RDoC Framework Through the Lens of Predictive Processing: Focusing on Cognitive Systems Domain

Abstract

In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.

Full text

REVIEW
CORRESPONDING AUTHOR:
Anahita Khorrami Banaraki
Institute for Cognitive Science
Studies, Tehran, Iran
khorramiaanahita@gmail.com
KEYWORDS:
Research domain criteria;
Predictive processing; Predictive
coding; Active inference;
computational psychiatry;
cognition; RDOC
TO CITE THIS ARTICLE:
Khorrami Banaraki, A., Toghi, A.,
& Mohammadzadeh, A. (2024).
RDoC Framework Through the
Lens of Predictive Processing:
Focusing on Cognitive Systems
Domain. Computational
Psychiatry, 8(1), pp. 178–201.
DOI: https://doi.org/10.5334/
cpsy.119
RDoC Framework Through
the Lens of Predictive
Processing: Focusing on
Cognitive Systems Domain
ANAHITA KHORRAMI BANARAKI
ARMIN TOGHI
AZAR MOHAMMADZADEH
*Author affiliations can be found in the back matter of this article
ABSTRACT
In response to shortcomings of the current classification system in translating discoveries
from basic science to clinical applications, NIMH offers a new framework for studying
mental health disorders called Research Domain Criteria (RDoC). This framework holds
a multidimensional outlook on psychopathologies focusing on functional domains of
behavior and their implementing neural circuits. In parallel, the Predictive Processing
(PP) framework stands as a leading theory of human brain function, offering a unified
explanation for various types of information processing in the brain. While both
frameworks share an interest in studying psychopathologies based on pathophysiology,
their integration still needs to be explored. Here, we argued in favor of the explanatory
power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating
testable hypotheses about mechanistic interactions between molecular biomarkers and
clinical traits. Together, predictive processing may serve as a foundation for achieving the
goals of the RDoC framework.

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1. INTRODUCTION
Despite recent advances in neuroscience, molecular biology, and cognitive science, much is still
unknown about the brain mechanisms behind psychiatric disorders (Scangos et al., 2023; Willsey
et al., 2018). The current categorization system, including the Diagnostic and Statistical Manual of
Mental Disorders DSM-5-TR (American Psychiatric Association, 2022), and the Mental and Behavioral
Disorders section of the International Classification of Diseases ICD (World Health Organization, 2019),
does not map well into the emerging findings from genetics, system neuroscience, and behavioral
science (Cohen & Öngür, 2023); consequently, there is no such clear path in translating research from
primary studies, like in animal models and humans, to a clear understanding of psychopathologies or
systematic treatments which target the related mechanism (Cuthbert & Insel, 2013).
NIMH’s research domain criteria (RDoC) project, initiated in 2009, intended to use a
multidimensional approach, focusing on translational research on functional domains of behavior
or psychological processes across the range of functioning from normal to abnormal (Cuthbert,
2020). The organization of the RDoC matrix comprised of six functional domains (Negative Valence
Systems, Positive Valence Systems, Cognitive Systems, Social Processes, Arousal and Regulatory
Systems, and Sensorimotor Systems) integrated into different units of analysis embracing genes,
molecules, circuits, physiology, behavior, and self-report with consideration of environmental
and developmental factors (Cuthbert, 2020; S. E. Morris et al., 2022). RDoC aims to enhance the
translation of circuit-level knowledge about psychiatric disorders from basic science to clinical
practice, seeking to identify specific neural targets and adopt a more mechanistic and targeted
approach to treatment development (Scangos et al., 2023). For a comprehensive discussion about
the RDoC framework, see; (Cuthbert, 2022; S. E. Morris et al., 2022).
For this ambitious goal, RDoC needs a mechanistic understanding of the main biological components
involved in psychopathologies, their relations to behavioral changes in mental illnesses, and
the reason behind these changes (Simmons et al., 2020). The emerging field of computational
psychiatry employs mathematical or computational models of brain function to understand and
describe the underlying mechanisms of psychopathologies (K. J. Friston et al., 2014). These models
provide a formal framework for analyzing and characterizing psychopathological processes using
computational and mathematical terms (K. J. Friston et al., 2014).
These computational models are divided into two broad groups: well-defined theory-driven
approaches and exploratory data-driven models (Huys et al., 2016; Simmons et al., 2020). Theory-
driven approaches utilize models that incorporate prior knowledge or explicit hypotheses about
the mechanisms, potentially at various levels of analysis and abstraction (Huys et al., 2016).
Conversely, Machine-learning methods are employed in data-driven approaches to enhance disease
classification, treatment outcome prediction, and treatment selection using high-dimensional
datasets (Huys et al., 2016). These two approaches are highly complementary and promising.
RDoC funding projects prioritize multisystem integration, encouraging scientists to use these
computational models to evaluate and validate RDoC constructs through quantitative analysis of
the relationships between various measurement systems (Cuthbert, 2022). In other words, how
these constructs are segregated, overlapped, or interrelated in terms of their underlying neural
circuits would be assessed by theory/data-driven computational models.
Theory-driven approaches utilize formal models to give us a mechanistic understanding of
brain/behavior relationships that serve as an excellent tool for validating RDoC construct and
integrating units of analysis in a meaningful way (Ferrante et al., 2019). Moreover, the collection
and interpretation of data-driven approaches depend on a theoretical background; reciprocally,
theory-driven models need data to test their plausibility (Ferrante et al., 2019).
In the 2017 NIMH workshop for opportunities and challenges of computational psychiatry,
participants highlighted the importance of working toward a “common language” about the
underlying computational theories of mental constructs (Ferrante et al., 2019). Here we talk in
favor of the predictive processing (PP) framework. PP framework is an umbrella term for different
theory-driven computational models that explain various brain functions in terms of prediction

