Major depression is a prevalent mental disorder that leads to persistent negative mood and tremendous suffering in affected individuals. However, the biological realization of this disorder and associated symptom clusters remain poorly understood. Recently, phenomenological accounts of major depressive disorder and contributions to the emerging predictive processing account have provided valuable insights into the phenomenological and neuro-functional components that lead to manifestations of major depressive episodes. The purpose of this paper is to weave together these different strands of research to develop a predictive processing account of major depressive disorder. In doing so, I will relate personal-level descriptions of associated phenomenal experiences to a sub-personal-level predictive processing account of the functional realization of major depression. I will argue that pervasive symptoms of the disorder, which include a diminished sense of agency, fatigue, social withdrawal, and rumination, are integrated by existential feelings of loss and impossibility. These phenomenal experiences, I will argue, are associated with dysfunctional processes of prediction error minimization, which are characterized by an overall decrease of the causal contributions of active inference and by distorted precision estimates. The emerging account promises to contribute to a better understanding of the complex processes that give rise to depressive experiences.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
I take it that these numbers refer to the sex, rather than the gender of individuals who are at risk of experiencing a major depressive episode. It would interesting to enquire to what extent the numbers would be different if the gender of individuals were included as a factor.
Given that the key terminology used by proponents of PP accounts is used in different ways, I would like to clarify how I understand the terms ‘predictive processing’, ‘predictive coding’, ‘perceptual inference’, and ‘active inference’. I use ‘predictive processing’ in a fairly general way, as an umbrella term for perceptual, active, cognitive, and affective processes that are guided by the principle of prediction error minimization in the spirit of Clark (2013). Furthermore, I am also following Clark (2013) by maintaining that ‘predictive coding’ is a “data compression strategy”, while ‘predictive processing’ refers to “the use of that strategy in the special context of hierarchical systems deploying probabilistic generative models” (Clark 2013, p. 202). Based on work by Clark (2013, 2016), Hohwy (2013), and Seth (2015), I see ‘perceptual inference’ and ‘active inference’ as two different ways of minimizing prediction error in the sense specified above. Perceptual inference and active inference (together with precision estimation) are constituents of ‘predictive processing’, not generalizations of ‘predictive coding’.
Interoception is the sense of the physiological states of the body, proprioception is the sense of the positions of parts of the body in space, and exteroception is the sense of states and processes originating in the local environment.
In the literature on the free energy principle, surprisal is sometimes also called surprise. Following Clark (2013), to avoid the misunderstanding that this information-theoretical quantity is equal to the phenomenal experience of being surprised, I prefer to use the more technical term surprisal and I will talk about ‘surprisaling’ signals.
I will pick up on this feature of depressive experience in Section 5.
It has been argued that the experiences of one’s own body in MDD share important features with experiences in somatic illnesses as they are traditionally construed in medicine (Fuchs 2013; Ratcliffe 2015). This applies especially to the phenomenal “[e]xperience of bodily lethargy, heaviness and pain” (Ratcliffe 2015, p. 87). This is consistent with empirical research on MDD assuming that it shares components with inflammation and other symptoms of somatic illness traditionally conceived (Barrett and Simmons 2015; Stephan et al. 2016).
This view is also supported by Solomon’s (2015) autobiographically informed description of being depressed: “You lose the ability to trust anyone, to be touched, to grieve. Eventually, you are simply absent from yourself” (p. 19). Similarly, a subject diagnosed with MDD also describes the pervasive experience of loss: “You know that you have lost life itself. You’ve lost a habitable earth. You’ve lost the invitation to live that the universe extends to us at every moment. You’ve lost something that people don’t know even is” (Hornstein 2009; quoted after Ratcliffe 2015, p. 15).
In his Heideggerian account of major depressive disorder, Fernandez (2014) suggests to make a distinction between two kinds of existential feelings, namely situatedness (Befindlichkeit) and mood (Stimmung). On his view, the experience of being situated in the world is the most basic and essential existential feeling an individual can have. The situatedness of an individual, of finding oneself in the world, “is manifest through some Stimmung, some mood or other” (Fernandez 2014, p. 601). However, given that situatedness cannot be felt independently from a certain mood, the question arises whether the consideration of situatedness and mood as at least partly distinct phenomenological concepts are heuristically necessary for an account of the phenomenology of major depressive disorder.
