What is mood? A computational perspective
Open accessAbstract
The neurobiological understanding of mood, and by extension mood disorders, remains elusive despite decades of research implicating several neuromodulator systems. This review considers a new approach based on existing theories of functional brain organisation. The free energy principle (a.k.a. active inference), and its instantiation in the Bayesian brain, offers a complete and simple formulation of mood. It has been proposed that emotions reflect the precision of - or certainty about - the predicted sensorimotor/interoceptive consequences of action. By extending this reasoning, in a hierarchical setting, we suggest mood states act as (hyper) priors over uncertainty (i.e. emotions). Here, we consider the same computational pathology in the proprioceptive and interoceptive (behavioural and autonomic) domain in order to furnish an explanation for mood disorders. This formulation reconciles several strands of research at multiple levels of enquiry.
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High-level priors constraining more domain-general affective states such as mood would also be relaxed under the REBUS model.
“High-level priors constraining more domain-general affective states such as mood (Clark et al. 2018) would also be relaxed under the REBUS model.”
From Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution (2021) · cites this paper as Clark et al. 2018
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Cites
Molecular Mechanisms of Depression: Perspectives on New Treatment Strategies
Lang, U. E., & Borgwardt, S. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350094 · Cited by 8,501 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · cited 2× · see citing works
“However, modern accounts of brain function reject the classical notion of the brain as a passive organ in favour of theories of embodiment and self-organisation (Friston, 2010; Seth, 2013; Corlett & Fletcher, 2014).”
BackgroundHelplessness: On Depression, Development, and Death
Seligman, M. E. P. (1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 6,181 · see citing works
Epigenetic programming by maternal behavior
Weaver, I. C. G., Cervoni, N., Champagne, F. A., D'Alessio, A. C., Sharma, S., Seckl, J. R., Dymov, S., Szyf, M., & Meaney, M. J. (2004) · Nature Neuroscience · doi:10.1038/nn1276 · Cited by 6,177 · see citing works
Stress, adaptation, and disease. Allostasis and allostatic load
McEWEN, B. S. (1998) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.1998.tb09546.x · Cited by 5,092 · see citing works
A Meta-Analysis of Cytokines in Major Depression
Dowlati, Y., Herrmann, N., Swardfager, W., Liu, H., Sham, L., Reim, E. K., & Lanctôt, K. L. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.09.033 · Cited by 4,729 · see citing works
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583 · see citing works
Requirement of hippocampal neurogenesis for the behavioral effects of antidepressants
Santarelli, L., Saxe, M., Gross, C., Surget, A., Battaglia, F., Dulawa, S., Weisstaub, N., Lee, J., Duman, R., Arancio, O., et al. (2003) · Science · doi:10.1126/science.1083328 · Cited by 4,321 · see citing works
Heim, C., & Nemeroff, C. B. (2001) · Biological Psychiatry · doi:10.1016/s0006-3223(01)01157-x · Cited by 2,791 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 2× · see citing works
“In current predictive coding formulations of the Bayesian brain, expectation and prediction error units are thought to occupy deep and superficial pyramidal layers of cortex, respectively (Friston & Kiebel, 2009; Bastos et al. 2012; Shipp et al. 2013); such that predictions generated, from expectations, at one level o…”
BackgroundThe Corticosteroid Receptor Hypothesis of Depression
Holsboer, F. (2000) · Neuropsychopharmacology · doi:10.1016/s0893-133x(00)00159-7 · Cited by 2,181 · see citing works
The mesolimbic dopamine reward circuit in depression
Nestler, E. J., & Carlezon, W. A. (2006) · Biological Psychiatry · doi:10.1016/j.biopsych.2005.09.018 · Cited by 2,059 · see citing works
Allostasis and Allostatic Load Implications for Neuropsychopharmacology
McEwen, B. (2000) · Neuropsychopharmacology · doi:10.1016/s0893-133x(99)00129-3 · Cited by 1,833 · see citing works
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831 · see citing works
Interoceptive inference, emotion, and the embodied self
Seth, A. K. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.007 · Cited by 1,816 · cited 2× · see citing works
“We argue that progress may be facilitated by an appreciation of the dynamic and selforganising nature of neurobiological systems (Seth, 2013; Fotopoulou, 2015; Clark et al. 2016).”
BackgroundInteroceptive predictions in the brain
Barrett, L. F., & Simmons, W. K. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3950 · Cited by 1,766 · see citing works
Berridge, K. C., & Kringelbach, M. L. (2015) · Neuron · doi:10.1016/j.neuron.2015.02.018 · Cited by 1,730 · see citing works
The link between childhood trauma and depression: insights from HPA axis studies in humans
Heim, C., Newport, D. J., Mletzko, T., Miller, A. H., & Nemeroff, C. B. (2008) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2008.03.008 · Cited by 1,704 · see citing works
Hippocampal volume and depression: a meta-analysis of MRI studies
Videbech, P., & Ravnkilde, B. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.11.1957 · Cited by 1,590 · see citing works
Predictive coding under the free-energy principle
Friston, K., & Kiebel, S. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0300 · Cited by 1,579 · see citing works
Allostasis: A New Paradigm to Explain Arousal Pathology
Sterling, P., & Eyer, J. (1988) · Zenodo · doi:10.5281/zenodo.13537700 · Cited by 1,450 · see citing works
Handbook of Life Stress, Cognition and Health
PASTOR, L. H. (1989) · American Journal of Psychiatry · doi:10.1176/ajp.146.10.1347 · Cited by 1,402 · see citing works
A free energy principle for the brain
Friston, K., Kilner, J., & Harrison, L. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2006.10.001 · Cited by 1,369 · see citing works
Amygdala, medial prefrontal cortex, and hippocampal function in PTSD
SHIN, L. M., RAUCH, S. L., & PITMAN, R. K. (2006) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1364.007 · Cited by 1,203 · see citing works
Heim, C., & Binder, E. B. (2012) · Experimental Neurology · doi:10.1016/j.expneurol.2011.10.032 · Cited by 986 · see citing works
Active interoceptive inference and the emotional brain
Seth, A. K., & Friston, K. J. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0007 · Cited by 945 · cited 1× · see citing works
“Recent theoretical arguments have converged on the idea that emotional states reflect changes in the uncertainty about the somatic consequences of action (Joffily & Coricelli, 2013; Wager et al. 2015; Seth & Friston, 2016).”
