler (University of Texas Rio Grande Valley) for guidance concerning the permutation tests used to obtain P values for the cross-loadings in the canonical correlation analysis.

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Author notes

  1. These authors contributed equally: Vasco A. Conceição, Frederike H. Petzschner.

Authors and Affiliations

  1. Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

    Vasco A. Conceição & Tiago V. Maia

  2. Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland

    Frederike H. Petzschner, David M. Cole, Katharina V. Wellstein & Sudhir Raman

  3. Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI, USA

    Frederike H. Petzschner

  4. Department of Psychiatry, University of Basel, Basel, Switzerland

    David M. Cole

  5. Hunter Medical Research Institute, University of Newcastle, Callaghan, New South Wales, Australia

    Katharina V. Wellstein

  6. Institute for Clinical Chemistry, University Hospital Zurich, Zurich, Switzerland

    Daniel Müller

  7. University Hospital Basel, Basel, Switzerland

    Daniel Müller

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  1. Vasco A. Conceição
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Contributions

The study was conceptualized by T.V.M., F.H.P., V.A.C. and D.M.C. The behavioral task was designed by T.V.M., V.A.C. and F.H.P., and implemented and piloted by F.H.P. The data were collected by D.M.C., K.V.W. and F.H.P. The computational models were designed by V.A.C., F.H.P., S.R. and T.V.M., and implemented by V.A.C. and S.R. The statistical analyses were designed by V.A.C., F.H.P. and T.V.M., and implemented by V.A.C. and F.H.P. Escitalopram plasma levels were measured by D.M. The article was written by V.A.C., F.H.P. and T.V.M.

Corresponding authors

Correspondence to Frederike H. Petzschner or Tiago V. Maia.

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Competing interests

S.R. is currently at Novartis, but all his work on this article was performed while he was at the Translational Neuromodeling Unit. The other authors declare no competing interests.

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Nature Mental Health thanks Praveen Suthaharan, Hang Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Behavior and model fit for an example participant.

Each plot represents a shell. Points at the bottom and top of each plot indicate the participant’s trial-by-trial responses: black points at the bottom represent NoGo responses; colored points at the top represent Go responses, with the color representing the reinforcement received (blue, reward; yellow, neutral; red, punishment). a, Go probability, P(Go), predicted by the winning model (S–S–R–8; solid black line) and its corresponding S–R model (S–R–8; dashed gray line). Horizontal color-coded bars indicate the shell’s season (blue, rewarding; yellow, neutral; red, punishing). Model S–S–R–8 tracked the participant’s behavior better, especially when seasons recurred (last two seasons for each shell). b, Belief trajectory for model S–S–R–8. Horizontal color-coded bars on top indicate the season inferred by the model; horizontal color-coded bars immediately below indicate the true season. The lines depict the posterior probability distribution of the beliefs about the shell season (state), P(State), on each trial: the beliefs that the shell is in rewarding, neutral and punishing states are represented in blue, yellow and red, respectively. In the model, each state corresponds to an outcome distribution; states are not labeled a priori as rewarding, neutral or punishing. To represent the inferred seasons in the top horizontal bars and the beliefs in the plotted lines, we assigned rewarding, neutral and punishing labels to states whose final distribution consisted mostly of reward, neutral or punishment outcomes, respectively. Given that the task included three season types, the model assumed that there could be up to three hidden states (see ‘S–S–R models’ in ‘Computational models’ in the Supplementary Methods). In all plots except the top-left plot, the model only inferred up to two states; a state that was never inferred does not have outcomes associated, so we represent it in black. Some lines overlap and therefore are not visible. The line markers indicate the state that the model believes applies after each trial; usually, this corresponds to the state with the largest posterior probability, but sometimes it does not because the model has a (parameter-dependent) tendency to stick with the prior belief (see ‘S–S–R models’ in ‘Computational models’ in the Supplementary Methods).

Extended Data Fig. 2 Model S–R–8, the S–R equivalent of winning model S–S–R–8, replicates some of the results concerning participants’ (n = 44) group-level behavior (panels a and b) but not the result that requires state inference (panel c).

This figure is equivalent to Fig. 4 but for model S–R–8 rather than model S–S–R–8. a, Average (±s.e.m.) Go probability, P(Go), for each season type: rewarding (R), neutral (N) and punishing (P). We analyzed the model fits using the approach described in the caption of Fig. 4a. Like for participants (see caption of Fig. 4a), P(Go) for the fits of model S–R–8 was largest for rewarding, intermediate for neutral and smallest for punishing seasons (main effect of season type: \({\chi }_{2}^{2}=971.71\), P < 10−16; repeated contrasts: rewarding versus neutral, b = 0.68, z = 8.79, two-sided P < 10−16, 95% CI (0.53, 0.83), OR = 1.97; neutral versus punishing, b = 1.39, z = 23.41, two-sided P < 10−16, 95% CI (1.27, 1.50), OR = 4.00). b, Average (±s.e.m.) P(Go) as a function of the shell phase, for each shell. The fits from model S–R–8 tracked the changes in participants’ choices as the seasons changed. c, Average (±s.e.m.) probability of a correct response, P(Correct), in the phases following the first and second identical state transitions: phases 2 and 4, respectively. As in Fig. 4c, P(Correct) was calculated using only R and P seasons, as there was no correct response for N seasons. We analyzed P(Correct) using a rmANOVA with independent variables phase (2 versus 4) and data type (participants versus fits of the S–R–8 model). As expected, the interaction was significant (F1,43 = 11.19, P = 0.002, \({\eta }_{p}^{2}=0.21\)). Planned contrasts confirmed that P(Correct) was significantly greater in phase 4 than in phase 2 for participants (mean difference = 0.05, t43 = 2.61, two-sided P = 0.012, 95% CI (0.01, 0.10), Cohen’s d = 0.39), like in Fig. 4c, but not for fits of the S–R–8 model (mean difference = 0.00, t43 = 0.01, two-sided P = 0.994, 95% CI (–0.02, 0.02), Cohen’s d = 0.00). Thus, unlike participants and model S–S–R–8 (Fig. 4c), model S–R–8 did not show better performance in phase 4 than in phase 2. NS: not significant; *P < 0.05; ***P < 0.001.

Extended Data Fig. 3 Behavior was better explained by a model that extended S–R learning with state inference than by models that implemented alternative hypotheses.

The winning state-inference model (model S–S–R–8) explained behavior better than models that implemented learning rates that could vary across the task (S–Rα(T) models) or models that implemented stimulus stickiness (S–RStimStick models; see ‘Alternative model families’ in ‘Computational models’ in the Methods and in the Supplementary Methods). a, Model frequencies. b, PEPs. The horizontal red line indicates the threshold for confident selection of a model. State-inference model S–S–R–8 was confidently selected as the best model.

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Supplementary Methods, Supplementary Results, Tables 1–4 and Figs. 1–8.

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Conceição, V.A., Petzschner, F.H., Cole, D.M. et al. Serotonin reduces belief stickiness. Nat. Mental Health (2026). https://doi.org/10.1038/s44220-026-00621-9

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