Bayesian Psychiatry and the Social Focus of Delusions

Abstract

A large and growing body of research in computational psychiatry draws on Bayesian modelling to illuminate the dysfunctions and aberrations that underlie psychiatric disorders. After identifying the chief attractions of this research programme, we argue that its typical focus on abstract, domain-general inferential processes is likely to obscure many of the distinctive ways in which the human mind can break down and malfunction. We illustrate this by appeal to psychosis and the social phenomenology of delusions.

Full text

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Draft. Please send comments, criticisms, corrections, suggestions, etc., to dw473@cam.ac.uk
Bayesian Psychiatry and the Social Focus of Delusions
Daniel Williams, Marcella Montagnese
Abstract. A large and growing body of research in computational psychiatry
draws on Bayesian modelling to illuminate the dysfunctions and aberrations that
underlie psychiatric disorders. After identifying the chief attractions of this
research programme, we argue that its typical focus on abstract, domain-general
inferential processes is likely to obscure many of the distinctive ways in which
the human mind can break down and malfunction. We illustrate this by appeal to
psychosis and the social phenomenology of delusions.
Keywords: Bayesian brain; predictive coding; predictive processing; evolutionary
psychology; modularity; computational psychiatry; functional specialization; psychosis;
social theory of delusion; paranoia
1. Introduction
It is common to think that psychiatric disorders are caused by dysfunctions in or disturbances
to the neural mechanisms that underlie human psychology. If so, significant progress in our
understanding of psychiatric disorders demands a model of how the healthy or typical brain
functions.
In recent decades, a large and growing body of research in cognitive science has sought to
model the brain as a statistical inference mechanism, constructing and refining probabilistic
models and hypotheses about the world from the streams of noisy and ambiguous information

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it leaves on our sensory transducers (Doya et al. 2007; Knill and Pouget 2004). For example,
theorists have drawn on Bayesian statistics to illuminate learning and inference across a wide
variety of cognitive domains, including perception, motor control, intuitive theories, and
more (see Doya et al. 2007). A prominent manifestation of this work has been in predictive
coding, an influential theory that models the brain as a hierarchically structured prediction
machine, comparing internally generated predictions of sensory information against the
sensory information generated by the body and environment and striving to minimize the
difference between the two (see Clark 2013; Friston 2005; Hohwy 2013; Rao and Ballard
1999).
Such ideas increasingly provide the framework for understanding healthy brain function that
guides research in computational psychiatry (Friston et al. 2014; Teufel and Fletcher 2016).
Specifically, researchers have sought to model a large range of psychiatric disorders by
appeal to dysfunctions or aberrations in the neural mechanics of statistical inference and
decision-making, including schizophrenia (Adams et al. 2013), autism (Lawson et al. 2014),
Parkinson’s disease (O’Callaghan et al. 2017), anorexia (Gadsby and Hohwy 2019), addiction
(Schwartenbeck et al. 2015), depression (Barrett et al. 2016), and more. As Griffin and
Fletcher (2017, p.265) put it,
“The growing understanding of the brain as an organ of predictive inference has been central
to establishing computational psychiatry as a framework for understanding how alterations in
brain processes can drive the emergence of high-level psychiatric symptoms” (Griffin and
Fletcher 2017, p.265).
Some proponents of this approach are extremely optimistic about its explanatory reach.
Carhart-Harris and Friston (2019, p.334), for example, argue that “most, if not all,
expressions of mental illness can be traced to aberrations in the normal mechanics of
hierarchical predictive coding” (our emphasis).

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We have two principal aims in this article. First, we will identify and clarify some of the core
theoretical attractions of what we call “Bayesian psychiatry” as a research programme.
Second, we will argue that this research programme is often hindered by a focus on content-
neutral, domain-general inferential processes that abstract away from much that is distinctive
about human psychology. Drawing on psychosis and the social nature of clinical delusions to
illustrate, we will argue that this focus likely blinds Bayesian psychiatry to many specific
ways in which the human mind can break down and malfunction.
We structure the article as follows. In Sections 2 and 3 we introduce Bayesian psychiatry (S2)
and outline applications of this research programme to understanding psychosis (S3). In
Section 4 we draw on the distinctive social phenomenology of psychosis to argue that such
applications seem inadequate and in Section 5 we suggest that combining Bayesian modelling
with information about the functional specializations of the human brain might help to
address this problem. We conclude in Section 6 by summarising our conclusions and
highlighting important areas for future research.
2. Bayesian Psychiatry
Computational psychiatry seeks to build computational models of the dysfunctions and
aberrations that underlie psychiatric disorders. It is built on two central ideas: first, psychiatry
should strive to trace psychiatric disorders to dysfunctions in neural mechanisms; second,
neural mechanisms are computational mechanisms, that is, mechanisms that extract and
process information through transformations of and operations over information-encoding
states and structures. Computational modelling of psychiatric disorders brings many
theoretical benefits. For example, it provides an explanatorily illuminating link between
neurobiological and psychological levels of description, it forces theories – and thus the
predictions of theories – to be explicit and mathematically precise, and it grounds psychiatric

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explanation in independently well-established models of brain function in computational and
cognitive neuroscience (see Friston et al. 2014; Teufel and Fletcher 2016).
Consonant with their broader influence in neuroscience, computational psychiatry has been
dominated by neural network models, reinforcement learning models, and Bayesian models,
the latter of which constitute our focus here. Bayes’ theorem is an implication of probability
theory that specifies the optimal procedure for redistributing the probabilities assigned to
hypotheses in light of new information. Specifically, the aim of Bayesian inference is to
calculate the posterior probability of a hypothesis conditional on novel evidence,
p(hypothesis | evidence). Bayes’ theorem states that this is proportional to how well the
hypothesis predicts the evidence, i.e. the likelihood p(evidence | hypothesis), multiplied by
the hypothesis’s probability before encountering the evidence, i.e. the prior p(hypothesis). To
calculate the posterior, this product is then divided by the categorical probability of the
evidence, the marginal likelihood p(evidence), which is typically calculated as a sum (for
discrete states) or integration (for continuous states) over the product of the priors and
likelihoods for all possible hypotheses. Importantly, exact Bayesian inference of this sort is
often infeasible when dealing with large or continuous hypothesis spaces. Thus, statistics and
artificial intelligence have developed various algorithms for approximating Bayesian
inference, the most influential of which are stochastic sampling approximations and
deterministic variational approximations.
The growing importance of Bayesian inference and its approximations as a model of neural
information processing can be traced to two principal factors. First, neuroscientists have
increasingly recognised that inductive inference under profound uncertainty is a fundamental
problem that the brain confronts. Bayesian inference provides the optimal method for solving
this problem. Thus, it is argued that we should expect – perhaps on evolutionary grounds –
that the brain implements some form of this solution. As Mathys et al. (2011, p.1) et al. put it,