BackgroundThe science of resilience: implications for the prevention and treatment of depression
Southwick, S. M., & Charney, D. S. (2012) · Science · doi:10.1126/science.1222942 · Cited by 875 · see citing works
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.018 · Cited by 809 · see citing works
Anterior insular cortex and emotional awareness
Gu, X., Hof, P. R., Friston, K. J., & Fan, J. (2013) · Journal of Comparative Neurology · doi:10.1002/cne.23368 · Cited by 712 · cited 1× · see citing works
“In this setting, negative emotions contextualise events that induce expectations of unpredictability, while positive emotions refer to events that resolve uncertainty and confer a feeling of control (Barrett & Satpute, 2013; Gu et al. 2013).”
Background(2000) · PubMed · Cited by 685 · see citing works
Raadsheer, F. C., Hoogendijk, W. J. G., Stam, F. C., Tilders, F. J. H., & Swaab, D. F. (2008) · Neuroendocrinology · doi:10.1159/000126778 · Cited by 667 · see citing works
Action understanding and active inference
Friston, K., Mattout, J., & Kilner, J. (2011) · Biological Cybernetics · doi:10.1007/s00422-011-0424-z · Cited by 665 · see citing works
Cortisol and post-traumatic stress disorder in adults: Systematic review and meta-analysis
Meewisse, M.-L., Reitsma, J. B., De Vries, G.-J., Gersons, B. P. R., & Olff, M. (2007) · British Journal of Psychiatry · doi:10.1192/bjp.bp.106.024877 · Cited by 661 · see citing works
Barrett, L. F., & Satpute, A. B. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.12.012 · Cited by 653 · see citing works
Uncertainty and stress: Why it causes diseases and how it is mastered by the brain
Peters, A., McEwen, B. S., & Friston, K. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.05.004 · Cited by 652 · see citing works
Self-citationActive Inference, homeostatic regulation and adaptive behavioural control
Pezzulo, G., Rigoli, F., & Friston, K. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.09.001 · Cited by 650 · cited 2× · see citing works
“…pre-empt the need for homoeostasis [e.g. in a hypogly-caemic state, by attenuating the precision of prediction errors reporting hypoglycaemia, we can suspend the reflex mobilisation of glucose and act on the world (by eating) to fulfil and maintain higher level predictions] (Pezzulo et al. 2015).”
BackgroundAn aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641 · see citing works
The GABA system in anxiety and depression and its therapeutic potential
Möhler, H. (2011) · Neuropharmacology · doi:10.1016/j.neuropharm.2011.08.040 · Cited by 599 · see citing works
Hohwy, J. (2016) · Noûs · doi:10.1111/nous.12062 · Cited by 578 · see citing works
Reward and Punishment Processing in Depression
Eshel, N., & Roiser, J. P. (2010) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.01.027 · Cited by 575 · see citing works
Carpenter, L. L., Carvalho, J. P., Tyrka, A. R., Wier, L. M., Mello, A. F., Mello, M. F., Anderson, G. M., Wilkinson, C. W., & Price, L. H. (2007) · Biological Psychiatry · doi:10.1016/j.biopsych.2007.05.002 · Cited by 564 · see citing works
An active inference theory of allostasis and interoception in depression
Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0011 · Cited by 554 · see citing works
Harmer, C. J., Goodwin, G. M., & Cowen, P. J. (2009) · British Journal of Psychiatry · doi:10.1192/bjp.bp.108.051193 · Cited by 542 · see citing works
(1993) · American Journal of Psychiatry · doi:10.1176/ajp.150.1.83 · Cited by 517 · see citing works
Morimoto, M., Morita, N., Ozawa, H., Yokoyama, K., & Kawata, M. (1996) · Neuroscience Research · doi:10.1016/s0168-0102(96)01105-4 · Cited by 475 · see citing works
Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., et al. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00550 · Cited by 437 · cited 1× · see citing works
“This means that allostatic control is precluded – and is replaced by low-level homoeostatic responses (Barrett et al. 2016; Stephan et al. 2016; Peters et al. 2017).”
BackgroundAntidepressants increase human hippocampal neurogenesis by activating the glucocorticoid receptor
Anacker, C., Zunszain, P. A., Cattaneo, A., Carvalho, L. A., Garabedian, M. J., Thuret, S., Price, J., & Pariante, C. M. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.26 · Cited by 435 · see citing works
Smith, M. E. (2005) · Hippocampus · doi:10.1002/hipo.20102 · Cited by 395 · see citing works
Nutt, D., Demyttenaere, K., Janka, Z., Aarre, T., Bourin, M., Canonico, P. L., Carrasco, J. L., & Stahl, S. (2007) · Journal of Psychopharmacology · doi:10.1177/0269881106069938 · Cited by 366 · see citing works
Knowing how much you don't know: a neural organization of uncertainty estimates
Bach, D. R., & Dolan, R. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3289 · Cited by 339 · see citing works
Clarifying the roles of homeostasis and allostasis in physiological regulation.
