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Pathological prediction: a top-down cause of organic disease

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We see what we want to see, what we expect to see, instead of what’s really there. I don’t think we do it on purpose, most of the time.

Lauren Miller.

Abstract

Though predictive processing (PP) approaches to the mind were originally applied to exteroceptive perception, i.e., vision and action, recent work has started to explore the role of interoceptive perception, i.e., emotion and affect (Barrett, 2017; Barrett and Simmons, 2015; Miller and Clark, 2018; Seth, 2013; Van de Cruys, 2017; Wilkinson et al., 2019). This article builds on this work by extending PP beyond emotion to the construction of emotional dispositions. I employ principles from dynamical systems theory and PP to provide a model of how dispositional anger (also known as ‘hostile attribution bias’ or HAB) can develop in response to early experiences of psychosocial stress. The model is then deployed to explain the established link between psychosocial stress in early life and the appearance of certain organic disease phenotypes (such as cardiovascular disease) in later life. This phenomenon can appear mysterious when viewed through the standard biomedical explanatory lens, which has difficulty accounting for the causal influence of subjective perceptions and evaluations of the social and material environment on the development of organic disease processes. The model provided presents such cases as instances of developmental mismatch. They occur when an organism develops an emotional disposition that leads them to make habitually-biased appraisals of what the social environment affords. The model provides a novel explanation of certain organic disease phenotypes with top-down and developmental causes, and demystifies one class of cases involving apparently spooky ‘mind-to-matter’ causation.

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Notes

  1. Whether Hebbian learning and PP are frameworks that can be integrated for explaining learning is an important question, but one that lies beyond the bounds of what I hope to accomplish here. Friston (2010) states that a gradient descent on free energy (changing synaptic connection strength to reduce free energy) is formally identical to Hebbian plasticity. Translated into a PP framework, Hebbian learning states that when presynaptic predictions and postsynaptic prediction errors are highly correlated, connection strength increases, so that predictions are able to suppress prediction errors more efficiently. Despite this formal equivalence, Hebbian learning and PP are distinct inasmuch as the former describes a recapitulation process through which learning occurs, while PP describes a representational or inferential process in which prediction is adjusted based on prediction error (Sumner et al. 2020).

  2. Adopting a dynamical approach to emotion here perhaps raises a broader question about the relationship between DST and PP. DST is frequently linked to approaches (e.g., Hohwy 2016; Ward et al. 2017) which seek to understand cognition primarily in terms of embodied agent and environment dynamics. This is because DST provides an apparatus for describing the unfolding operations of complex systems composed of multiple closely interacting parts, in this way providing a tool for describing the evolving states of a system as it navigates its environment over time. But actually, DST itself is simply a tool to study temporal dynamics, i.e., differential equations are used to describe the ways in which the system can transition from one point on the state space to another (as opposed to the various concepts and theories DST is frequently related to, such as self-organisation), and it is this theory-neutral and narrower characteristion of DST I draw on here. My focus here will include a methodological suggestion about using a dynamical conception of emotion to get clearer about the nature of prediction and precision weighting, but for a broader reflection on the relationship between ecological psychology, embodied dynamics and PP, see Bruineberg and Rietveld (2014).

  3. I say ‘partly’ here because there are other subsystem processes that also partly constitute emotion, such as facial expression and action tendencies. A broader and more detailed account would incorporate these processes, but I present here a deliberately simplified model focused more narrowly on evaluative perception.

  4. This example is from Griffiths (2003).

  5. I am grateful to an anonymous reviewer whose considered feedback helped me to see why a dynamical model may, in and of itself, be insufficient to explain HAB, and for their encouragement to explore how emotion might play a role in creating, updating, and sustaining long-term predictions in greater depth.

  6. This is not to exclude the possibility that lower-level predictions also share this twofold structure. I only emphasise higher-level predictions here because I have been focusing on appraisals that involve the thematic content of blame attribution.

  7. For details of this subcortical processes involved in this communicative process between the nervous system and brain see Miller and Clark (2018).