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and prediction error minimization (e.g., predictive coding and active inference). Although evidence
supporting these models is considered as evidence for a broader idea of PP, they differ in algorithmic
and implementation level (Hodson et al., 2024).
This framework encompasses theoretical models that can stretch from cellular biology to
phenomenology that could bring experts in a variety of scales (e.g., molecular biologists, clinical
neuroscientists) to converge their findings in one unifying concept that finally leads to explaining
psychopathology in terms of pathophysiology (K. Friston, 2023).
Based on this framework, any symptom of psychopathology, at some level, arises from false
inference (K. Friston, 2023). False inference is attributed to the imbalance in message passing
between prediction and prediction error units at different levels of the hierarchy. Crucially, this
imbalance is attributed to aberrant precision weighting of hierarchical prediction errors. This
provides a link between belief updating and pathophysiology; in the sense that precision weighting
is thought to be mediated by neuromodulatory effects. In turn, this speaks to a pernicious
(neuromodulatory) synaptopathy, consistent with a view of psychiatric disorders as functional
dysconnection syndromes (K. J. Friston et al., 2014).
Empirically, precision weighting imbalance is suggested to explain many psychiatric conditions
like autism (Lawson et al., 2017; Van de Cruys et al., 2014), ADHD (Richards et al., 2020), psychosis
(Adams et al., 2013; Powers et al., 2017; Sterzer et al., 2018), PTSD (Homan et al., 2019), anxiety
(Hein et al., 2023; Paulus et al., 2019), personality disorders (Moutoussis et al., 2014), and
depression (Badcock et al., 2017).
Indeed, PP models can establish a clear connection between neural systems and behavior
(Ferrante et al., 2019; K. Friston, 2023; Huys et al., 2016; Shine et al., 2021). This allows scientists to
develop solid theoretical conceptualizations that establish bidirectional links across different units
of analysis, ranging from molecules to circuits and from circuits to behavior (Ferrante et al., 2019).
Here, we suggested that an explanatory power of the PP framework could serve as a groundwork
for the RDoC matrix. Equally, PP benefits more if it characterizes psychopathologies multi-
dimensionally (e.g., autism-schizophrenia continuum (Tarasi et al., 2022)).
In the first section, we foreground empirical evidence of the PP framework that presents a
mesoscale understanding of the normative neurobehavioral functions listed in constructs within
the cognitive system domain of RDoC. This section only focuses on studies involving healthy
human participants, where we bring theoretical explanations of the PP framework and subsequent
empirical evidence supporting those explanations. We restrict our review article on the cognitive
construct of RDoC due to the extensive basic science research conducted on these psychological
processes. Moreover, we only consider studies with human participants, as RDoC emphasized for
validating its construct (NIMH 2024).
After this section, we turned to PP’s explanatory potential in understanding psychopathologies,
especially, to bring a mechanistic understanding of connections between biomarkers and clinical
traits in the psychosis continuum by targeting particular RDoC construct (Perception). Lastly, we
provide a framework to illustrate how these two lines of research can be integrated, guiding future
directions in this field.
2. PREDICTIVE PROCESSING FRAMEWORK IN THE COGNITIVE
SYSTEM DOMAIN OF RDOC
RDoC offers a translational perspective in studying mental health disorders, starting with what we
know about normative neurobehavioral functions (for example, what we know about attention?),
and mental health disorders were studied as disruptions in these functions leading to dysfunction
of varying degrees (S. E. Morris et al., 2022). Meanwhile, there is still a lack of understanding of the
brain function underlies these constructs, mainly due to the complexity of studying brain circuits
underlies a specific type of information processing referred to as mesoscale.

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Predictive processing casts brain function as belief updating in the face of new information to
maximize the evidence for internal or generative world models. This affords a powerful framework
for linking microscale (i.e., cellular and molecular function) and macroscale (i.e., behavioral and
self-reports) (Smith et al., 2021).
This section will briefly examine recent advancements in PP hypotheses and empirical evidence
from basic science related to the constructs within the cognitive system domain, including
perception, attention, working memory, language, and declarative memory.
2.1. PERCEPTION
Perception involves a series of complex processes through which we receive information from our
senses, organize and interpret it, and give it meaning (Pomerantz, 2006). The perception construct
of RDoC is further divided into Visual, Auditory, and Olfactory/Somatosensory/Multimodal
subconstructs.
In terms of PP, perception arises from a bidirectional message passing between hierarchical
cortical levels; ascending prediction error is thought to be represented explicitly by superficial
pyramidal cells, and descending prediction is thought to originate in deep pyramidal cells that
cancel prediction error via targeting inhibitory interneurons that are connected with superficial
pyramidal cells (Bastos et al., 2012; Shipp, 2016). Moreover, prediction errors are modulated
by precision (predicting precision) via modulatory backward connections dealing with context
dependencies (K. Friston, 2018).
Empirical evidence for supporting PP in early visual processing primarily arises from studies
observing early visually evoked responses in the absence of bottom-up input, across both deep
and superficial layers of primary visual cortex (V1) (Aitken et al., 2020; Kok et al., 2016; Muckli et
al., 2015). Ultra-high field fMRI studies showed, prior expectations selectively trigger stimulus-
specific activity in the deep layers of the V1 (Aitken et al., 2020), while unexpected events invoke
responses in superficial layers of V1 (Thomas et al., 2024). These findings support the PP laminar
specification of prediction and prediction error in deep and superficial layers of early visual
processing, respectively.
In higher-order visual processing, PP is mainly supported by expectation suppression paradigms
(Hodson et al., 2024), indicating that expected or predicted stimuli evoke smaller responses. In the
Egner et al. (2010) experiment, participants responded to expected and unexpected face and house
stimuli. The study found that when faces and objects are highly expected, the BOLD activity in the
fusiform face area (FFA) was indistinguishable; however, with lower expectation levels, the FFA’s
response to faces was greater than objects. Given the central role of the FFA in face processing, the
strong response to unexpected faces (rather than objects), can be well explained by the presence
of prediction error units in category-specific visual areas such as the FFA (Egner et al., 2010).
In the auditory sub-construct, support for PP mostly comes from auditory mismatch negativity
(MMN) paradigms (see (Heilbron & Chait, 2018)). These paradigms typically consist of repeated tone
sequences disrupted by an atypical deviant tone. MMN is calculated by subtracting the brain’s response
to the standard tone from its response to the deviant (Heilbron & Chait, 2018). This physiological
component is considered an RDoC element in auditory perception and interpreted as a prediction
error (i.e., the discrepancy between sensory inputs and predictions) within the PP framework.
Dynamic causal modeling (DCM) is a prevalent approach extensively used to test PP in relation
to auditory MMN (Garrido et al., 2009; Heilbron & Chait, 2018). DCM studies of evoked potentials
in different oddball paradigms revealed that frequency, intensity, and duration MMNs are best
explained with bidirectional connectivity changes between primary auditory cortex (A1), inferior
frontal gyrus (IFG), and superior temporal gyrus (STG) (Garrido et al., 2009). Garrido et al. (2008)
tries to compare three competing theories of MMN generation, including adaptation, memory
adjustment, and predictive coding using DCM of evoked potentials. The results show that the
predictive coding model, incorporating elements of both adaptation and model adjustment, best
explained the ERP differences (Garrido et al., 2008). Complementary, evidence for the predictive