This does not mean that depressive experiences can be reduced to a feeling of loss or that the feeling of loss ‘encapsulates’ these experiences. Rather, the assumption is that the experience of loss integrates various experiences that can be ascribed to individuals diagnosed with MDD.
For a recent account of depressive rumination and its relation to mind wandering and creativity, see Fabry (2018). The different types of spontaneous cognition, it is argued in that paper, can be described as different coupling relations of cognitive agents and their local environment, ranging from weak coupling in cases of depressive rumination to strong coupling in cases of creative cognition.
Allostasis is defined as “the process by which the brain efficiently maintains energy regulation in the body” (Kleckner et al. 2017, p. 1).
Evidence in support of low concentrations of neurotransmitters, especiall fo serotonin and dopamine, in MDD comes from neuroscientific research integrating empirical results and computational modelling (e.g., Cools et al. 2011; Dunlop and Nemeroff 2007). Barrett et al. (2016) indicate that their functional and computational hypotheses about the relation of low concentrations of neurotransmitters and precision estimations is currently only indirectly supported by empirical research and future studies directly testing their hypothesis are clearly needed.
Based on recent research in computational neuroscience on the realization of neuromodulation (Dayan 2012), I suggest that these low concentrations of neurotransmitters might be located in the striatum and in prefrontal areas.
For constructive criticism on this line of argumentation, see Klein (2018).
I am greateful to a reviewer for a suggestion to include a discussion of Klein (2018) into my considerations on the dark room problem and the phenomenology of MDD. Note that my current goal is not to solve the dark room problem or to enrich Klein’s (2018) discussion of it. I am interested in relating the phenomenology of MDD and PP and the important insight provided by Klein (2018) that the dark room problem is a problem of providing an account of motivation.
Adams, R. A., Shipp, S., & Friston, K. J. (2013). Predictions not commands: Active inference in the motor system. Brain Structure and Function, 218(3), 611–643.
Adolphs, R. (2010). What does the amygdala contribute to social cognition? Annals of the New York Academy of Sciences, 1191(1), 42–61.
Alexander, W. H., & Brown, J. W. (2018). Frontal cortex function as derived from hierarchical predictive coding. Scientific Reports, 8(1), 1–11.
Allen, N. B., & Badcock, P. B. T. (2003). The social risk hypothesis of depressed mood: Evolutionary, psychosocial, and neurobiological perspectives. Psychological Bulletin, 129(6), 887–913.
Allen, M., & Friston, K. J. (2016). From cognitivism to autopoiesis: Towards a computational framework for the embodied mind. Synthese, 1–24.
Allen, M., Frank, D., Schwarzkopf, D. S., Fardo, F., Winston, J. S., Hauser, T. U., & Rees, G. (2016). Unexpected arousal modulates the influence of sensory noise on confidence. ELife, 5, 1–17.
Badcock, P. B., Davey, C. G., Whittle, S., Allen, N. B., & Friston, K. J. (2017). The depressed brain: An evolutionary systems theory. Trends in Cognitive Sciences, 21(3), 182–194.
Badcock, P. B., Friston, K. J., Ramstead, M. J. D., Ploeger, A., & Hohwy, J. (2019). The hierarchically mechanistic mind: An evolutionary systems theory of the human brain, cognition, and behavior. Cognitive, Affective, & Behavioral Neuroscience, 1–33. https://doi.org/10.3758/s13415-019-00721-3.
Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience, 16(7), 419–429.
Barrett, L. F., Quigley, K. S., & Hamilton, P. (2016). An active inference theory of allostasis and interoception in depression. Philosophical Transactions of the Royal Society B, 371(1708), 1–17.
Binitie, A. (1975). A factor-analytical study of depression across cultures (African and European). The British Journal of Psychiatry, 127(6), 559–563.
Brosch, T., Pourtois, G., & Sander, D. (2010). The perception and categorisation of emotional stimuli: A review. Cognition and Emotion, 24(3), 377–400.