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‘Since a Bayesian learner processes information optimally, it should have an evolutionary
advantage over other types of agents, and one might therefore expect the human brain to have
evolved such that it implements an ideal Bayesian learner’.
Second, experimental neuroscientists and cognitive psychologists have uncovered evidence
across a wide variety of domains that human inference is approximately Bayes-optimal (see
Clark 2013; Knill and Pouget 2004).
Both factors have motivated the Bayesian brain hypothesis, the hypothesis that information
processing in the brain – or at least certain parts of the brain – is approximately Bayesian
(Knill and Pouget 2004). However, this hypothesis is silent on how the brain implements
approximate Bayesian inference. One of the most influential theories for addressing this issue
is hierarchical predictive coding.
Strictly speaking, predictive coding is an encoding strategy in which only the unpredicted
elements of a signal are fed forward for further stages of information processing. In
neuroscience, this encoding strategy was advanced as a model of visual processing by Rao
and Ballard (1999), which proposes that cortical networks acquire and update probabilistic
models of the causes of sensory signals through a process in which successive levels of
cortical hierarchies attempt to minimize the error in their predictions of activity registered at
the level below them. In recent work, however, “predictive coding” often refers to an
extension and elaboration of this model of perceptual processing to encompass neural
information processing more generally. Thus, Sterzer et al. (2017) write that,
‘Predictive coding conceives of the brain as a hierarchy whose goal is to maximize the
evidence for its model of the world by comparing prior beliefs with sensory data, and using
the resultant prediction errors (PEs) to update the model’ (our emphasis).

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We will use the term “predictive processing” to refer to this more global theory of brain
function (see, e.g., Clark 2013; Friston 2005; 2010; Hohwy 2013). There are two aspects of
predictive processing that will be important in what follows. The first is a conception of
neural information processing in terms of hierarchical precision-weighted prediction error
minimization. “Prediction error” refers to the divergence between the brain’s predictions of
incoming information and the information itself. “Precision” names the inverse of the
variance of a probability density, and thus the degree of certainty or confidence associated
with it. Precision-weighting therefore adjusts the degree to which predictions are updated in
light of prediction errors as a function of the relative uncertainty associated with prior
expectations and incoming evidence. Consonant with the hierarchical structure of the
neocortex, this process of uncertainty-weighted prediction error minimization is thought to be
iterated up an inferential hierarchy, with each successive level attempting to predict activity
at the level below it.
The second component is the idea that prediction error minimization constitutes the
overarching function, goal, or “imperative” of the brain (Friston 2010; Hohwy 2013). This
radical view is often motivated by or grounded in the broader idea, central to the free energy
principle (Friston 2010), that all self-organizing systems obey an overarching imperative to
minimize surprise or maximize model evidence. On this view, action – typically described as
active inference – is also modelled as a form of prediction error minimization, except that
rather than updating predictions to match incoming sensory information, action involves
intervening on the world to match sensory information to the brain’s expectations, the most
fundamental of which are thought to be installed by evolution (see Hohwy 2013).
These broad ideas about brain function have played a central role in a large and growing body
of research in computational psychiatry (see Friston et al. 2014; Teufel and Fletcher 2016).
We will henceforth use the term “Bayesian psychiatry” to refer to this broad research

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programme. Setting aside the details and potential criticisms of specific theories and
hypotheses within Bayesian psychiatry, we believe that it constitutes a promising framework
for modelling psychopathologies for reasons over and above the more general theoretical
attractions of computational psychiatry.
First, the Bayesian brain and related ideas were not developed to explain psychiatric
disorders, but rather have compelling independent support as models of neural information
processing (see Knill and Pouget 2004). Second, conceptualising the brain as an inferential
organ provides an explanatorily illuminating link between the biological mechanisms that
implement information processing and the intentionality and the role of misrepresentation
essential to many psychiatric disorders (Friston et al. 2014). Third, Bayesian psychiatry
draws attention to the important role of uncertainty and uncertainty management in
psychiatric disorders (Hohwy 2013). Fourth, the emphasis on bi-directional hierarchical
processing within theories such as predictive coding constitutes a promising framework for
understanding the complex and often bi-directional interplay between percepts, beliefs, and
more abstract self-narratives that are central to many psychiatric disorders (Sterzer et al.
2018). Finally, and most importantly, Bayesian psychiatry has undeniably been explanatorily
fecund, generating myriad novel conceptualisations and surprising predictions (Teufel and
Fletcher 2016).
In the next section, we will illustrate these attractions by appeal to influential predictive
coding models of psychosis.
3. Psychosis and Bayesian Psychiatry
Psychosis is a complex and heterogeneous functional disorder that is generally understood as
an impairment in reality testing. The term is used as an umbrella category for a cluster of
symptoms that comprise hallucinations and delusions that can occur across many psychiatric,