  8. I am grateful to an anonymous reviewer who assisted in the development of this line of argument.

  9. See Ransom et al. (2020) for further discussion of how the broad hypothesis that affect-biased attention modulates precision weighting modulation could be precissified and further investigated. It is noteworthy that Ransom et al. (2020) discuss different varieties of affect-biased attention, including those involving stimuli that strike the perceiver as salient even when the precision weighting of prediction error is low. Representative examples include habitual attentional orientation towards a fence where a ferocious Doberman was seen only once, or to situational features that resemble a single past traumatic event (e.g., so-called Type 1 trauma, see Terr 1991). I have focused here on a variety of affect-biased attention that arises from repeated events of the same type: ones that generate, over time, a habitual attentional orientation. The apparatus of a generative model continuously updated by ‘biased’ appraisals of the type here described seems well-suited to explaining the phenomenon of HAB (and it is possible the same general schematic might be useful in explaining PTSD arising through so-called Type 2 trauma). Further work would be required, however, to explain how this apparatus might be further developed to incorporate ‘Type 1-like’ cases of habitual attentional orientation. For further discussion of this issue, see Ransom et al. (2020), and for an interesting PP-based proposal applied to both Type 1 and Type 2 trauma in the context of PTSD see Wilkinson et al. (2017).

  10. Miller and Clark (2018) suggest that emotion modulates precision via the activity of the pulvinar complex, but I do not think that this claim need be understood as limiting the essential dynamics involved in this process to the brain. I expand on this point below in footnote 13.

  11. For a discussion of the nature of data available and methods used to identify windows of synchronisation, see Bulteel et al. (2014), Hollenstein and Lanteigne (2014).

  12. These dynamics are explored using EEG and fMRI to measure changes in oscillatory band frequency and event-related potential, see, for instance Smout et al. (2019).

  13. I have here attempted to motivate the role the generative model can play in explaining the formation of emotional dispositions. But this is just one step towards a fuller picture of how the model may be implemented in the brain and nervous system, or perhaps more broadly as embodied and enacted in the whole organism itself as it navigates its environment (e.g., Allen and Friston 2018; Friston 2013). I cannot delve in detail into this issue here, beyond noting that the proposal outlined here seems to be compatible with a ‘radically embodied’ PP (Friston 2013; Allen and Friston 2018). Such an approach sees the generative model of the world embodied in a web of neural connections of varying strengths, and causally coupled to the body, specifically its homeostatic needs and the environmental niche within which it has evolved. On this approach there is a sense in which homeostatic set points (e.g., blood pressure and blood glucose levels) partly constitute the organism’s generative model, even though the neural connections forged through a particular organism’s unique learning history also constitute this same model. This more extensive construal of the generative model is consonant with the suggestion made in Sect. 3 that slower bodily dynamics of arousal can lengthen the time interval during which the feedback loop constituting an emotional episode is operative. If the homeostatic set points relevant to such arousal are conceived as part of the generative model, we have a putative mechanism through which the former might influence the development of real-time emotional episodes, triggering attentional filtering and biased appraisals.

  14. For a discussion of how to distinguish ‘emotional’ versus ‘non-emotional’ periods of operation of a system, see Colombetti (2014).

  15. I am grateful to Paul Griffiths and members of the Theory and Method in Biosciences Lab, Charles Perkins Centre, University of Sydney for useful discussion of this idea.

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Acknowledgements

Thanks are owed to the members of the Theory and Method in Biosciences Lab at the Charles Perkins Centre, University of Sydney, for discussion of the central ideas in this manuscript, including Pierrick Bourrat, Axel Constant, Caitrin Donovan, Wesley Fang, Stefan Gawronski, Paul Griffiths, Kate Lynch, Maureen O’Malley, and Arnaud Pocheville. I am also grateful for insightful and constructive feedback received from anonymous reviewers. This feedback was instrumental in developing the final line of argument appearing herein.

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Walsh, E. Pathological prediction: a top-down cause of organic disease. Synthese 199, 4127–4150 (2021). https://doi.org/10.1007/s11229-020-02972-x

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