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Into the dark room: a predictive processing account of major depressive disorder

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Abstract

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.

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Notes

  1. 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.

  2. 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’.

  3. 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.

  4. 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.

  5. I will pick up on this feature of depressive experience in Section 5.

  6. 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).

  7. 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).

  8. 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.

  9. 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.

  10. 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.

  11. Allostasis is defined as “the process by which the brain efficiently maintains energy regulation in the body” (Kleckner et al. 2017, p. 1).

  12. 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.

  13. 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.

  14. The dysfunction of these sub-cortical structures, as postulated by Badcock et al. (2017) and Barrett and Simmons (2015), is supported by neuroscientific research on the neuronal realisation of affect (for reviews cited by them, see Kupferberg et al. 2016; Price and Drevets 2012).

  15. For constructive criticism on this line of argumentation, see Klein (2018).

  16. 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.

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Acknowledgments

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.

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    Regina E. Fabry

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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

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