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neurodevelopmental, and neurodegenerative disorders. Thus, psychotic symptoms have been
widely researched in affective disorders such as bipolar disease (Shinn et al., 2012) and in
neurodegenerative ones such as Parkinson’s Disease (Fénelon et al., 2010) and dementia with
Lewy bodies (Waters et al., 2014). The clinical manifestation of psychosis is varied and
heterogenous across these nosological categories, as well as within each disorder. Here we
will focus mostly on psychosis in schizophrenia, where much of the research within Bayesian
psychiatry on psychosis has been focused.
3.1. Psychosis in Schizophrenia
Schizophrenia is a mental disorder affecting 0.3 to 0.7% of the population worldwide (DSM
5th ed., 2013). Patients diagnosed with schizophrenia can show a heterogeneity of symptoms,
which are classified as either positive or negative, where positive symptoms include
hallucinations and delusions and negative symptoms include a lack of useful goal-directed
behaviours (Garofalo et al., 2017), anhedonia (i.e. a lack of anticipation and seeking of
rewards), poverty of speech, and asociality (Frith, 2005). Research has shown that the
aetiological roots of schizophrenia span from genetic risk factors (Tsuang, 2000) to social and
environmental ones (Mortensen et al., 1999; see below Section 5), including complex
interactions between them (Ursini et al., 2018). Further, it is important to distinguish changes
in patients’ psychopathology across time. For example, chronic patients with schizophrenia
tend to have fixed delusions, whilst these tend to be less immovable in those at early stages of
the disorder, such as in First-Episode Psychosis, where individuals often retain insight about
the implausibility of delusional thoughts (see Sterzer et al., 2018). Even though the exact
causes of schizophrenia are still not well understood, there is abundant evidence implicating
different neurotransmitters (especially dopamine and glutamate) and multiple brain areas (see
Gill and Grace 2016).

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Hallucinations take different forms in schizophrenia, with heterogeneous manifestations
across different sensory modalities, although auditory hallucinations are the most studied
(Montagnese et al. 2020). These tend to revolve around hearing voices, either individual or in
conversation, which often generate a running commentary on the individual’s behaviour.
Although the specific contents of delusions vary widely across individuals and cultures, they
tend to cluster in a surprisingly small subset of themes, almost all of which concern the
individual’s standing in the social world (Bentall et al. 1991; Gold 2017). Here, we will focus
largely on the most common form of delusions, persecutory delusions, an extreme form of
paranoia which involves the unsubstantiated belief that an agent or group of agents wants to
harm the delusional individual (Freeman 2016).
3.2. Predictive Coding and Psychosis
The most important precursors to predictive coding models of psychosis are those that posit a
dysfunction in the integration of sensory experience, learned expectations, and higher-level
explanations of such experiences (see Sterzer et al. 2018). For example, Maher (1974)
famously proposed that delusions are best understood as reasonable responses to anomalous
experiences caused by dysfunctions in or damages to perceptual mechanisms. Building on
this research and on the aforementioned work implicating dopamine in psychosis, Kapur
(2003) suggested that dopaminergic dysregulation in schizophrenia might disrupt the
attribution of salience to stimuli. According to this influential aberrant salience hypothesis,
seemingly irrelevant events and stimuli elicit excessive attributions of salience and delusions
are understood as the individual’s attempts to make sense of and explain such anomalous
experiences.
Another influential precursor comes from the model of control of intended action developed
by Frith et al. (2000). Here one’s sense of agency can be seen as emerging from the

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integration of different agency cues, including both internal (e.g. from processes serving
motor control) and external cues (e.g. feedback from sensory systems), as well as prior
information, where each kind of information is weighed by its reliability. To feel like the
agent of one’s actions, this model holds that agents must be able to reliably anticipate the
sensory consequences of such actions. A failure in such prediction will thus render one’s own
behaviour surprising, thus suggesting an external cause. By extending this framework to
psychosis more generally (Moore and Fletcher, 2012), positive symptoms can be seen as
emerging from repeated confusion between external and internal origins of sensory data.
Experimental evidence confirms this loss of normal attenuation of sensory feedback for
motor action in patients with psychosis (Shergill et al, 2005; Blakemore et al., 2000).
Such ideas have laid the groundwork for the development of what Sterzer et al. (2017, p.634)
call the “canonical predictive coding account of psychosis.” According to this model, the
emergence of psychosis can be explained in terms of a dysfunction in the interaction between
and integration of top-down expectations and bottom-up information. As noted above,
optimal prediction error minimization necessitates that prediction errors are effectively
weighted by their precision or certainty. The canonical predictive coding accounts posits that
this process of precision-weighting is disrupted in psychosis, such that sensory data is
assigned too much precision relative to higher-level, more abstract expectations, leading to
maladaptive statistical inference and learning and thus the development of inaccurate models
of the world (see Adams et al. 2013; Clark 2016). Further, because of the bi-directional
interaction between perceptual experiences and higher-level beliefs within predictive coding,
inaccurate inferences at lower levels of the inferential hierarchy both influence and are
influenced by maladaptive higher-level expectations, driving both hallucinations and
delusions and a complex interplay between them (see Sterzer et al. 2017). Except when stated

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otherwise, reference to the predictive coding model of psychosis in what follows refers to this
canonical model.
4. The Social Contents of Delusions
The canonical predictive coding model of psychosis has many well-advertised attractions (see
Sterzer et al. 2018). For example, predictive coding comes with an implementational theory
in which precision-weighting is regulated by the action of neuromodulators such as
dopamine, and, as noted, there is substantial independent evidence that dopamine
dysregulation plays a causal role in psychosis. Further, there is compelling neuro-imaging
and behavioural evidence that individuals with psychosis do exhibit deficits in prediction
error-driven learning and probabilistic reasoning. Finally, there are interesting simulations
demonstrating that aberrations in precision-weighting generate effects similar to those
observed in individuals with psychosis, including in psychological domains such as visual
tracking distinct from psychosis itself (see Adams et al. 2013). Nevertheless, this theory also
faces several objections and challenges (see Bell et al. 2019; Williams 2018; and Sterzer et al.
2018 for a review). Here, we focus on just one: namely, how to reconcile the hypothesis that
psychosis results from a domain-general dysfunction of the sort posited by this theory with
the apparent domain specificity of psychosis itself.
To see this problem, first consider how schematic the proposed account of psychosis is.
Summarising this explanation, for example, Clark (2013, p.197) writes that
“understanding the positive symptoms of schizophrenia requires understanding disturbances
in the generation and weighting of prediction error… [M]alfunctions within that complex
economy… yield wave upon wave of persistent and highly weighted “false errors” that then
propagate all the way up the hierarchy forcing, in severe cases… extremely deep revisions in