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nature of top-down auditory signals is also provided by omission paradigms (Heilbron & Chait,
2018; Hodson et al., 2024), which shows that omitting an expected sound can still evoke brain
responses time-locked to the omitted stimulus (Heilbron & Chait, 2018).
Moreover, mismatch responses at shorter latencies than the traditional MMN indicate potential
involvement of sub-cortical auditory pathways in PP processes (Cacciaglia et al., 2015; Escera,
2023). Cacciaglia et al. (2015) explored this by examining BOLD responses in a passive frequency
oddball paradigm, supporting the role of sub-cortical auditory pathways including the inferior
colliculus (IC) and medial geniculate body (MGB) in statistical inference and regularity encoding.
Brain rhythms are another approach to understanding the PP underlying visual and auditory
perception (Walsh et al., 2020). Much work in human and primate studies supports the cortical
communication between alpha/beta feedback connections from deep cortical layers and gamma
feedforward connections in superficial cortical layers (Fontolan et al., 2014; Mendoza-Halliday et al.,
2024). From the predictive coding perspective, alpha/beta feedback connections carry predictive
information, and prediction errors are related to gamma-band oscillations (Bastos et al., 2012).
This statement has repeatedly been supported in human studies utilizing Electrocorticography
(ECoG) (Dürschmid et al., 2016; Edwards et al., 2005; El Karoui et al., 2015; Sedley et al., 2016), EEG
(Chao et al., 2022; Mohanta et al., 2021), and Magnetoencephalography (MEG) (Arnal et al., 2011).
In the «Olfactory/Somatosensory/Multimodal Perception» sub-construct, PP is supported by
limited but significant evidence. This includes the observed predictive activity in the piriform cortex
(PPC) during olfactory search task (Zelano et al., 2011), as well as predictive feedback mechanisms
observed in both superficial and deep layers of the somatosensory cortex during prediction tasks
(Yu et al., 2019). Additionally, there are emerging concepts regarding the role of PP in multisensory
integration, suggesting its broader applicability across various sensory modalities (Talsma, 2015).
Based on comprehensive review articles that evaluate PP claims in perception in a series of invasive
and non-invasive studies, it seems some of the PP claims like hierarchically organized predictions
underlying perception are well supported (Heilbron & Chait, 2018; Hodson et al., 2024; Walsh et al.,
2020). However, the empirical data supporting the existence of separate prediction and prediction
error units still need to be established (Heilbron & Chait, 2018; Walsh et al., 2020).
2.2. ATTENTION
Attention function serves several purposes, such as maintaining a state of alertness, picking out
relevant information from sensory input, and regulating conflicts (Posner & Rothbart, 2007). The
attention networks consist of dispersed computational nodes located in various brain regions that
often collaborate with networks responsible for sensory perception, memory, and various other
functions (Posner, 2023; Posner & Rothbart, 2023).
Attention in the PP framework is a function for optimizing perception and learning via collecting
contextually informative sensations (Lecaignard et al., 2022; Parr & Friston, 2019). This could
happen through the precision weighting of sensory channels or actions (Lecaignard et al., 2022).
In other words, attending to the features of a stimulus is equivalent to predicting high precision for
related prediction error that increases the influence of that error for updating related perceptual
hypothesis (Walsh et al., 2020), or in the case of active inference; it corresponds to a behaviorally
salient action for reducing uncertainty (Parr & Friston, 2019). However, the physical implementation
of precision modulation is one of the less comprehensively understood aspects of PP (Sprevak &
Smith, 2023). Theoretically, precision modulation is suggested to occur via the neuromodulatory
mechanism of gain control at a synaptic level (Moran et al., 2013), or fast synchronized presynaptic
inputs (Feldman & Friston, 2010).
Recent Meta-analyses of functional connectivity studies based on predictive coding show
remarkable similarities between brain regions involved in prediction and brain networks associated
with top-down control of attention (e.g., dorsal attention network) (Ficco et al., 2021). Meanwhile,
a growing body of evidence supports the dissociable yet intertwined roles of attention and
prediction in cognitive processes (Auksztulewicz & Friston, 2016; Ficco et al., 2021; Hsu et al., 2014;