Ramsay, D. S., & Woods, S. C. (2014) · Psychological Review · doi:10.1037/a0035942 · Cited by 339 · see citing works
Emotional Valence and the Free-Energy Principle
Joffily, M., & Coricelli, G. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003094 · Cited by 322 · see citing works
Wang, X., & Krystal, J. H. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.018 · Cited by 309 · see citing works
Almeida, J. R. C. D., Versace, A., Mechelli, A., Hassel, S., Quevedo, K., Kupfer, D. J., & Phillips, M. L. (2009) · Biological Psychiatry · doi:10.1016/j.biopsych.2009.03.024 · Cited by 305 · see citing works
Repeated dexamethasone suppression test during depressive illness
Holsboer, F., Liebl, R., & Hofschuster, E. (1982) · Journal of Affective Disorders · doi:10.1016/0165-0327(82)90039-8 · Cited by 301 · see citing works
Maternal prenatal depressive symptoms predict infant<i>NR3C1</i>1F and<i>BDNF</i>IV DNA methylation
Braithwaite, E., Kundakovic, M., Ramchandani, P., Murphy, S., & Champagne, F. (2015) · Epigenetics · doi:10.1080/15592294.2015.1039221 · Cited by 299 · see citing works
Hart, A. S., Rutledge, R. B., Glimcher, P. W., & Phillips, P. E. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.2489-13.2014 · Cited by 294 · see citing works
Relationship of childhood maltreatment to the onset and course of major depression in adulthood
Bernet, C. Z., & Stein, M. B. (1999) · Depression and Anxiety · doi:10.1002/(sici)1520-6394(1999)9:4 · Cited by 289 · see citing works
Duque, A., & Vázquez, C. (2014) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2014.09.005 · Cited by 288 · see citing works
Local circuit regulation of paraventricular nucleus stress integration
Herman, J. P., Tasker, J. G., Ziegler, D. R., & Cullinan, W. E. (2002) · Pharmacology Biochemistry and Behavior · doi:10.1016/s0091-3057(01)00681-5 · Cited by 274 · see citing works
A Bayesian Model of Category-Specific Emotional Brain Responses
Wager, T. D., Kang, J., Johnson, T. D., Nichols, T. E., Satpute, A. B., & Barrett, L. F. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004066 · Cited by 262 · see citing works
Testing the Reward Prediction Error Hypothesis with an Axiomatic Model
Rutledge, R. B., Dean, M., Caplin, A., & Glimcher, P. W. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.1747-10.2010 · Cited by 244 · see citing works
Neuronal plasticity and antidepressant actions
Castrén, E., & Hen, R. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2012.12.010 · Cited by 243 · see citing works
Reflections on agranular architecture: predictive coding in the motor cortex
Shipp, S., Adams, R. A., & Friston, K. J. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2013.09.004 · Cited by 239 · see citing works
Duman, R. S. (2014) · Depression and Anxiety · doi:10.1002/da.22227 · Cited by 213 · see citing works
Corticotropin-releasing hormone in depression and post-traumatic stress disorder
Kasckow, J. W., Baker, D. G., & Geracioti, T. D. (2001) · Peptides · doi:10.1016/s0196-9781(01)00399-0 · Cited by 203 · see citing works
Using Attentional Bias Modification as a Cognitive Vaccine Against Depression
Browning, M., Holmes, E. A., Charles, M., Cowen, P. J., & Harmer, C. J. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.04.014 · Cited by 197 · see citing works
The depressed brain: an evolutionary systems theory
Badcock, P. B., Davey, C. G., Whittle, S., Allen, N. B., & Friston, K. J. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.005 · Cited by 190 · see citing works
Harmer, C. J., & Cowen, P. J. (2013) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2012.0407 · Cited by 183 · see citing works
Towards a genuinely medical model for psychiatric nosology
Nesse, R. M., & Stein, D. J. (2012) · BMC Medicine · doi:10.1186/1741-7015-10-5 · Cited by 163 · see citing works
Sladky, R., Höflich, A., Küblböck, M., Kraus, C., Baldinger, P., Moser, E., Lanzenberger, R., & Windischberger, C. (2015) · Cerebral Cortex · doi:10.1093/cercor/bht279 · Cited by 152 · see citing works
Schlösser, R. G. M., Wagner, G., Koch, K., Dahnke, R., Reichenbach, J. R., & Sauer, H. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.002 · Cited by 148 · see citing works
Risk and risk prediction error signals in anterior insula
Bossaerts, P. L. (2010) · Brain Structure and Function · doi:10.1007/s00429-010-0253-1 · Cited by 144 · see citing works
Stress risk factors and stress-related pathology: Neuroplasticity, epigenetics and endophenotypes
Radley, J. J., Kabbaj, M., Jacobson, L., Heydendael, W., Yehuda, R., & Herman, J. P. (2011) · Stress · doi:10.3109/10253890.2011.604751 · Cited by 140 · see citing works
Lu, Q., Li, H., Luo, G., Wang, Y., Tang, H., Han, L., & Yao, Z. (2012) · Neuroscience Letters · doi:10.1016/j.neulet.2012.06.058 · Cited by 129 · see citing works
Dissociable Effects of Serotonin and Dopamine on the Valuation of Harm in Moral Decision Making
Crockett, M. J., Siegel, J. Z., Kurth-Nelson, Z. L., Ousdal, O. T., Story, G. W., Frieband, C., Grosse-Rueskamp, J. M., Dayan, P., & Dolan, R. J. (2015) · Current Biology · doi:10.1016/j.cub.2015.05.021 · Cited by 123 · see citing works
Sullivan, T. P., Fehon, D. C., Andrés-Hyman, R. C., Lipschitz, D. S., & Grilo, C. M. (2006) · Journal of Traumatic Stress · doi:10.1002/jts.20092 · Cited by 122 · see citing works
Childhood trauma in bipolar disorder
Watson, S., Gallagher, P., Dougall, D., Porter, R., Moncrieff, J., Ferrier, I. N., & Young, A. H. (2014) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867413516681 · Cited by 122 · see citing works
Self-citationvan der Gucht, E., Morriss, R. K., Lancaster, G., Kinderman, P., & Bentall, R. P. (2009) · The British Journal of Psychiatry · doi:10.1192/bjp.bp.107.047894 · Cited by 113 · see citing works
Fronto-limbic disconnection in bipolar disorder
Radaelli, D., Papa, G. S., Vai, B., Poletti, S., Smeraldi, E., Colombo, C., & Benedetti, F. (2014) · European Psychiatry · doi:10.1016/j.eurpsy.2014.04.001 · Cited by 106 · see citing works
Computational psychiatry: a Rosetta Stone linking the brain to mental illness
Corlett, P. R., & Fletcher, P. C. (2014) · The Lancet Psychiatry · doi:10.1016/s2215-0366(14)70298-6 · Cited by 105 · see citing works
Are computational models of any use to psychiatry?