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our model of the world. The improbable (telepathy, conspiracy, persecution, etc.) then
becomes the least surprising…” (our emphasis).
However, this explanation leaves it opaque why the contents of common delusional themes
such as persecution and conspiracy should constitute the least surprising hypotheses about the
world in light of aberrant precision-weighting. Specifically, although dysfunctions in
precision-weighting and prediction error-driven processing can explain why individuals
process information in abnormal ways and thus form beliefs that appear implausible to those
not suffering from the relevant dysfunction, an adequate explanation of delusions must
explain why individuals form the highly specific delusional beliefs that they come to hold
(Parrott 2019). That is, psychosis demands an explanation of the distinctive way in which
psychotic experience is abnormal out of the vast space of possible ways in which it could
deviate from normal perception and belief but does not.
Focusing specifically on delusions, the predictive coding model conforms to the standard
view in the psychiatric literature that the explanandum should be characterised in a way that
is content-neutral. Thus, the DSM-5 defines clinical delusions as “fixed beliefs that are not
amenable to change in light of conflicting evidence” (American Psychiatric Association
2013, p.87). Setting aside the problem that this definition subsumes many widespread non-
delusional (e.g. religious, ideological, self-serving) beliefs, it characterises delusions in a way
that focuses on their purely formal characteristics, and specifically their irrationality. It
therefore invites the view that delusions result from inferential or reasoning abnormalities
(see Gold 2017). Further, because the definition is content-neutral, it strongly suggests that
such abnormalities afflict domain-general inferential processes ranging over all possible
contents of thought. This is a deep problem, however, because – as highlighted above – the
distribution of delusional beliefs is not a random sample of all possible abnormal beliefs, but

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a highly specific subset, almost all of which concern the individual’s standing in the social
universe (see Bell et al. 2019; Gold and Gold 2015).
Further, it is not clear that delusional subjects do exhibit any significant domain-general
inferential impairments or reasoning abnormalities (see Bell et al. 2019; Gold 2017). At best,
the voluminous body of empirical research attempting to identify such impairments is
inconclusive. Perhaps the most influential proposal in this area – often taken as support for
the predictive coding model of psychosis (Adams et al. 2013) – is that delusional subjects
suffer from a “jumping to conclusions” bias (Garety 1991). In the famous “beads task”, for
example, participants are told that there are two jars, A and B, with jar A containing 85% red
beads and 15% black beads and jar B containing the reverse. On the basis of drawing beads
from a jar, participants are asked to judge which of the jars the beads come from. The core
finding is that individuals with psychosis tend to form a judgement on the basis of fewer
beads than controls (Garety 1991). In addition, recent meta-analyses also indicate small-to-
moderate effect sizes when it comes to other reasoning biases (McLean et al. 2017).
There are problems with this research, however. For example, often the alleged differences
between delusional subjects and healthy individuals disappear when controlling for general
cognitive function, which is known to be reduced in individuals with psychotic symptoms
(Bell et al. 2019). In some meta-analyses, such as McLean et al’s (2017), theorists do not
control for possible confounds of this kind. Further, the domain-general reasoning differences
between delusional subjects and healthy controls are typically small, especially when
compared to the striking deviations from normality observed in psychosis. Thus, the relevant
question is not whether delusional subjects exhibit domain-general differences in inference
relative to neurotypical controls, but whether – and, if so, in what way – such differences are
causally responsible for the formation and entrenchment of delusional beliefs. The relatively
small differences in domain-general inference that have been discovered in the empirical

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literature suggest that such differences might be better understood as effects of other
underlying factors associated with but not responsible for psychosis, or else factors that
function as necessary but not sufficient causes of psychotic experience and delusions.
Importantly, proponents of the predictive coding model of psychosis are aware of at least the
first of these problems. Thus, Griffin and Fletcher (2017, p.272) refer to
“the paradox of why, given that we are positing a very domain-general problem with
weighting information by its reliability in Bayesian inference, delusions tend to be domain
specific in their content, which usually “seem to concern the patient’s place in the social
universe” (Bentall et al. 1991, p.14).”
There are various responses available to proponents of the predictive coding approach. One
strategy is to appeal to the contents of specific experiences. As noted above, an influential
theory dating back to Maher (1974) is that delusions constitute attempts to explain – and thus
derive their contents from – anomalous experiences. This seems highly applicable in many
cases. For example, Capgras delusion has famously been connected to a dysfunction in which
facial recognition is disconnected from interoceptive mechanisms in such a way that
individuals cognitively recognise loved ones but fail to experience any of the typical
autonomic (i.e. affective) cues that accompany such recognition (Langdon and Coltheart
2000). This violation of expectations cries out for explanation, thus generating the thought
that perhaps the “loved one” is really an imposter. Similarly, influential precursors to
predictive coding described above trace the hallucinated voices and illusions of control that
are common in psychosis to dysfunctions in sensory predictive mechanisms that make the
individual’s own voice and actions seem surprising, thus suggesting an external cause.
Nevertheless, although it is extremely likely that anomalous experience plays an important
causal role in delusion formation, there are two problems with locating delusional contents

