Predictive coding and phenomenological approaches of delusions: convergence and differences – Authors' reply
Authors: Jessica Niamh Harding, Noham Wolpe, Stefan Peter Brugger, Victor Navarro, Christoph Teufel, Paul Charles Fletcher
Journal: The Lancet Psychiatry (2024)

We thank Jasper Feyaerts and colleagues for their thoughtful comments and appreciate this chance to address their concern that our Personal View struggles to accommodate the phenomenon of a dual reality or "double bookkeeping".1Bleuler E Zinkin J Dementia praecox or the group of schizophrenias. International Universities Press, New York1950Google Scholar To begin with a clarification, amortised inference is not a "top-down initial best guess". Rather, amortised inference is fundamentally bottom-up, acting to set posterior inferences at all levels of the hierarchy almost simultaneously.2Tschantz A Millidge B Seth AK Buckley CL Hybrid predictive coding: inferring, fast and slow.PLOS Comput Biol. 2023; 19e1011280Google Scholar In presenting our example ("if 2x=20, what is x?"3Harding JN Wolpe N Brugger SP et al.A new predictive coding model for a more comprehensive account of delusions.Lancet Psychiatry. 2024; 11: 295-302Summary Full Text Full Text PDF Scopus (0) Google Scholar), we sought to illustrate how this process shifts the brain instantaneously into a particular space of reasoning, which accords with the idea of an "ontological transformation". This illustration shows that our approach can account for "double bookkeeping".1Bleuler E Zinkin J Dementia praecox or the group of schizophrenias. International Universities Press, New York1950Google Scholar We suggest that amortised inference could have a suboptimal biasing effect on the overall inferential process in situations of uncertainty and ambiguity. This effect is because, under such conditions, amortisation might be more likely to initialise inference near a local minimum. The iterative process will subsequently trap the agent in this inference. Prediction error minimisation still proceeds, but in such a way that the final inference is determined by a local (rather than a global) minimum. We speculate that occupation of such a minimum would generate a coherent but narrow (perhaps, in the words of Feyaerts and colleagues, "a quasi-solipsistic") slice of reality that could coexist with, even if it contradicts, a broader inference based on an "intersubjective" model of reality. Operating between these two parallel realities—what is supposed to be true and what is experientially true—could result in belief itself becoming a porous concept.4Wang EW The Collected Schizophrenias: Essays. Graywolf Press, Minneapolis, MN2019Google Scholar Seemingly incongruous realities might not be interpreted as such. In this respect, the hybrid predictive coding account2Tschantz A Millidge B Seth AK Buckley CL Hybrid predictive coding: inferring, fast and slow.PLOS Comput Biol. 2023; 19e1011280Google Scholar, 3Harding JN Wolpe N Brugger SP et al.A new predictive coding model for a more comprehensive account of delusions.Lancet Psychiatry. 2024; 11: 295-302Summary Full Text Full Text PDF Scopus (0) Google Scholar does seem to be epistemically just in its recognition of a profoundly different experience of reality, within which reasoning and inference can be more fully understood. PCF has received consulting fees from Ninja Theory and Hooke London. All other authors declare no competing interests. A new predictive coding model for a more comprehensive account of delusionsAttempts to understand psychosis—the experience of profoundly altered perceptions and beliefs—raise questions about how the brain models the world. Standard predictive coding approaches suggest that it does so by minimising mismatches between incoming sensory evidence and predictions. By adjusting predictions, we converge iteratively on a best guess of the nature of the reality. Recent arguments have shown that a modified version of this framework—hybrid predictive coding—provides a better model of how healthy agents make inferences about external reality. Full-Text PDF Predictive coding and phenomenological approaches of delusions: convergence and differencesIn their Personal View, Jessica N Harding and colleagues1 present an updated predictive coding model of delusions in psychotic disorders, such as schizophrenia, which aimed to offer a more comprehensive account of their distinctive phenomenological features. As an explicit attempt to bring the computational and phenomenological levels of analysis closer together in explaining delusions, we applaud the authors' original and constructive proposal. In this Correspondence, we take the opportunity to reflect, from our perspective as phenomenological researchers, on what we consider the strengths and potential limitations of the updated and canonical predictive coding accounts of delusions. Full-Text PDF