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Kok et al., 2012), which shows voluntary attention improves the precision of perceptual inference
by up-weighting prediction error signals (Garrido et al., 2018).
Even without voluntary attention, a stable or predictable environment facilitates efficient learning
through an implicit precision-weighted process (Lecaignard et al., 2022; Rowe et al., 2023).
Lecaignard et al. (2022) showed this by using simultaneous EEG-MEG recording while participants
performed the passive auditory oddball task. By manipulating sound predictability and using
trial-by-trial modeling of cortical responses and the DCM of evoked responses, they also found
empirical evidence for the link between precision weighting of prediction errors and self-inhibition
in superficial pyramidal cells. They argued that linking voluntary attention and the passive
predictability process would be a promising way to investigate attentional capture mechanistically.
In sum, the attention function is suggested to emerge from precision modulation via modulating
post-synaptic gain at different levels of the hierarchy, and attention networks are hypothesized to
have a crucial role in estimating the precision of prediction signals (Katsumi et al., 2023).
2.3. WORKING MEMORY
Working memory deals with the selective maintenance and manipulation of information when
we are not exposed to external stimuli (Baddeley, 2011). There are few simulations, and empirical
evidence tries to conceptualize the working memory function and its interaction with other
cognitive processes (e.g., decision-making and attention) under the assumptions of PP.
The frontal lobe plays a vital role in working memory function (Prabhakaran et al., 2000). Alexander
and Brown (2018) propose a simple computational motif for frontal cortex function referred to as
the Hierarchical Error Representation (HER) model. In their model, the error signal in mPFC train
representation of the error signal in dlPFC. Then, this error is learned and maintained in dlPFC for
reducing prediction error in mPFC for subsequent stimulus presentation. The simulation of this
model in a variety of findings, including fMRI, ERP, single-unit, and neuropsychological studies,
shows that this self-organized hierarchical network could learn, maintain, and flexibly change
working memory representation (as a product of learning) for prediction error minimization
(Alexander & Brown, 2018).
Simulation studies based on active inference models conceptualized working memory function
as an accumulation of evidence within temporal hierarchies (Parr & Friston, 2017; Parr et al.,
2020), that involves evaluating future policies or accumulating evidence for different stages
of the world to predict future states and guide decision-making (Parr & Friston, 2017). In this
conceptualization, updating or maintenance of representations in working memory depends on
attentional processes. In that sense, updating working memory involves perceiving sensory data
as precise while maintaining a representation in the presence of distractions requires perceiving
new sensory data noisy (Parr & Friston, 2017). Although the model has been evaluated through
simulated ERP, electrophysiological, and in silico lesion experiments (Parr & Friston, 2017; Parr et
al., 2020), we have not discovered any empirical evidence directly supporting this explanation.
2.4. DECLARATIVE MEMORY
‘Declarative’ or conscious memories refer to memories of facts and events that are consciously
available. This function highly depends on the hippocampus in the brain’s temporal lobe (Hainmueller
& Bartos, 2020). The hippocampus plays a fundamental role in all processing stages of learning
(e.g., memory encoding, consolidation, and retrieval) (Topolnik & Tamboli, 2022) and also has a
remarkable capacity for online prediction of upcoming sensory inputs (Barron et al., 2020). From a
PP perspective, the hippocampus is crucial in learning environmental statistics and exploits them for
generating perceptual predictions (Aitken & Kok, 2022; Katsumi et al., 2023; Pezzulo et al., 2017).
Firstly, memory encoding and retrieval through pattern separation and completion have suggested
relying on prediction error, in which, in the encoding phase, prediction error derives learning to update
our internal model of the world, while in retrieval mode, we have learned the statistical regularities
of the environment; thus, prediction error decrease and predictions may dominate (Aitken & Kok,

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2022; Bein et al., 2020; Henson & Gagnepain, 2010). Bein et al. (2020) showed that when human
participants face novel stimuli, prediction error drives the hippocampus towards an encoding mode
with increasing CA1-entorhinal connectivity and stops a retrieval mode through decreasing CA1-
CA3 connectivity. Recently, Aitken and Kok (2022) conducted an fMRI study to illustrate how the
hippocampus balances encoding and retrieval in a predictive association task. In this study, they
demonstrated that the hippocampus switches from representing prediction error (encoding mode)
during learning to represent prediction when the learning processes are completed.
Secondly, declarative memory function occurs via cortico-hippocampal and cortico-cortical
interactions. Barron et al. (2020) proposed a PP version of neocortical-hippocampal interaction
based on long-range inhibitory pathways. They suggested that the hippocampus projects prediction
via long-range GABAergic neurons to explain away activity in lower-level regions (Barron et al.,
2020). A recent ultra-high field fMRI study supported this idea by showing a negative predictive
representation in CA2/CA3 and deep layers of the parahippocampal cortex while participants
performed an omission task (Warrington et al., 2024). Complementary, a DCM study supports
the role of vmPFC in driving the hippocampal theta during the processing of prediction violation
signals (Garrido et al., 2015). These support the role of hippocampus in explaining away predicted
ascending cortical inputs and neocortical-hippocampal interaction in computing mismatch
responses (e.g., prediction errors).
Furthermore, human fMRI studies based on the mental imagery paradigm also investigate the PP
mechanism within cortico-cortical interactions associated with declarative memory (Chu et al.,
2023; Ortiz-Tudela et al., 2023). These studies support the differentiated mechanism between
memory and motor-based predictions (Chu et al., 2023), and episodic and semantic predictions
(Ortiz-Tudela et al., 2023).
Together, these studies further show how PP could bridge the field of learning and perception
(Aitken & Kok, 2022; K. Friston, 2018), and capture declarative memory function.
2.5. COGNITIVE CONTROL
In order to reach our desired goal, we need to adjust our behavior by using our perception,
knowledge, and goals to bias the selection of actions and thoughts from multiple choices
(Gazzaniga et al., 2019). These processes are called cognitive control or executive function,
essential for our intelligent behavior (Miller, 2000). Converging studies showed that the prefrontal
cortex (PFC) networks including the frontoparietal (FPN) network, the cingulo-opercular network
(CON), the salience network (SN), the default mode network (DMN), and the dorsal and ventral
attention networks (DAN and VAN) are central to these processes (Menon & D’Esposito, 2022;
Miller, 2000); Yet there is a clear need for a unifying framework for interpreting these varieties of
PFC networks supporting cognitive control functions.
Active inference holds a unified view of functional brain architectures and suggests that multiple
behavioral controllers (i.e., pavlovian, habitual, and goal-directed) can be understood as the
successive contextualizing basic sensorimotor mechanisms within hierarchical generative models
(Pezzulo et al., 2015). The achievement of goals and fulfilling drives require suppressing various
types of prediction errors (including interoceptive, proprioceptive, and exteroceptive errors)
in the hierarchical architecture and resolving them through appropriate actions (Pezzulo et al.,
2015). Active Inference considers control as distributed processes across a continuous spectrum
ranging from abstract, forward-looking, and conscious reasoning at the highest levels (e.g., PFC) to
concrete, nearsighted unconscious reasoning at lower levels, extending to the arc reflex (Pezzulo
et al., 2015). In this scenario, cortical nodes in the FPN network, including dorsolateral PFC, induce
top-down biases to lower areas, which permits higher-level goals to bias sensorimotor competition
and to exert cognitive control (Pezzulo et al., 2018). Meanwhile, cortical nodes in the CON and
SN network, including the insula, hypothalamus, the solitary nucleus, and the amygdala, sets
the precision of top-down signals (Pezzulo et al., 2018). Finally, the attentional networks play a
crucial role in managing the equilibrium between higher-level cognitively intricate goals and more