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wholly in perceptual experiences. The first is that even in cases such as Capgras where one
can identify a specific anomalous experience, there is still the question of why delusional
subjects gravitate towards specific delusional hypotheses. As has been widely noted, for
example, positing an imposter looks like an exceptionally implausible explanation in such
cases (see Parrott 2019). Not only is the belief in tension both with many other beliefs that
people hold in general (i.e. about the limits of disguise) and with the testimony of doctors and
trusted love ones, but there appear to be many other, more plausible explanations of the
relevant experience (e.g. “there’s something wrong with me”).
Second, the canonical predictive coding account of psychosis is supposed to apply in cases
where there are no specific anomalous experiences over and above those generated by
aberrant precision-weighting. One might respond that aberrant precision-weighting provides a
computational-level description of – and can thus draw on the explanatory resources of –
Kapur’s (2003) influential “aberrant salience” model of psychosis described above, according
to which dopaminergic dysfunction (here understood as aberrant precision-weighting) causes
otherwise irrelevant stimuli and connections between stimuli to strike the agent as highly
salient and thus in need of explanation. Once again, however, tracing delusions to a domain-
general aberration in salience attribution predicts that delusional beliefs will range freely over
all possible topics of attention (Gold and Gold 2015). Further, it is unclear why a hyper-
attention to otherwise irrelevant low-level sensory stimuli (driven by highly weighted low-
level sensory prediction errors) does not merely generate an immersion in the sensory world
of the sort observed in autism. Indeed, as has been noted (Sterzer et al. 2017), the dominant
predictive coding account of autism (Lawson et al. 2014) looks highly similar to the
canonical predictive coding account of psychosis, which is a problem given the substantial
dissimilarities in their associated symptoms.

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Another suggestion is that social cognition is likely to be differentially impaired by a domain-
general dysfunction in precision-weighting. For example, Griffin and Fletcher (2017, p.276)
write that
“social cues may be inherently more uncertain than non-social ones, because they rely on
inferring intentions from ambiguous physical acts. Consequently, representations of the social
world could be the first to break down when the system encounters a relatively minor
impairment in uncertainty-weighting inference…”
Even if one accepts that social inference is more difficult than non-social inference, however,
the social focus of delusions is not characterised by a general breakdown in social inference.
For example, persecutory delusions are distinctive not just because they diverge from
ordinary, non-delusional beliefs about the social world, but because of the malign and self-
directed intentions that they attribute to other agents. Why should a paranoid stance towards
the social world result from greater uncertainty or difficulty in social inference? One
suggestion is that “aberrant predictive coding could render other people unreliable, to be
treated with suspicion” (Griffin and Fletcher 2017, p.276; our emphasis). To quote Griffin
and Fletcher (2017, p.276) again,
“Just as reduced discriminability in PE [prediction error] signalling could lead to a consistent
sense of unease or surprise, so too could reduced discriminability between social sources
make everything (and everyone) seem uniformly unreliable, even suspicious.”
Again, however, even granting that aberrant precision-weighting might make other people
seem unreliable – and it is not clear why the substantial divergence between the individual’s
beliefs and other people’s does not make her question her own reliability – unreliability need
not entail suspicion or the attribution of malign intentions. Astrologists are unreliable, but we
do not generally assume that they are part of a hidden plot to do us harm. Further, in the case
of persecutory delusions, people’s unreliability manifests itself primarily in disagreement

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over the veracity of the delusions, suggesting that the paranoia is the cause of the epistemic
estrangement from other people, not the effect of such estrangement.
Finally, in recent work theorists have posited a novel kind of domain-general inferential
difference as a potential driver of paranoia. In a set of fascinating experimental studies, Reed
et al. (2020) demonstrate that paranoid individuals expect greater volatility relative to non-
paranoid controls in a non-social learning task, and they show that this greater expectation of
volatility can be reproduced in rats exposed to methamphetamine, a drug that is known to
increase paranoia in humans. They (Reed et al. 2020) take this as “evidence of fundamental,
domain-general learning differences in paranoid individuals” (p.1), and thus hypothesize
“that aberrations to these domain-general learning mechanisms underlie paranoia” (p.2; our
emphasis).
Granting the existence of such domain-general differences, however, the explanatory
connection between a greater expectation of volatility and the specific focus and contents of
paranoia and persecutory beliefs is opaque. Reed et al. (2020, p.2) write that “since excessive
unexpected uncertainty is a signal of change, it might drive the recategorization of allies as
enemies” (our emphasis). Why should higher levels of expected volatility drive the
recategorization of allies as enemies rather than the reverse, however, or no change in their
status at all? Reed et al. (2020, p.29) suggest that “when humans experience non-social
volatility… they appeal to the influence of powerful enemies, even when those enemies’
influence is not obviously linked to the volatility,” but positing malevolent agency as the
explanation of volatility without sufficient evidence constitutes an implausible – and so
presumably unlikely – explanation of volatility. They also suggest that “with a well-defined
persecutor in mind, a volatile world may be perceived to have less randomly distributed risk”
(Reed et al 2020, p.29). It is not clear how connecting volatility to the seemingly unrelated
actions of agents with hidden and inexplicitly malevolent intentions towards oneself –

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intentions which must be as volatile as the events they cause – is supposed to reduce
uncertainty, however. Further, it is opaque why populating the world with malevolent agency
directed towards oneself should be a desirable psychological outcome even if it did reduce
uncertainty.
Importantly, our point here is not to deny that human beings might be biased towards
suspicion and paranoia of the sort highlighted in these explanations. Our point is that these
biases are independent of any proposed difference in domain-general statistical inference, and
thus illicitly imported in from contingent assumptions about the human mind. These
assumptions might be correct, but they reflect aspects of human psychology that are not
themselves logical consequences of domain-general aberrations in statistical inference.
4.1. Summary
To summarise, the predictive coding account of psychosis is both attractive and problematic.
Although there is compelling evidence that some form of dysfunction in uncertainty
estimation plays a causal role in psychosis, it is difficult to reconcile such a domain-general
explanation with the conspicuous domain specificity of psychotic symptoms, especially when
it comes to delusions. Attempts to avoid this conclusion are either unconvincing or end up
importing contingent assumptions about human psychology external to the model itself and
beyond the scope of content-neutral, domain-general learning differences.
Crucially, this problem seems to stem directly from the emphasis on abstract, domain-general
inferential processes within Bayesian psychiatry more generally. As noted above, this
framework is often aligned with predictive processing, a global theory of brain function in
which the brain is viewed as a general-purpose uncertainty management mechanism
operating in the service of a single, overarching epistemic goal – namely, minimizing (long-
term, average) prediction error or maximizing model evidence (see Friston 2010; Hohwy