Huys, Q. J. M., Moutoussis, M., & Williams, J. H. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.03.001 · Cited by 102 · see citing works
Mood Instability and Reward Dysregulation—A Neurocomputational Model of Bipolar Disorder
Mason, L., Eldar, E., & Rutledge, R. B. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.3163 · Cited by 100 · see citing works
Reward Processing in Adolescents With Bipolar I Disorder
Singh, M. K., Chang, K., Kelley, R. G., Cui, X., Sherdell, L., Howe, M. E., Gotlib, I. H., & Reiss, A. L. (2012) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2012.10.004 · Cited by 95 · see citing works
Ota, K. T., & Duman, R. S. (2013) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.05.022 · Cited by 94 · see citing works
Sladky, R., Höflich, A., Atanelov, J., Kraus, C., Baldinger, P., Moser, E., Lanzenberger, R., & Windischberger, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050050 · Cited by 85 · see citing works
The Clinical Translation of a Measure of Gain Control: The Contrast-Contrast Effect Task
Barch, D. M., Carter, C. S., Dakin, S. C., Gold, J. M., Luck, S. J., MacDonald, A. W., Ragland, J. D., Silverstein, S. M., & Strauss, M. E. (2011) · Schizophrenia Bulletin · doi:10.1093/schbul/sbr154 · Cited by 85 · see citing works
Dynamic causal modelling of precision and synaptic gain in visual perception — an EEG study
Brown, H. R., & Friston, K. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.044 · Cited by 79 · see citing works
An Active Inference view of cognitive control
Pezzulo, G. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00478 · Cited by 68 · cited 2× · see citing works
“The active minimisation of free energy is known as active inference , which means the brain can selectively sample from data that concur with its current expectations (Friston et al. 2011; Pezzulo, 2012).”
BackgroundVai, B., Bulgarelli, C., Godlewska, B. R., Cowen, P. J., Benedetti, F., & Harmer, C. J. (2016) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2016.09.640 · Cited by 66 · see citing works
Early life trauma, depression and the glucocorticoid receptor gene—An epigenetic perspective
Smart, C., Strathdee, G., Watson, S., Murgatroyd, C., & McAllister-Williams, R. H. (2015) · Psychological Medicine · doi:10.1017/s0033291715001555 · Cited by 61 · see citing works
Unifying treatments for depression: an application of the free energy principle
Chekroud, A. M. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00153 · Cited by 55 · see citing works
The virtual bodily self: Mentalisation of the body as revealed in anosognosia for hemiplegia
Fotopoulou, A. (2014) · Consciousness and Cognition · doi:10.1016/j.concog.2014.09.018 · Cited by 52 · see citing works
Computational Psychiatry and the Challenge of Schizophrenia
Krystal, J. H., Murray, J. D., Chekroud, A. M., Corlett, P. R., Yang, G., Wang, X., & Anticevic, A. (2017) · Schizophrenia Bulletin · doi:10.1093/schbul/sbx025 · Cited by 49 · see citing works
Nantharat, M., Wanitchanon, T., Amesbutr, M., Tammachote, R., & Praphanphoj, V. (2015) · Genetics and Molecular Research · doi:10.4238/2015.december.29.15 · Cited by 46 · see citing works
Vai, B., Poletti, S., Radaelli, D., Dallaspezia, S., Bulgarelli, C., Locatelli, C., Bollettini, I., Falini, A., Colombo, C., Smeraldi, E., et al. (2015) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2015.07.015 · Cited by 42 · see citing works
Perturbed reward processing in pediatric bipolar disorder: an antisaccade study
Mueller, S. C., Ng, P., Temple, V., Hardin, M. G., Pine, D. S., Leibenluft, E., & Ernst, M. (2010) · Journal of Psychopharmacology · doi:10.1177/0269881109353462 · Cited by 42 · see citing works
Valiengo, L. L., Soeiro-de-Souza, M. G., Marques, A. H., Moreno, D. H., Juruena, M. F., Andreazza, A. C., Gattaz, W. F., & Machado-Vieira, R. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.11.046 · Cited by 36 · see citing works
Dopaminergic Impact on Cortical Excitability in Humans
Nitsche, M. A., Monte-Silva, K. K., Kuo, M., & Paulus, W. (2010) · Reviews in the Neurosciences · doi:10.1515/revneuro.2010.21.4.289 · Cited by 36 · see citing works
Gao, S., Klomp, A., Wu, J., Swaab, D. F., & Bao, A. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.12.003 · Cited by 33 · see citing works
Sher, L., Oquendo, M. A., Burke, A. K., Cooper, T. B., & Mann, J. J. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.06.049 · Cited by 30 · see citing works
The aetiopathogenesis of fatigue: unpredictable, complex and persistent
Clark, J. E., Ng, W., Watson, S., & Newton, J. L. (2016) · British Medical Bulletin · doi:10.1093/bmb/ldv057 · Cited by 19 · see citing works
I Predict, Therefore I Am: Perturbed Predictive Coding Under Ketamine and in Schizophrenia
Corlett, P. R. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.12.007 · Cited by 12 · see citing works
Palma‐Gudiel, H., Córdova‐Palomera, A., Marqués-Feixa, L., Miquel, F. C., & Fañanás, L. (2016) · European Psychiatry · doi:10.1016/j.eurpsy.2016.01.025 · Cited by 2 · see citing works
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REBUS and the anarchic brain: Toward a unified model of the brain action of psychedelics
Carhart-Harris, R. L., & Friston, K. J. (2019) · Pharmacological Reviews · doi:10.1124/pr.118.017160 · Cited by 1,180 · see citing works
Being a Beast Machine: The Somatic Basis of Selfhood
Seth, A. K., & Tsakiris, M. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.008 · Cited by 357 · see citing works
“Clark and colleagues similarly emphasize precision, associating major depression with being certain about encountering uncertain environments, thereby precluding effective allostatic regulation [83].”
Emotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354 · see citing works
A step-by-step tutorial on active inference and its application to empirical data
Smith, R., Friston, K. J., & Whyte, C. J. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2021.102632 · Cited by 246 · see citing works
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
Hesp, C., Smith, R., Parr, T., Allen, M., Friston, K. J., & Ramstead, M. J. D. (2021) · Neural Computation · doi:10.1162/neco_a_01341 · Cited by 246 · see citing works
Self-citationDistorted Cognitive Processes in Major Depression: A Predictive Processing Perspective
Κube, T., Schwarting, R. K. W., Rozenkrantz, L., Glombiewski, J. A., & Rief, W. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.07.017 · Cited by 238 · see citing works
The power of predictions: An emerging paradigm for psychological research
Hutchinson, J. B., & Barrett, L. F. (2019) · Current Directions in Psychological Science · doi:10.1177/0963721419831992 · Cited by 216 · see citing works
Smith, R. S., Kuplicki, R., Feinstein, J. S., Forthman, K. L., Stewart, J. L., Paulus, M. P., Tulsa 1000 investigators, & Khalsa, S. S. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008484 · Cited by 195 · see citing works
Smith, R., Badcock, P., & Friston, K. J. (2021) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13138 · Cited by 174 · see citing works
Badcock, P. B., Friston, K. J., Ramstead, M. J. D., Ploeger, A., & Hohwy, J. (2019) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-019-00721-3 · Cited by 166 · see citing works
Animal affect and decision-making
Mendl, M., & Paul, E. S. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.01.025 · Cited by 161 · see citing works
Perry, A., Roberts, G. M. P., Mitchell, P. B., & Breakspear, M. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0267-2 · Cited by 146 · see citing works
Computational psychiatry: from synapses to sentience
Friston, K. J. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01743-z · Cited by 158 · see citing works
When Beliefs Face Reality: An Integrative Review of Belief Updating in Mental Health and Illness
Κube, T., & Rozenkrantz, L. (2020) · Perspectives on Psychological Science · doi:10.1177/1745691620931496 · Cited by 150 · see citing works
Above and beyond the concrete: The diverse representational substrates of the predictive brain
Gilead, M., Trope, Y., & Liberman, N. (2019) · Behavioral and Brain Sciences · doi:10.1017/s0140525x19002000 · Cited by 129 · see citing works
Smith, R. S., Parr, T., & Friston, K. J. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02844 · Cited by 128 · see citing works
Harrison, L., Kats, A., Williams, M. E., & Aziz‐Zadeh, L. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00103 · Cited by 117 · see citing works
Sandved-Smith, L., Hesp, C., Mattout, J., Friston, K. J., Antoine Lutz, A., & Ramstead, M. J. D. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab018 · Cited by 116 · see citing works
Revisiting the Cognitive Model of Depression: The Role of Expectations
Rief, W., & Joormann, J. (2019) · Clinical Psychology in Europe · doi:10.32872/cpe.v1i1.32605 · Cited by 93 · see citing works
Rethinking post-traumatic stress disorder – A predictive processing perspective
Κube, T., Berg, M., Kleim, B., & Herzog, P. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.014 · Cited by 101 · see citing works
Smith, R., Lane, R. D., Parr, T., & Friston, K. J. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.09.002 · Cited by 89 · see citing works
Self-citationPlay in predictive minds: A cognitive theory of play.