Brown, H., Adams, R. A., Parees, I., Edwards, M., & Friston, K. (2013). Active inference, sensory attenuation and illusions. Cognitive Processing, 14(4), 411–427.
Christoff, K., Irving, Z. C., Fox, K. C. R., Spreng, R. N., & Andrews-Hanna, J. R. (2016). Mind-wandering as spontaneous thought: A dynamic framework. Nature Reviews Neuroscience, 17, 718–731.
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(03), 181–204. https://doi.org/10.1017/S0140525X12000477.
Clark, A. (2015). Embodied prediction. In T. Metzinger & J. M. Windt (Eds.), Open MIND (pp. 1–21). Frankfurt am Main: MIND Group.
Clark, A. (2017). Busting out: Predictive brains, embodied minds, and the puzzle of the evidentiary veil. Noûs, 51(4), 727–753. https://doi.org/10.1111/nous.12140.
Clark, A. (2016). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford, New York: Oxford University Press.
Colombo, M. (2013). Constitutive relevance and the personal/subpersonal distinction. Philosophical Psychology, 26(4), 547–570.
Colombo, M., & Fabry, R. E. (under review). Underlying delusion: Predictive processing, looping effects, and the personal/sub-personal distinction.
Cools, R., Nakamura, K., & Daw, N. D. (2011). Serotonin and dopamine: Unifying affective, activational, and decision functions. Neuropsychopharmacology, 36(1), 98–113.
Csukly, G., Czobor, P., Szily, E., Takács, B., & Simon, L. (2009). Facial expression recognition in depressed subjects: The impact of intensity level and arousal dimension. The Journal of Nervous and Mental Disease, 197(2), 98–103.
Damasio, A. (1999). The feeling of what happens: Body and emotion in the making of consciousness. London: Vintage Books.
Dayan, P. (2012). Twenty-five lessons from computational neuromodulation. Neuron, 76(1), 240–256.
de Bruin, L., & Michael, J. (2017). Prediction error minimization: Implications for embodied cognition and the extended mind hypothesis. Brain and Cognition, 112, 58–63. https://doi.org/10.1016/j.bandc.2016.01.009.
DSM-5 American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington: American Psychiatric Publishing.
Dunlop, B. W., & Nemeroff, C. B. (2007). The role of dopamine in the pathophysiology of depression. Archives of General Psychiatry, 64(3), 327–337.
Fabry, R. E. (2018). Spontaneous cognition and epistemic agency in the cognitive niche. Frontiers in Psychology, 9, 1–13. https://doi.org/10.3389/fpsyg.2018.00931.
Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free-energy. Frontiers in Human Neuroscience, 4. https://doi.org/10.3389/fnhum.2010.00215.
Fernandez, A. V. (2014). Depression as existential feeling or de-situatedness? Distinguishing structure from mode in psychopathology. Phenomenology and the Cognitive Sciences, 13(4), 595–612.
Friston, K. (2005). A theory of cortical responses. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1456), 815–836. https://doi.org/10.1098/rstb.2005.1622.
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787.
Friston, K. (2013). Active inference and free energy. Behavioral and Brain Sciences, 36(03), 212–213. https://doi.org/10.1017/S0140525X12002142.
Friston, K., & Frith, C. (2015a). A duet for one. Consciousness and Cognition, 36, 390–405.
Friston, K. J., & Frith, C. D. (2015b). Active inference, communication and hermeneutics. Cortex, 68(Kelso 2012), 129–143. https://doi.org/10.1016/j.cortex.2015.03.025.
Friston, K., Thornton, C., & Clark, A. (2012). Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3, 1–7.
Fuchs, T. (2005). Corporealized and disembodied minds: A phenomenological view of the body in melancholia and schizophrenia. Philosophy, Psychiatry, & Psychology, 12(2), 95–107.
Fuchs, T. (2013). Depression, intercorporeality, and interaffectivity. Journal of Consciousness Studies, 20(7–8), 219–238.
Gerrans, P., & Scherer, K. (2013). Wired for despair: The neurochemistry of emotion and the phenomenology of depression. Journal of Consciousness Studies, 20(7–8), 254–268.