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2013). Thus, Adams et al’s (2013, p.10) article outlining the canonical predictive coding
account of psychosis involves “[s]tarting with the assumption that the brain is trying to
maximize the evidence for its model of the world…” (our emphasis). Given this assumption,
it is difficult to see how the account that they develop could locate psychosis in anything but
a content-neutral, domain-general dysfunction in statistical inference. This assumption
abstracts away from almost all of the distinctive functions, motives, interests, and concerns of
the human mind, however. Thus, perhaps by integrating such contingent features of human
psychology back into the framework and its starting assumptions, one might be able to
address the explanatory gap described in this section. We turn to this possibility next.
5. A Bayesian Social Theory of Delusions
In recent years, a prominent social framework for understanding delusions has emerged (see,
e.g. Bell et al. 2019; Gold 2017; Gold and Gold 2015; Raihani and Bell 2019). Although
highly schematic, the unifying idea underlying this approach is that we should understand
delusions not primarily in terms of domain-general inferential impairments but rather in terms
of an evolved social psychology adapted to the recurring features, opportunities, and risks
encountered in human social life. It is easy to see why this framework has been opposed to
the predictive coding account – or, more generally, accounts – of psychosis (see, e.g. Bell et
al. 2019). In this section, we briefly outline this framework and then argue that it can in fact
be reconciled with Bayesian psychiatry once the latter’s focus on abstract, domain-general
inferential processes is replaced with a richer view of human psychology in which statistical
inference mechanisms operate in the context of the distinctive and often idiosyncratic
functions of the human mind.
5.1. The Social Approach to Delusions

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The social approach to delusions is motivated by some of the facts outlined above: for
example, that evidence of significant domain-general inferential differences between
delusional subjects relative to neurotypical controls is weak, and that the actual delusional
themes that occur cluster in a tiny region of the vast space of possible themes, with the
overwhelming majority concerning the social world (Bell et al. 2019; Gold and Gold 2015).
According to proponents of a social approach, these and other explananda suggest that
delusions are better understood in terms of dysfunctions in psychological mechanisms
specialized for the distinctive problems and opportunities of human social life. As Gold and
Gold (2015, p.289) put it, “To understand delusions, one has to understand the history of
human sociality.” Thus, this approach takes its inspiration from an evolutionary framework
for understanding human psychology, according to which the human mind is best understood
not as a general-purpose statistical inference mechanism but as a mosaic of specialised
mechanisms adapted to the distinctive features, opportunities, and risks of human life (see
Del Giudice 2018).
Although human social dynamics exhibit massive variation across place and time, this
variation is underpinned by certain core characteristics. Most fundamentally, human social
life is characterised by a complex interplay between cooperation and competition at multiple
scales, including both within and between groups. Success within such environments is thus
dependent on substantial social support, protection, and interpersonal coordination in the
service of shared goals, but such cooperation is always fragile given the diverse and often
divergent interests of individuals and groups competing for dominance, prestige, and
resources. Further, the difficulties of navigating such opportunities and risks are amplified by
the suite of unique human traits that underpin cooperation and competition, including
sophisticated communication abilities (along with the attendant risk of deliberate deception),

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flexible and reliable mindreading, and highly developed reasoning capacities that facilitate
long-term plans and complex behavioural strategies.
How might such characteristics have selected for a psychological apparatus vulnerable to
delusion? One proposal concerns the evolution of psychological mechanisms concerned with
detecting and responding to social threats (see Gold and Gold 2015). “Social threat” here
names a heterogeneous category of costs imposed by other agents and coalitions of agents,
including those generated by outright violence, exploitation, betrayal, free riding on one’s
investments, and more. Such threats are ubiquitous and have likely constituted the most
significant danger to individual survival and reproductive success throughout our ancestral
past (Dunbar 1998). It is thus highly unlikely that the human mind has evolved to learn about
the costs, cues, and sources of such threats wholly from experience. Such a blank slate would
be quickly outcompeted by agents structured in advance of experience to detect, respond to,
and actively learn about this recurring risk of human social life.
What characteristics would one expect from psychological mechanisms specialised for
navigating social threats? First, one would expect them to err on the side of caution (see Gold
and Gold 2015). That is, given the high – and potentially catastrophic – risk of social threats,
false positives are likely to be less costly than false negatives. Further, this cost asymmetry is
exacerbated by the fact that an absence of evidence of social threat does not imply evidence
of its absence, especially given that the sources of such threats have the capacity and
motivation to deliberately conceal their intentions from us. Thus, once a genuine suspicion of
threat is activated, one would expect this suspicion to be difficult to assuage, and for agents to
downgrade their level of trust in threat-related testimony. In these ways the structural
characteristics of threat detection might have selected for a mild form of paranoia – or at least
hypervigilance – even in properly functioning mechanisms (Raihani and Bell 2019).

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Second, one would also expect the threshold for threat detection – and, by corollary, social
trust – to be calibrated to the characteristics of the social environment that individuals
encounter. That is, just as social threat detection mechanisms should motivate individuals to
learn about and detect specific cues of potential threats, they should also modulate the
threshold for threat detection in response to the more general statistical characteristics of the
environment. There is considerable evidence for conditional adaptation of this kind, which
involves adjustments to the structural development of mechanisms (including information-
processing mechanisms) in response to environmental cues, especially during sensitive
periods such as childhood (see Del Giudice 2018). Thus, early and/or recurrent exposure to
social stressors and exploitation would be expected to lower the threshold for social threat
detection, sometimes in ways that are extremely difficult to change.
Third, mechanisms of social threat detection need to combine both fast and automatic
detection of threats – and attention to the potential cues of threats – posed by the immediate
environmental context with a powerful motivation to ruminate and reflect on the possibility
of more distant threats generated by complex, future-oriented and deliberately concealed
intentions (see Gold and Gold 2015). That is, threat detection is not merely – or even mostly
– a perceptual function but must draw on the resources of reasoning capacities capable of
both integrating information from diverse sources and of exploring complex hypothetical risk
scenarios and possibilities.
Finally, one consideration that has not – to the best of our knowledge, at least – been
explored in the psychiatric literature is the fact that one’s beliefs about social threats provide
important information to other agents. Thus, beliefs about the likelihood of social exploitation
might be influenced by social signalling pressures that adjust one’s level of suspicion not just
to the available evidence but to the deterrent effect of one’s suspicion on others (see Williams
2019). In The Godfather, for example, Don Corleone exemplifies how deterrence can