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fundamental goals maintained at various hierarchical levels (Pezzulo et al., 2015). This balance
represents a significant characteristic of cognitive control.
Another PP-based model from Alexander and Brown suggested a unifying model that incorporates
hierarchical predictive coding interaction between FPN and CON networks supporting varieties of
cognitive control functions including goal selection, maintenance, and performance monitoring
(Alexander & Brown, 2011, 2015, 2018, 2019). As discussed in the Working memory section,
the HER model tries to capture the hierarchical function of ACC/mPFC and dlPFC/mPFC with the
hierarchical iterative motif of prediction and prediction error computation (Alexander & Brown,
2015, 2018, 2019). The assumption of hierarchical predictive coding between FPN and CON
networks, recently supported with an offline TMS-fMRI study that showed cTBS over mid-dlPFC
increased both CON and FPN activity down to the hierarchy (Wood et al., 2024).
In sum, the (precision-weighted) prediction error minimization principle can be applied not only
to solve lower-level processes in a hierarchy but also to explain cognitive control functions. This
principle offers an excellent opportunity to investigate how information from lower levels of
the hierarchy contributes to higher-level decision-making processes, such as cognitive control
(See (Verguts, 2017)). However, these explanations are still largely hypothetical and need more
investigations to compare their explanatory power to other descriptive or phenomenological
models in cognitive control, such as reinforcement learning or drift-diffusion models (Sprevak &
Smith, 2023).
2.6. LANGUAGE
Human language involves a multistage computational process that transforms thoughts into
auditory signals and vice versa (Hickok, 2009).
Decades of experimental work show that processing linguistic stimuli is highly context-dependent,
and predictability of upcoming stimulus facilitates language processing while deviating from
expectation increases processing time and costs (Altmann & Kamide, 1999; Balota et al., 1985;
McDonald & Shillcock, 2003; R. K. Morris, 1994). These contextual predictions come from all stages
of linguistic hierarchy including speech sounds, words, and sentences (Ferreira & Chantavarin,
2018; Ferreira & Qiu, 2021; Huettig, 2015; Nieuwland, 2019; Tavano & Scharinger, 2015). In that
sense, PP could well explain language comprehension, and language production (speech) in the
human brain by considering the hierarchical directional message-passing of predictions between
lower-order sensory (i.e., auditory signals), motor (i.e., motor commands) and higher-order
cognitive levels (thoughts) (Tavano & Scharinger, 2015).
The coordinated temporal interplay between the inferior frontal gyrus (IFG), superior temporal
gyrus and sulcus (STS), and angular gyrus (AG) is suggested to play an important role in language
comprehension (Obleser & Kotz, 2010; Schroën et al., 2023). PP mechanism between these core
regions in language comprehension is well supported by multiple studies. For instance, MEG
studies support the top-down predictive mechanism of left IFG (Liu et al., 2020) and bottom-up
prediction error activity in the STG (Gagnepain et al., 2012) during expected versus unexpected
speech processing. A recent online TMS-EEG study reveals their precise Causal temporal interaction
(Schroën et al., 2023), which underscores the top-down influence of the left IFG on the left STG
during the processing of highly predictive verbs within a 150 to 350 ms time window, alongside a
bottom-up activity from the left STG to the IFG within a 300 to 500 ms time frame (Schroën et al.,
2023). Moreover, another TMS study also supports the contribution of top-down predictive activity
from the angular gyrus when bottom-up sensory signals are degraded (Hartwigsen et al., 2015).
Towards a mechanistic understanding of this interplay, Caucheteux et al. (2023) fit deep language
algorithms with long-range predictions to fMRI brain activation of 304 participants while listening
to spoken language. They demonstrated that prediction in language processing organizes
hierarchically in multiple timescales; STG predicts lower-level syntactic representations, while IFG
and angular gyrus predict high-level semantic representations. Together these results align with
the functionally and temporally distinct pathway of prediction and prediction error in the human
language network supporting language comprehension.