Grandjean, D., & Scherer, K. R. (2008). Unpacking the cognitive architecture of emotion processes. Emotion, 8(3), 341–351.
NIMH. (2017). Major depression. Retrieved from https://www.nimh.nih.gov/health/statistics/major-depression.shtml
Hohwy, J. (2011). Phenomenal variability and introspective reliability. Mind & Language, 26(3), 261–286. https://doi.org/10.1111/j.1468-0017.2011.01418.x.
Hohwy, J. (2013). The predictive mind. Oxford: Oxford University Press.
Hohwy, J. (2015). The neural organ explains the mind. In T. Metzinger & J. M. Windt (Eds.), Open MIND (pp. 1–22). Frankfurt am Main: MIND Group.
Hohwy, J. (2016). Prediction, agency, and body ownership. In A. K. Engel, K. J. Friston, & D. Kragic (Eds.), The pragmatic turn: Toward action-oriented views in cognitive science (pp. 109–120). Cambridge: MIT Pess.
Hornstein, G. A. (2009). Agnes’s jacket: A psychologist’s search for the meanings of madness. New York: Rodale.
Hurley, S. (2008). The shared circuits model (SCM): How control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences, 31(1), 1–22.
Joffily, M., & Coricelli, G. (2013). Emotional valence and the free-energy principle. PLoS Computational Biology, 9(6), 1–14. https://doi.org/10.1371/journal.pcbi.1003094.
Kanai, R., Komura, Y., Shipp, S., & Friston, K. (2015). Cerebral hierarchies: Predictive processing, precision and the pulvinar. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1668), 20140169.
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of time-scales and the brain. PLoS Computational Biology, 4(11). https://doi.org/10.1371/journal.pcbi.1000209.
Kirchhoff, M. D. (2015). Experiential fantasies, prediction, and enactive minds. Journal of Consciousness Studies, 22(3–4), 68–92.
Kirchhoff, M. D. (2017). Predictive processing, perceiving and imagining: Is to perceive to imagine, or something close to it? Philosophical Studies, 1–17.
Kiverstein, J., Miller, M., & Rietveld, E. (2017). The feeling of grip: Novelty, error dynamics, and the predictive brain. Synthese, 196, 1–23. https://doi.org/10.1007/s11229-017-1583-9.
Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., et al. (2017). Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nature Human Behaviour, 1, 1–14. https://doi.org/10.1038/s41562-017-0069.
Klein, C. (2018). What do predictive coders want? Synthese, 195(6), 2541–2557.
Kupferberg, A., Bicks, L., & Hasler, G. (2016). Social functioning in major depressive disorder. Neuroscience & Biobehavioral Reviews, 69, 313–332.
Limanowski, J. (2017). (Dis-)attending to the body: Action and self-experience in the active inference framework. In T. K. Metzinger & W. Wiese (Eds.), Philosophy and predictive processing (pp. 1–13). Frankfurt am Main: MIND Group. https://doi.org/10.15502/9783958573192.
McDowell, J. (1994). The content of perceptual experience. The Philosophical Quarterly, 44(175), 190–205.
Metzinger, T. (2016). Suffering. In K. Almqvist & A. Haag (Eds.), The return of consciousness: A new science on old questions (pp. 237–262). Stockholm: Axel and Margaret Ax:son Johnson Foundation.
Miller, M., & Clark, A. (2018). Happily entangled: Prediction, emotion, and the embodied mind. Synthese, 195(6), 2559–2575. https://doi.org/10.1007/s11229-017-1399-7.
Miller, M., Kiverstein, J., & Rietveld, E. (under review). Mood as tuning prediction: A neurophenomenological perspective on depression.
Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424.
Price, J. L., & Drevets, W. C. (2012). Neural circuits underlying the pathophysiology of mood disorders. Trends in Cognitive Sciences, 16(1), 61–71.
Quattrocki, E., & Friston, K. (2014). Autism, oxytocin and interoception. Neuroscience & Biobehavioral Reviews, 47, 410–430. https://doi.org/10.1016/j.neubiorev.2014.09.012.