Andersen, M. M., Kiverstein, J., Miller, M., & Roepstorff, A. (2023) · Psychological Review · doi:10.1037/rev0000369 · Cited by 122 · see citing works
Lawson, R. P., Bisby, J. A., Nord, C. L., Burgess, N., & Rees, G. E. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.043 · Cited by 85 · see citing works
Smith, R. S., Kirlić, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., Feinstein, J. S., Paulus, M. P., & Aupperle, R. L. (2021) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.200032 · Cited by 78 · see citing works
Schiller, D., Yu, A. N. C., Alia‐Klein, N., Becker, S., Cromwell, H. C., Dolcos, F., Eslinger, P. J., Frewen, P., Kemp, A. H., Pace-Schott, E. F., et al. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105450 · Cited by 86 · see citing works
Ray, D., Bezmaternykh, D., Mel’nikov, M., Friston, K. J., & Das, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105730118 · Cited by 73 · see citing works
Self-citationGut inference: A computational modelling approach
Smith, R. S., Mayeli, A., Taylor, S., Zoubi, O. A., Naegele, J., & Khalsa, S. S. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108152 · Cited by 68 · see citing works
Biased belief updating in depression
Kube, T. (2023) · Clinical Psychology Review · doi:10.1016/j.cpr.2023.102298 · Cited by 61 · see citing works
Everything is connected: Inference and attractors in delusions
Adams, R. A., Vincent, P., Benrimoh, D. A., Friston, K. J., & Parr, T. (2021) · Schizophrenia Research · doi:10.1016/j.schres.2021.07.032 · Cited by 58 · see citing works
Arnaldo, I., Corcoran, A. W., Friston, K. J., & Ramstead, M. J. D. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104590 · Cited by 56 · see citing works
Self-citationKiverstein, J. D., Miller, M., & Rietveld, E. (2020) · Neuroscience of Consciousness · doi:10.1093/nc/niaa003 · Cited by 56 · see citing works
Dynamics of amygdala connectivity in bipolar disorders: a longitudinal study across mood states
Rey, G., Bolton, T. A. W., Gaviria, J., Piguet, C., Preti, M. G., Favre, S., Aubry, J., Ville, D. V. D., & Vuilleumier, P. O. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-021-01038-x · Cited by 55 · see citing works
Herzog, P., Κube, T., & Faßbinder, E. (2022) · Psychological Medicine · doi:10.1017/s0033291722002458 · Cited by 55 · see citing works
Transdiagnostic computations of uncertainty: towards a new lens on intolerance of uncertainty
Sandhu, T. R., Xiao, B., & Lawson, R. P. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105123 · Cited by 51 · see citing works
A Step-by-Step Tutorial on Active Inference and its Application to Empirical DataPreprint
Smith, R. S., Friston, K. J., & Whyte, C. J. (2021) · doi:10.31234/osf.io/b4jm6 · Cited by 51 · see citing works
Smith, R. S., Steklis, H. D., Steklis, N. G., Weihs, K. L., & Lane, R. D. (2020) · Biological Psychology · doi:10.1016/j.biopsycho.2020.107925 · Cited by 46 · see citing works
Osteopathy and Mental Health: An Embodied, Predictive, and Interoceptive Framework
Bohlen, L., Shaw, R., Cerritelli, F., & Esteves, J. E. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.767005 · Cited by 51 · see citing works
Harris, D., Wilkinson, S., & Ellmers, T. J. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105115 · Cited by 46 · see citing works
Is depression a global brain disorder with topographic dynamic reorganization?
Northoff, G., & Hirjak, D. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02995-9 · Cited by 45 · see citing works
Toward the unity of pathological and exertional fatigue: A predictive processing model
Greenhouse‐Tucknott, A., Butterworth, J. B., Wrightson, J. G., Smeeton, N. J., Critchley, H., Dekerle, J., & Harrison, N. A. (2021) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-021-00958-x · Cited by 46 · see citing works
Interoceptive technologies for psychiatric interventions: From diagnosis to clinical applications
Schoeller, F., Horowitz, A. H., Jain, A., Maes, P., Reggente, N., Christov-Moore, L., Pezzulo, G., Barca, L., Allen, M., Salomon, R., et al. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105478 · Cited by 42 · see citing works
Self-citationAn Investigation of the Free Energy Principle for Emotion Recognition
Demekas, D., Parr, T., & Friston, K. J. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00030 · Cited by 41 · see citing works
Deane, G. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab024 · Cited by 38 · see citing works
“High-level priors constraining more domain-general affective states such as mood (Clark et al. 2018) would also be relaxed under the REBUS model.”
BackgroundVilliger, D., & Trachsel, M. (2023) · Journal of Medical Ethics · doi:10.1136/jme-2022-108816 · Cited by 41 · see citing works
Alpha-to-beta- and gamma-band activity reflect predictive coding in affective visual processing
Strube, A., Rose, M., Fazeli, S., & Büchel, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-02939-z · Cited by 39 · see citing works
A predictive coding framework for understanding major depression
Gilbert, J. R., Wusinich, C., & Zarate, C. A. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.787495 · Cited by 38 · see citing works
From filters to fillers: an active inference approach to body image distortion in the selfie era
Tremblay, S. C., Tremblay, S. E., & Poirier, P. (2020) · AI & Society · doi:10.1007/s00146-020-01015-w · Cited by 38 · see citing works
Tracking mood fluctuations with functional network patterns
Mirchi, N., Betzel, R. F., Bernhardt, B. C., Dagher, A., & Mišić, B. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy107 · Cited by 35 · see citing works
Learned uncertainty: The free energy principle in anxiety
McGovern, H. T., De Foe, A., Biddell, H., Leptourgos, P., Corlett, P., Bandara, K., & Hutchinson, B. T. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.943785 · Cited by 33 · see citing works
How negative mood hinders belief updating in depression: results from two experimental studies
Κube, T., Kirchner, L., Gärtner, T., & Glombiewski, J. A. (2021) · Psychological Medicine · doi:10.1017/s0033291721002798 · Cited by 34 · see citing works
Putica, A., & Agathos, J. A. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105836 · Cited by 32 · see citing works
Oversampled and undersolved: Depressive rumination from an active inference perspective
Berg, M., Feldmann, M., Kirchner, L., & Κube, T. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104873 · Cited by 32 · see citing works
Young, H. A., Gaylor, C. M., de‐Kerckhove, D., Watkins, H., & Benton, D. (2019) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2019.0244 · Cited by 31 · see citing works
Willinger, D., Häberling, I. S., Ilioska, I., Berger, G. E., Walitza, S., & Brem, S. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1386984 · Cited by 30 · see citing works
Dissolving the self:Active inference, psychedelics, and ego-dissolution
Deane, G. (2020) · Philosophy and the Mind Sciences · doi:10.33735/phimisci.2020.i · Cited by 37 · see citing works
Synthesising boredom: a predictive processing approach
Darling, T. (2023) · Synthese · doi:10.1007/s11229-023-04380-3 · Cited by 29 · see citing works
Minds and Brains, Sleep and Psychiatry
Hobson, J. A., Gott, J., & Friston, K. J. (2020) · Psychiatric Research and Clinical Practice · doi:10.1176/appi.prcp.20200023 · Cited by 27 · see citing works
Krupnik, V. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.657738 · Cited by 27 · see citing works
YANAGISAWA, H. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.698252 · Cited by 27 · see citing works
Willinger, D., Karipidis, I. I., Beltrani, S., Pietro, S. V. D., Sladky, R., Walitza, S., Stämpfli, P., & Brem, S. (2019) · eNeuro · doi:10.1523/eneuro.0079-19.2019 · Cited by 27 · see citing works
A social inference model of idealization and devaluation.