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In language production, PP involves predicting the sensory consequences of motor commands
through motor-to-sensory neural projections, which contribute to detecting and correcting
errors in motor control (Okada et al., 2018). Neural evidence supporting the presence of the PP
mechanism in speech-motor control has been demonstrated through experiments contrasting
internally and externally generated speech. Empirical investigations utilizing MEG (Tremblay et
al., 2003) and ECoG (Forseth et al., 2020) reveal that unlike internally generated speech (e.g.,
reading), externally produced speech (e.g., speaking) suppresses activity in auditory sensory
regions, which represent forward predictions. Moreover, overtly articulated speech, as opposed to
imagined speech, enhances response in sensory areas as measured by fMRI, reflecting an increase
in prediction error (Okada et al., 2018).
Furthermore, PP is not just restricted to spoken language; even in a deaf population using sign
language for communication, semantic predictions also exist (Wienholz & Lieberman, 2019). This
shows that there are predictive mechanisms for sign language processing in the visual modality
and further suggests that PP is a modality-independent property in language processing (Radošević
et al., 2022).
2.7. SUMMARY SO FAR
The RDoC project is dedicated to exploring mental health disorders through the lens of neuroscience
advancements. Current constructs relied on our circuit-based understanding of psychological
processes and their relationship to clinical syndromes. Meanwhile, RDoC is an evolving tool and
calls for computational models to validate their constructs (Cuthbert, 2022). To understand the
differences between domains and their relations based on their underlying neural circuity.
Researchers have criticized the RDoC matrix for lacking a clear rationale or systematic foundation,
with no robust path to external validation (Ross & Margolis, 2019). Generally, RDoC suffers from a
holistic view of human cognition (Lange et al., 2021). In that sense, we find the explanatory power
of the PP framework as the best option, to be a groundwork in understanding RDoC constructs
and their overlapping neural circuity. Within this framework, the RDoC matrix could go beyond
clustering constructs with correlational studies. This represents a pathway towards a mechanistic
understanding of psychological processes, or causality, which consolidates various psychological
phenomena under the overarching principle of (precision-weighted) prediction error minimization.
Nevertheless, the explanatory potential of PP requires empirical testing. Although there is
compelling evidence supporting PP in the perception construct and its underlying neural circuitry,
empirical human studies investigating PP in other constructs within the cognitive system domain
are notably scarce.
The first reason for this limited evidence is the lack of methodology in testing PP claims in human
studies (Walsh et al., 2020). Although RDoC emphasizes human studies for validating constructs,
the current methodology used in human studies could not bring a definite answer to the existence
of prediction and prediction error units in different processing hierarchies supporting different
psychological processes (Walsh et al., 2020). That’s why using other non-invasive methods like
optogenetic, calcium imaging, and single-unit recording in non-human studies is suggested,
which directly evaluates the PP hypothesis and makes refinements in neural circuity supporting
prediction error minimization (Keller & Mrsic-Flogel, 2018; Walsh et al., 2020). However, we believe
emerging techniques like ultra-high-field fMRI and non-invasive brain stimulation techniques like
TMS could enhance our understanding of PP in constructs within the cognitive system domain.
Ultra-high-field fMRI could distinguish between bottom-up and top-down cognitive processes,
offering deeper insights into the laminar circuitry of PP underlying various psychological processes
(Haarsma et al., 2022). Moreover, integrating this technique with non-invasive brain stimulation
methods, such as TMS, offers a unique opportunity to causally test PP claims on specific neural
circuits within constructs. This combination could lead to a more nuanced understanding of how
PP mechanisms operate in each brain hierarchy.
Second, while simulation studies based on active inference provide promising formal explanations
for different psychological processes related to decision-making and action selection, empirical

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support for these explanations is limited (see (Hodson et al., 2024)). Indeed, empirical evidence for
active inference has been carried out primarily within the context of computational psychiatry for
describing mechanisms underlying psychopathologies, rather than describing the brain functions
itself (Hodson et al., 2024; Smith et al., 2021).
Together, although PP models present a promising framework for understanding efficient
information processing in the human brain, the current evidence does not fully support their
utility for validating RDoC constructs. We strongly suggest empirical testing of PP explanations
(predictive coding and active inference), and their translation from theoretical algorithms to
concrete biophysical implementations within each construct. Only in that case, validating RDoC
constructs based on underlying neural circuity with the PP framework is possible.
3. MULTI-SYSTEM INTEGRATION WITH PREDICTIVE PROCESSING
Psychiatric disorders encompass complex interactions among genes, molecules, cells, circuits,
physiology, and behaviors. The RDoC project encourages scientists to acquire data in different
units of analysis in each functional domain beyond the categorization boundaries of DSM or
ICD (Cuthbert, 2022). Crucially, the integration between these units of analysis is essential for
a comprehensive understanding of mental disorders, and the application of computational
psychiatry plays a pivotal role in deciphering the complex, dynamic interrelations among these
varied dimensions (Sanislow et al., 2019).
In the previous section, we reviewed empirical studies supporting PP in psychological processes
within the cognitive system domain. Here, we talk in favor of its power in integrating data from
different units of analysis while considering developmental trajectories and environmental factors
in psychopathologies.
What is unique about the PP framework is its power to formulate hypotheses (Pezzulo et al., 2024).
These hypotheses make specific empirical predictions that span different units of analysis, ranging
from gene to behavior, which can be empirically validated. One way toward this validation involves
formalizing these hypotheses into generative models. By fitting these models to measured data,
we can systematically compare them to alternative or competing hypotheses and assess their
explanation (Figure 1).
At the physiological level, PP has empirical predictions about the top-down and bottom-up
dynamics supporting prediction and prediction error under the name of predictive coding (K.
Friston, 2018). Additionally, it elucidates how motor functions (e.g., oculomotor performance)
and higher-level cognitive processes (e.g., planning) manifest as an active form of prediction error
minimization, under the name of active inference (Parr & Friston, 2018; Pezzulo et al., 2018, 2024).
At the circuit level, the exchange of information between top-down and bottom-up pathways can
be understood through oscillatory dynamics within and across brain areas, potentially indicating
temporal predictions (Bastos et al., 2012; Walsh et al., 2020). On the molecular and cellular levels,
synaptic activity and efficacy, modulated by neuromodulators, correspond to inferential processes
that minimize free energy across faster and slower time scales, respectively (K. Friston, 2023; Parr
& Friston, 2018; Pezzulo et al., 2024). This involves precision dynamics, which balance inferential
processes at multiple levels by adjusting the post-synaptic gain of sensory or prediction error units
(K. Friston, 2023). Finally, the PP framework can interpret genetic findings from Genome-Wide
Association Studies (GWAS) and Transcriptome-Wide Association Studies (TWASs), particularly
when we find its associations with prediction error signals like Mismatch Negativity (MMN) (Bhat et
al., 2021; Herzog et al., 2023).
What this broad explanatory power could offer to the RDoC matrix is interpreting data gathered in
different units of analysis under the one unifying principle (K. Friston, 2023): minimizing free energy.
In other words, for each construct, multiple hypotheses could emerge under the PP framework in
different units of analysis, and each of them tested empirically to find the best explanation for the
underlying mechanism (Figure 1).