Ramstead, M. J. D., Badcock, P. B., & Friston, K. J. (2018). Answering Schrödinger’s question: A free-energy formulation. Physics of Life Reviews, 24, 1–16.
Ratcliffe, M. (2010). Depression, guilt and emotional depth. Inquiry, 53(6), 602–626.
Ratcliffe, M. (2015). Experiences of depression: A study in phenomenology. Oxford: Oxford University Press.
Rottenberg, J. (2005). Mood and emotion in major depression. Current Directions in Psychological Science, 14(3), 167–170.
Rottenberg, J., Gross, J. J., Wilhelm, F. H., Najmi, S., & Gotlib, I. H. (2002). Crying threshold and intensity in major depressive disorder. Journal of Abnormal Psychology, 111(2), 302–312.
Rottenberg, J., Gross, J. J., & Gotlib, I. H. (2005). Emotion context insensitivity in major depressive disorder. Journal of Abnormal Psychology, 114(4), 627–639.
Saarinen, J. A. (2017). A critical examination of existential feeling. Phenomenology and the Cognitive Sciences, 1–12.
Sander, D., Grandjean, D., & Scherer, K. R. (2005). A systems approach to appraisal mechanisms in emotion. Neural Networks, 18(4), 317–352.
Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in Cognitive Sciences, 17(11), 565–573. https://doi.org/10.1016/j.tics.2013.09.007.
Seth, A. K. (2015). The cybernetic Bayesian brain: From interoceptive inference to sensorimotor contingencies. In T. Metzinger & J. M. Windt (Eds.), Open MIND (pp. 1–24). Frankfurt am Main: MIND Group.
Seth, A. K., & Friston, K. J. (2016). Active interoceptive inference and the emotional brain. Philosophical Transactions of the Royal Society B, 371(1708), 1–10. https://doi.org/10.1098/rstb.2016.0007.
Smit, F., Shields, L., & Petrea, I. (2016). Preventing depression in the WHO European Region. WHO Regional Office for Europe: Copenhagen. Retrieved from http://www.euro.who.int/__data/assets/pdf_file/0003/325947/New-Preventing-depression.pdf?ua=1 Accessed date 16–08–2019.
Solomon, A. (2015). The noonday demon: An atlas of depression. New York: Scribner.
Stephan, A. (2012). Emotions, existential feelings, and their regulation. Emotion Review, 4(2), 157–162.
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., et al. (2016). Allostatic self-efficacy: A metacognitive theory of dyshomeostasis-induced fatigue and depression. Frontiers in Human Neuroscience, 10, 1–27.
Styron, W. (2004). Darkness visible. London: Vintage.
van de Cruys, S. (2017). Affective value in the predictive mind. In T. K. Metzinger & W. Wiese (Eds.), Philosophy and predictive processing (pp. 1–21). Frankfurt am Main: MIND Group. https://doi.org/10.15502/9783958573253.
van Doorn, G., Hohwy, J., & Symmons, M. (2014). Can you tickle yourself if you swap bodies with someone else? Consciousness and Cognition, 23, 1–11.
van Doorn, G., Paton, B., Howell, J., & Hohwy, J. (2015). Attenuated self-tickle sensation even under trajectory perturbation. Consciousness and Cognition, 36, 147–153.
Wiese, W. (2017). Action is enabled by systematic misrepresentations. Erkenntnis, 82(6), 1233–1252.
Many thanks to Matteo Colombo, Michael Kirchhoff, Thomas Metzinger, Markus Pantsar, Maxwell Ramstead, Jennifer Windt, and the audience of the conference on Predictive Engines: Andy Clark and Predictive Processing (Macquarie University, December 2017) for their constructive feedback on previous versions of this work. I would also like to thank two reviewers for their helpful feedback on earlier versions of this paper.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Fabry, R.E. Into the dark room: a predictive processing account of major depressive disorder. Phenom Cogn Sci 19, 685–704 (2020). https://doi.org/10.1007/s11097-019-09635-4
- Major depressive disorder
- Predictive processing
- Active inference
- Precision estimation
- Existential feelings