Story, G. W., Smith, R. S., Moutoussis, M., Berwian, I. M., Nolte, T., Bilek, E., Siegel, J. Z., & Dolan, R. J. (2023) · Psychological Review · doi:10.1037/rev0000430 · Cited by 32 · see citing works
Storm on predictive brain: A neurocomputational account of ketamine antidepressant effect
Bottemanne, H., Berkovitch, L., Gauld, C., Balcerac, A., Schmidt, L., Mouchabac, S., & Fossati, P. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105410 · Cited by 28 · see citing works
Trauma or Drama: A Predictive Processing Perspective on the Continuum of Stress
Krupnik, V. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01248 · Cited by 29 · see citing works
Induced illusory body ownership in borderline personality disorder
Neustadter, E. S., Fineberg, S. K., Leavitt, J., Carr, M. M., & Corlett, P. R. (2019) · Neuroscience of Consciousness · doi:10.1093/nc/niz017 · Cited by 24 · see citing works
Testing predictive coding theories of autism spectrum disorder using models of active inference
Arthur, T., Vine, S., Buckingham, G., Brosnan, M., Wilson, M., & Harris, D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011473 · Cited by 21 · see citing works
“Given that similar types of ‘hyperpriors’ have already been proposed as a critical element of mood disorders [80], further research into sensorimotor skills could examine the persistence of participants’ volatility beliefs (e.g., volatility of volatility) and/or the mechanisms that underpin…”
BackgroundEnhanced top-down sensorimotor processing in somatic anxiety
Bouziane, I., Das, M., Friston, K. J., Caballero‐Gaudes, C., & Ray, D. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02061-2 · Cited by 23 · see citing works
Sarasso, P., Billeci, M., Ronga, I., Raffone, F., Martiadis, V., & Petta, G. D. (2024) · Psychopathology · doi:10.1159/000539714 · Cited by 24 · see citing works
Transcutaneous Auricular Vagus Nerve Stimulation to Improve Emotional State
Ruiz, A. A. (2024) · Biomedicines · doi:10.3390/biomedicines12020407 · Cited by 24 · see citing works
Niu, L., Luo, D., Chen, X., Wang, M., Zhou, W., Zhang, D., & Xiao, S. (2019) · Journal of the International AIDS Society · doi:10.1002/jia2.25332 · Cited by 22 · see citing works
Aberrant EEG signal variability at a specific temporal scale in major depressive disorder
Yun, S., & Jeong, B. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.05.011 · Cited by 22 · see citing works
Neville, V., Dayan, P., Gilchrist, I. D., Paul, E. S., & Mendl, M. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008555 · Cited by 21 · see citing works
Instability and Uncertainty Are Critical for Psychotherapy: How the Therapeutic Alliance Opens Us Up
Connolly, P. M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.784295 · Cited by 21 · see citing works
What we think about when we think about predictive processing.
Corlett, P. R., Mohanty, A., & MacDonald, A. W. (2020) · Journal of Abnormal Psychology · doi:10.1037/abn0000632 · Cited by 20 · see citing works
Body as First Teacher: The Role of Rhythmic Visceral Dynamics in Early Cognitive Development
Corcoran, A. W., Perrykkad, K., Feuerriegel, D., & Robinson, J. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916231185343 · Cited by 19 · see citing works
Yankouskaya, A., Denholm-Smith, T., Yi, D., Greenshaw, A. J., Cao, B., & Sui, J. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.833625 · Cited by 19 · see citing works
Will psilocybin lose its magic in the clinical setting?
Hayes, C., Wahba, M., & Watson, S. (2022) · Therapeutic Advances in Psychopharmacology · doi:10.1177/20451253221090822 · Cited by 19 · see citing works
Tabas, A., & von Kriegstein, K. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.644743 · Cited by 20 · see citing works
Ramos-Grille, I., Weyant, J., Wormwood, J. B., Robles, M., Vallès, V., Camprodon, J. A., & Chanes, L. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.05.030 · Cited by 19 · see citing works
How Psychedelic-Assisted Treatment Works in the Bayesian Brain
Villiger, D. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.812180 · Cited by 19 · see citing works
Sarasso, P., Francesetti, G., Roubal, J., Gecele, M., Ronga, I., Neppi-Mòdona, M., & Sacco, K. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.906188 · Cited by 20 · see citing works
Krupnik, V. (2024) · Frontiers in Neural Circuits · doi:10.3389/fncir.2024.1283372 · Cited by 19 · see citing works
Active Inference in Psychology and Psychiatry: Progress to Date?