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motivate a kind of strategic irrationality when he informs fellow mafia bosses of his
willingness to jump to conclusions without evidence:
"… I'm a superstitious man, and if some unlucky accident should befall [my son], if he should
get shot in the head by a police officer, or if he should hang himself in his jail cell, or if he's
struck by a bolt of lightning, then I'm going to blame some of the people in this room.”
Such considerations help to clarify what is meant by “functional specialization.” Gold and
Gold (2015; see also Gold 2017) posit a “suspicion system” for detecting and responding to
social threats, but this terminology carries the unfortunate connotation of a discrete self-
contained cognitive module. As the foregoing suggests, adaptive threat detection requires
mechanisms that integrate information from a variety of different sources, that are capable of
substantial learning, and that modulate the activity of other psychological mechanisms
involved in attention, deliberation, action, and so on. Such information-processing
mechanisms and procedures are thus not self-contained and are certainly not realised in a
discrete anatomical module at the macroscopic level of brain structures. Nevertheless, such
mechanisms are still specialised insofar as their characteristics would not be appropriate for
many other cognitive tasks, such as estimating the spatial layout of the environment,
forecasting the weather, or parsing the syntactic structure of a sentence.
As noted, even properly functioning mechanisms of social threat detection might exhibit
signs of paranoia. Now consider a dysfunction in such mechanisms, however. This
dysfunction could make individuals less capable of detecting and responding to social threats,
and thus extremely vulnerable to exploitation. Equally, however, it could make individuals
overly sensitive to the possibility of social threat, driving their attention towards and
ruminating on the possibility of such threats in a way that will appear wholly disconnected

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from objective evidence to other agents. At first, this hypersensitivity to social threat might
be reined in by conscious reflection on the implausibility of paranoid thoughts. Over time,
however, hyperactive threat detection might result in an accumulation of evidence that
overcomes such rational defences and gives rise to entrenched persecutory beliefs, driving
conscious reasoning away from challenging paranoid thoughts and towards integrating them
with the rest of the individual’s worldview.
This is the essence of the model of persecutory delusions advanced by Gold and Gold (2015;
Gold 2017). As they note, it has myriad attractions. First, it explains why persecutory
delusions have the specific theme that they do. Of course, contingent features about the
relevant individual’s time and cultural milieu will no doubt influence what kinds of social
threats are salient to them, but this model has the advantage of explaining why social threat in
general is such a common theme of delusional ideation. Second, it accounts for the relatively
weak differences in domain-general inference found in the empirical literature. Although this
model is consistent with such differences (see below), it suggests that they are not the sole
driver of delusions. Third, it illuminates powerful correlations found between various forms
of social adversity (e.g. trauma, abuse, exploitation) and the risk of clinical paranoia (see
Raihani and Bell 2019). As noted above, conditional adaptation might have selected for a
lower threshold for threat detection in response to such circumstances. Finally, there is some
direct – albeit fairly limited – evidence that social threat detection is specifically impaired in
conditions such as schizophrenia (see Gold and Gold 2015; Gold 2017).
Of course, as sketched here and as found in Gold and Gold’s proposal, this model is highly
schematic. For example, it may be that dysfunctional mechanisms underlying persecutory
delusions do not track social threat as such but – at least in many cases – specific coalitional
threats, which could account for why severe paranoia often involves misperceptions of
coalitional boundaries and collective action (Raihani and Bell 2017). Further, persecutory

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delusions are obviously not the only kind of delusion. Nevertheless, this model illustrates a
much more general approach to understanding delusions, one that explicitly connects the
contents of delusional ideation and beliefs to the distinctive concerns, motives, and functions
of the human mind, and the psychological mechanisms specialised for such distinctive
characteristics (Bell et al. 2019; Del Giudice 2018; Gold and Gold 2015).
5.2. A Bayesian Social Approach to Delusions
This social approach to understanding delusions appears to conflict with the predictive coding
model – or, more generally, models – of psychosis (see Bell et al 2019). Nevertheless, the
apparent domain specificity of delusions need not be in tension with Bayesian modelling as
such. Indeed, there are various ways in which Bayesian inference generally – and
uncertainty-weighted prediction error minimization specifically – could accommodate
domain specificity. Most obviously, domain-specific mechanisms might themselves make use
of Bayesian computations that infer social threat from ambiguous cues. That is, even if
psychological mechanisms are “function-specific, their algorithms needn’t be” (Carruthers
2006, p.62). As Sperber (2019, p.36) puts it,
“[T]he fact that the formal properties of a learning procedure are best specified without assigning
to it any specific domain or goal does not entail that the use of such a procedure in an organism or
a machine cannot be tied and adjusted to specific goals.”
Such adjustment to specific goals or functions might take various forms. For example, it
might involve domain-specific priors (Sperber 2019). Given the ubiquity and risks of social
threats, it is highly like that humans have priors concerning the presence of such threats both
in general and in specific contexts that need not be acquired wholly from experience. More
subtly, a central issue for Bayesian inference concerns the hypothesis space itself (Parrott
2019). In principle, an infinite number of hypotheses could explain any given observation,