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Another critical concern of the RDoC matrix is integrating developmental processes and their
interactions with environmental factors (Cuthbert & Insel, 2013; S. E. Morris et al., 2022). In terms
of PP, top-down predictive information could emerge while the agency continually faces persistent
statistical regularities of the natural environment; otherwise, it could be hardwired in the first place
due to phylogenic development (Walsh et al., 2020). Animal studies showed that exposure to
statistical regularities of visual stimuli in the course of the experiment results in the emergence of
predictive activity (Attinger et al., 2017; Berkes et al., 2011; Fiser et al., 2016; Gavornik & Bear, 2014);
aligned with animal studies, neurophysiological studies with human participants also showed a
sustained increase in neural activity when auditory stimuli transit from random to regular sequences
(Auksztulewicz et al., 2017; Barascud et al., 2016; Southwell & Chait, 2018). Interestingly, recent
studies showed that even infants can predict basic contingencies of the environment by employing
statistical learning principles (Köster et al., 2020). In this regard, extracting statistical regularities
while exploring the environment leads to learning and updating our internal model of the world,
and this also includes the infant’s early development and learning (Köster et al., 2020).
Together, the dynamic interaction between developmental trajectories and environmental
influences leads to shaping our internal models and priors across multiple levels (Figure 1).
Designing experiments that target the influence of these variables on construct-based PP processes
could unravel the interaction between these factors and the aberrant encoding of internal models
(or precision) associated with psychopathological symptoms.
4. INTEGRATING RDOC AND PP: A FRAMEWORK FOR
UNDERSTANDING PSYCHOTIC DISORDERS
Numerous studies have employed the PP framework to explain psychopathological symptoms,
yet integrating PP with the RDoC needs a more structured experimental design. This process could
begin with identifying an interpretable physiological component that could explained in terms of
the PP framework (e.g., MMN as a prediction error) that demonstrates variability across a spectrum
of psychiatric conditions (Randeniya et al., 2018) (Figure 2A). This spectrum includes individuals at
high risk of a condition as well as those with established psychiatric disorders (Larsen et al., 2020;
Randeniya et al., 2018).
The PP framework’s explanatory potential can then be applied to explaining the psychopathological
symptoms and generate testable hypotheses about their underlying mechanisms. These
Figure 1 RDoC matric through
the lens of Predictive Processing.
Computational models based
on a predictive processing
framework provide mesoscale
insight into psychopathologies,
generating testable hypotheses
regarding data produced in
different units. Furthermore,
psychopathologies may be
conceptualized as the aberrant
encoding of internal models,
influenced by developmental
and environmental factors.

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hypotheses are formulated as conceptual models restricted to the specific construct of the RDoC
(Figure 2B). To test these hypotheses, one could design an experiment to target the network
underlying corresponding construct (e.g., classic auditory oddball for perception construct) and
then use generative models of brain responses, fit these models to empirical data (such as EEG
and fMRI), estimate the model parameters and infer unobservable biological components within
a target network (K. J. Friston et al., 2003) (Figure 2C).
DCM is a prominent approach for testing PP-based hypotheses. The field of neural mass models
within DCM for electrophysiological signals could offer insights beyond simple feedforward and
feedback connections, and model extrinsic and intrinsic connectivities by representing three to
four population neuronal models in each region (Pereira et al., 2021). This approach facilitates
linking various units of analysis including physiology, circuit, and cellular levels in human studies.
Furthermore, genetic findings could be taken into account when we find their associations with
prediction error signals like Mismatch Negativity (MMN) (Bhat et al., 2021; Herzog et al., 2023).
These hypotheses could be evaluated and refined with respect to empirical data coming from
different units of analysis (Figure 2D). While we primarily discuss human studies, advances in
neuroscience research of PP in animal studies could translate to PP-based explanations and related
computational models (Bastos et al., 2020; Keller & Mrsic-Flogel, 2018; O’Toole et al., 2023).
Several recent studies have embarked on this path, indicating a promising trend for future research
in this field. We now turn our focus to these studies, illustrating the utility of the PP framework in
explaining psychotic symptoms. Following the RDoC guidelines, these studies offer insights into
different units of analysis, providing mechanistic explanations of complex psychiatric phenomena.
Psychotic disorders (e.g., bipolar affective disorder and schizoaffective disorder) and schizophrenia
share genetic risk variants, neurobiological abnormalities, cognitive dysfunctions, and patterns of
symptoms (Cuthbert & Morris, 2021). Various biomarkers for psychosis exist in different units of
analysis, including genetic biomarkers (Allen et al., 2020), neurophysiological biomarkers (Ford et
al., 2020; Wang et al., 2022), brain imaging biomarkers (Lahti & Kraguljac, 2020; Lyall et al., 2020;
Pearlson & Stevens, 2020), and cognitive biomarkers (Hill et al., 2020). One way toward multi-system
integration is by using PP explanatory power to explain the interaction between these biomarkers.
As said, we could start with an interpretable physiological component in terms of the PP framework
that varies across a dimension of psychiatric conditions. In the case of psychosis, we could see the
Figure 2 Integrating units
of analysis with predictive
processing framework
across psychosis continuum.
A. Attenuation of mismatch
negativity (MMN) illustrates
aberrant sensory prediction error
across the psychosis spectrum,
from healthy individuals with
psychosis-like experiences
to those with established
psychotic disorders. B. predictive
processing framework generates
testable hypotheses for specific
RDoC constructs, exemplified
here by the auditory MMN circuit
within the perception construct.
C. Hypothesis are investigated
using predictive processing
paradigms, leveraging
generative models to integrate
data across molecular, cellular,
and physiological levels. In this
context, our example connects
auditory predictive processing
to a spectrum of biomarkers,
each reflecting different units of
analysis from genes to behavior.
D. Predictive processing
explanations are then revised
based on empirical data.