Badcock, P. B., & Davey, C. G. (2024) · Entropy · doi:10.3390/e26100833 · Cited by 20 · see citing works
Mediterranean diet, interoception and mental health: Is it time to look beyond the ‘Gut-brain axis’?
Young, H. A., Freegard, G., & Benton, D. (2022) · Physiology & Behavior · doi:10.1016/j.physbeh.2022.113964 · Cited by 18 · see citing works
Trait anxiety is linked to increased usage of priors in a perceptual decision making task
Kraus, N., Niedeggen, M., & Hesselmann, G. (2020) · Cognition · doi:10.1016/j.cognition.2020.104474 · Cited by 16 · see citing works
A Computational Model of Hopelessness and Active-Escape Bias in Suicidality
Karvelis, P., & Diaconescu, A. O. (2022) · Computational Psychiatry · doi:10.5334/cpsy.80 · Cited by 21 · see citing works
Maladaptive Avoidance Learning in the Orbitofrontal Cortex in Adolescents With Major Depression
Willinger, D., Karipidis, I. I., Neuer, S., Emery, S., Rauch, C., Häberling, I. S., Berger, G. E., Walitza, S., & Brem, S. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.06.005 · Cited by 16 · see citing works
Free energy model of emotional valence in dual-process perceptions
YANAGISAWA, H., Wu, X., Ueda, K., & Kato, T. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.10.027 · Cited by 16 · see citing works
Interoceptive Technologies for Psychiatric Interventions: From Diagnosis to Clinical ApplicationsPreprint
Schoeller, F., Horowitz, A. H., Maes, P., Jain, A., Reggente, N., Christov‐Moore, L., Trousselard, M., Klein, A., Barca, L., Pezzulo, G., et al. (2022) · doi:10.31234/osf.io/sqr6z · Cited by 15 · see citing works
Anticipating and enacting worlds: moods, illness and psychobehavioral adaptation
Crippen, M. (2023) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-023-09934-x · Cited by 14 · see citing works
The Human AffectomePreprint
Schiller, D., Yu, A. N. C., Alia‐Klein, N., Becker, S., Cromwell, H. C., Dolcos, F., Eslinger, P. J., Frewen, P., Kemp, A. H., Pace-Schott, E. F., et al. (2022) · doi:10.31234/osf.io/9nu32 · Cited by 14 · see citing works
A Computational Neuroscience Perspective on the Change Process in Psychotherapy
Smith, R. P., Lane, R. D., Nadel, L., & Moutoussis, M. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190881511.003.0015 · Cited by 13 · see citing works
Théories du complot et COVID-19 : comment naissent les croyances complotistes ?
Bottemanne, H. (2022) · L'Encephale · doi:10.1016/j.encep.2021.12.005 · Cited by 10 · see citing works
Würtz, F., Κube, T., Woud, M. L., Margraf, J., & Blackwell, S. E. (2024) · Cognitive Therapy and Research · doi:10.1007/s10608-023-10454-w · Cited by 12 · see citing works
A continuum hypothesis of psychotomimetic rapid antidepressants
Haarsma, J., Harmer, C. J., & Tamm, S. (2021) · Brain and Neuroscience Advances · doi:10.1177/23982128211007772 · Cited by 12 · see citing works
Bayesian Approach to Psychotherapy Integration: Strategic Modification of Priors
Krupnik, V. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00356 · Cited by 11 · see citing works
Czekalla, N., Schröder, A., Mayer, A. V., Stierand, J., Stolz, D. S., Κube, T., Korn, C. W., Wilhelm, I., Klein, J. P., Paulus, F. M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.09.593087 · Cited by 10 · see citing works
Erwartungsfokussierte psychotherapeutische Interventionen bei Depression: ein Behandlungsmanual
Wilhelm, M., Bleichhardt, G. M., Ewen, A.-C. I., & Rief, W. (2022) · Psychotherapeut · doi:10.1007/s00278-022-00590-1 · Cited by 10 · see citing works
Smith, R. S., Kirlić, N., Stewart, J. L., Touthang, J., Kuplicki, R., Khalsa, S. S., Paulus, M. P., T Investigators, & Aupperle, R. L. (2020) · doi:10.31234/osf.io/t2dhn · Cited by 9 · see citing works
Modelling mood updating: a proof of principle study
Clark, J. E., & Watson, S. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2022.175 · Cited by 9 · see citing works
Döbler, N. A., & Carbon, C. (2023) · Integrative Psychological and Behavioral Science · doi:10.1007/s12124-023-09797-6 · Cited by 9 · see citing works
- Authors
- Clark, J. E., Watson, S., & Friston, K. J.
- Affiliations
- Newcastle University (GB)
- Cumbria Northumberland Tyne and Wear NHS Foundation Trust (GB)
- University College London (GB)
- Corresponding author
- Clark, James E
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2018-02-26
- Volume
- 48
- Issue
- 14
- Pages
- 2277-2284
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- PMC
- PMC6340107
- OpenAlex
- W2789154098
- Cited by
- 204
- References
- 104
- Citations by year
- 2026: 21 · 2025: 28 · 2024: 21 · 2023: 36 · 2022: 34 · 2021: 21 · 2020: 23 · 2019: 17 · 2018: 3
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Psychological Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Newcastle University ePrints (Newcastle Univesity) (cc-by)
- Funders
- Wellcome Trust (088130, 088130/Z/09/Z)
- National Institute for Health and Care Research
- Newcastle University
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- mood, mood disorders, free energy principle, active inference, Bayesian brain, emotions, interoception, proprioception, uncertainty, hyperprior, computational psychiatry
- MeSH terms
- Affect, Brain, Humans, Models, Theoretical, Mood Disorders
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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