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and a real-life Bayesian inference machine cannot consider all of them. Thus, evolution might
have endowed the human brain with constraints that narrow and structure the hypothesis
space within which Bayesian takes place, including the procedures for generating candidate
hypotheses. For example, people might instinctively consider threat-related hypotheses as
explanations for events, especially those that strike the person as anomalous or distressing.
Further, Bayesian decision theory provides a formal framework for explicitly modelling how
asymmetries in the costs of false positives and false negatives modulate judgement and
decision-making in different domains (see Williams 2020). Finally, all of these features of
Bayesian mechanisms could be adjusted in accordance with conditional adaptation, such that
individuals exposed to early social stressors and exploitation might have higher social threat-
related priors, a greater motivation to generate social threat-related hypotheses, and a lower
threshold for social threat detection.
Given such considerations, there is nothing in the social approach to delusions that is in
tension with the idea that the computational architecture underlying delusions makes use of
Bayesian inference or prediction error minimization. Indeed, one might view these
frameworks as highly complementary, with the social approach proposing distinctive
functions and dysfunctions that underlie delusional cognition at a conceptual level and the
Bayesian approach generating hypotheses about how such phenomena are implemented in the
brain’s information-processing mechanisms.
Return to the canonical predictive coding model of psychosis, for example. At the core of this
model is the idea that psychosis in schizophrenia is driven by aberrant uncertainty-estimation,
with a bias towards assigning greater precision to lower-level sensory prediction errors
relative to higher-level, more abstract expectations. As we have seen, there is compelling
evidence for this proposal, but it struggles to account for the domain specificity of delusional
ideation. Adams et al. (2013, pp.1-2), for example, propose that the failure of precision-

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weighting that they posit can be “understood intuitively by considering classical statistical
inference,” where “if we overestimate the precision of the data…. we expose ourselves to
false positives.” As noted above, however, positing such abstract, domain-general failures in
statistical inference as the cause of psychosis fails to account for the highly specific focus of
delusional ideation and belief. Now consider how such a domain-general difference in
statistical inference might interact with the functionally specialised machinery for social
threat detection outlined in the previous sub-section, however.
First, we have already seen that such machinery is likely biased towards false positives
independent of any aberration in precision-weighting, perhaps especially so in individuals
previously exposed to social stressors. Thus, an additional – and perhaps initially domain-
general – bias towards false positives might have a disproportionate effect on social threat
processing, with threat-related cues coming to seem even more salient and thus capturing the
individual’s attention.
Further, as noted, it is similarly plausible that people will have an inherent bias to generate
and search for hypotheses positing social threats when confronted with anomalous
experiences in general. Further, the tendency to generate such hypotheses is likely to be
amplified given the anxiety known to be associated with paranoia and psychosis (see
Freeman 2007). For example, Pezzulo (2014) has argued that interoceptive cues of anxiety
(e.g. an increased heart rate and galvanic skin response) provide evidence that can bias
Bayesian updating towards paranoid inferences that might seem deeply implausible to those
without the relevant interoceptive evidence, just as the paranoid hypotheses that occur to us
after watching a horror film at night might seem comically implausible to us when we awake
the next morning.
1
1
The role of anxiety in biasing individuals towards paranoid hypotheses is central to Freeman’s (2007) threat
anticipation model of paranoia and persecutory delusions.

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Once the possibility of social threat is seriously entertained as a consequence of one or both
of these factors, the considerations about threat detection described above – for example, the
difficulties in finding evidence of the absence of threat, the risks of wilful deception, and the
potential source of threats in complex and concealed plans – will motivate individuals to
differentially search out, attend to, and ruminate on threat-related information and
possibilities, in addition to being on greater guard against the possibility of wilful deception.
In this way the motivated search for threat-related information and possibilities might interact
with a general oversensitivity to low-level sensory prediction errors to provide additional
evidence that fuels the paranoia.
Whilst at first such paranoid forms of informational-sampling and hypothesis generation
might be reined in by more global, integrative systems of reflection, over time the apparent
accumulation of evidence might overpower such defences, changing the focus of conscious
reasoning away from a reasonable scepticism and towards the development of explanations
that rationalise the evidence of social threat.
2
Thus, this might explain the transition from a
prodromal phase in schizophrenia in which individuals retain insight concerning the
implausibility of their paranoia towards to the entrenchment of more fixed persecutory
beliefs.
Finally, as the estimated risk of social threat and potential exploitation increases, the
motivation for increasing the confidence in one’s paranoid thoughts might be further
incentivised by the deterrent effects of such paranoia on others. Here the willingness to
identify persecutors and conspiracies in a world that has become increasingly distressing
serves a protective function, signalling to others a hypervigilance for potential exploitation.
Although such conspicuous paranoia might serve this protective function well, however, it
2
Note that this might also occur due to more direct damage to those regions of the brain that subserve higher-
level belief integration and evaluation (see Langdon and Coltheart 2000).

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will also further alienate the individual from others and erode social trust, thereby reinforcing
the paranoia and its evidential basis further.
Our repeated use of the word “might” in these suggestions should be emphasised. That is, we
do not intend these extremely schematic and highly speculative suggestions as a serious
model of clinical paranoia and the onset of persecutory delusions. Instead, we have advanced
them to illustrate how augmenting a Bayesian approach to understanding psychosis with the
content-rich, domain-specific biases and concerns of the human mind helps to broaden the
hypothesis space for this approach, thus providing a greater range of potential explanations
when it comes to accounting for some of the distinctive features of psychosis and delusional
ideation.
6. Conclusion
We are convinced of the explanatory power and fecundity of the Bayesian brain and
predictive coding when it comes to modelling the information-processing dysfunctions and
aberrations that underlie psychiatric disorders. Nevertheless, we also believe that the
emphasis within much of Bayesian psychiatry on highly abstract, domain-general inferential
processes likely blinds it to many distinctive features of human psychology and
psychopathology. The human brain is not a general-purpose blank slate employing statistical
algorithms in the service of dispassionate inference, but the control centre of a unique primate
that evolved to navigate a distinct world of opportunities and risks. This control centre might
make extensive use of sophisticated statistical learning and inference, but such strategies
must be understood in the context of the distinctive features, functions, and interests of the
human mind. We have sought to illustrate this lesson by appeal to a highly influential
predictive coding model of psychosis, which – we have argued – is currently unable to
capture the specific contents of delusional ideation precisely because of its exclusive focus on

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aberrations in content-neutral, domain-general statistical inference. As noted, we are aware
how schematic and speculative our proposals have been for integrating this application of
Bayesian psychiatry with a richer, evolutionary framework for understanding human
psychology. Nevertheless, we hope that this article motivates more extensive, detailed
investigations into this subject in the future.
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