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mismatch negativity deficit across the spectrum, ranging from individuals at high risk for psychosis to
those with established psychotic disorders (Randeniya et al., 2018). This component, which elicits EEG
and MEG signals, is a hallmark of sensory prediction errors (Randeniya et al., 2018). This suggests that
the impairment in sensory prediction error processing, as evidenced by MMN reductions, is a marker
of psychosis vulnerability rather than a symptom unique to schizophrenia (Randeniya et al., 2018).
The next step is targeting a specific construct. There are different kinds of paradigms that produce
MMN responses (Kirihara et al., 2020), that could engage networks related to different constructs
within a cognitive system domain (Lee et al., 2017). For instance, in the classic and roving auditory
oddball paradigm, participants were instructed to either ignore the presentation of auditory stimuli
or engage in a distraction task while listening to repetitive sounds, which aligns with the perception
construct as they target pre-attentive processing of auditory prediction errors (Larsen et al., 2024).
Consequently, PP can provide explanations and generate testable hypotheses, specifically within
the perception construct and auditory modality.
PP hypothesizes that auditory hallucinations come from aberrant precision control of priors (Corlett
et al., 2019). In a study of a large cohort consisting of 116 schizophrenia and schizoaffective
disorder cases, 75 bipolar and major depressive disorder cases, and 248 non-psychotic disorders,
Donaldson et al. (2020) found a negative correlation between auditory hallucinations, as
measured by the Scale for the Assessment of Positive Symptoms (SAPS), and mismatch negativity
(MMN) duration across all case groups compare to never-psychotic individuals. Complementary,
a machine learning study based on the BOLD responses to an auditory oddball task showed that
prediction error could predict the severity of hallucinations in schizophrenia patients (Taylor et
al., 2020). These suggest that, in the context of the psychosis continuum, diminished auditory
prediction error may be linked to increased auditory hallucinations from a physiological, behavioral,
and self-reported standpoint.
The generation of frequency and duration MMN in oddball paradigms is linked to neural populations
in the A1, STG, and IFG (Garrido et al., 2008; Schall et al., 2003). A growing body of evidence
now supports the altered effective connectivity between these regions across the continuum of
psychosis from a non-clinical population with psychotic-like experiences (Dzafic et al., 2020) to
individuals with a genetically high risk of psychosis (Larsen et al., 2018, 2024) to schizophrenia
patients at the end of the continuum (Adams et al., 2022; Dzafic et al., 2021; Larsen et al., 2020).
More importantly, attenuated connectivity between IFG and STG and intrinsic connectivity in the IFG
have been linked to the degree of positive symptoms, including hallucination and delusion (Dzafic et
al., 2020; Larsen et al., 2020). The strength of these connections could also depend on the severity
of psychopathologies; for instance, Dzafic et al. (2021) demonstrated a decrease in STG to IFG
connectivity underpinning auditory prediction errors in individuals with more severe hallucinations.
Another way to explore precision control in PP at the synaptic, cellular, and molecular scales is by
using neural mass models (NMM) (Pereira et al., 2021). By parameterizing intrinsic and extrinsic
connectivities in the canonical microcircuit (CMC) model and fitting these models to time-series
data, we could have a closer look at PP within a target network (K. Friston, 2023). Adams et al.
(2022) investigated synaptic efficacy in the MMN network by studying 107 schizophrenia patients,
57 first-degree relatives, and 108 control subjects across various paradigms, including resting
state EEG, resting state fMRI, MMN paradigm, and 40-Hz auditory steady-state response (ASSR).
They employed parametric empirical bayes in DCM for group-level analysis within and across
paradigms that reveal increased self-inhibition in superficial pyramidal cells as a major difference
in schizophrenia patients (Adams et al., 2022). While a previous study supports the link between
self-inhibition in superficial pyramidal cells and precision weighting (and not prediction error per
se) (Lecaignard et al., 2022), this study could further support the aberrant encoding of precision in
schizophrenia patients.
Aberrant precision control in psychosis is also linked to the hypofunction of cortical N-methyl-D-
aspartate receptors (NMDAR) (Adams et al., 2013). In terms of PP, NMDAR determined synaptic
gain and was suggested as a responsible factor for encoding precision (Adams et al., 2014).
Thus, altered neurotransmitter function and network dynamics due to NMDAR hypofunction

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result in the aberrant encoding of precision, leading to increased prediction error and subsequent
aberrant learning (Adams et al., 2014; Sterzer et al., 2018). Rosch et al. (2019) explored this
idea by analyzing the effects of NMDAR blockade in healthy participants during a roving auditory
oddball paradigm under ketamine. This study suggested that ketamine-induced MMN amplitude
reduction is linked to intrinsic regional connections, specifically disinhibition in the IFG due
to altered interneuron activity. Another computational modeling study further showed the
association between the ketamine effect and decreasing higher-level prediction errors in healthy
human adults (Weber et al., 2020). These studies offer insights into bridging neurophysiological
and biological components, enhancing understanding of ketamine-induced psychotic-like
symptoms (Rosch et al., 2019).
Psychotic symptoms also share genetic liabilities across various diagnostic categories (Calabrò et al.,
2020; Herzog et al., 2023). Bhat et al. (2021) examined the relationship between gene expression
in cortical tissues and MMN peak amplitude in 728 individuals. They found that gene expressions
related to MMN, particularly the FAM89A gene in the frontal region and the ENGASE gene in the
entire brain, negatively correlated with MMN amplitude (Bhat et al., 2021). These genes mainly
decode proteins related to regulating the concentration of neurotransmitters in synaptic clefts
(Bhat et al., 2021). Such investigations into gene expressions associated with auditory prediction
error hold the potential to develop precise genetic models delineating abnormal modulation of
precision, specifically within the perception domain of the RDoC framework, thereby advancing our
understanding of psychosis.
Together, we start with an idea of aberrant precision control in the continuum of psychosis.
Then we provide a few valuable studies that try to understand this aberrant control from gene
to behavior (see Table 1). In psychosis, a negative correlation was observed between auditory
hallucinations and MMN across the entire continuum (Donaldson et al., 2020). DCM studies
have revealed associations between forward and backward connections between IFG and STG,
increased disinhibition (evidenced by enhanced self-inhibition in the superficial pyramidal cells
and subsequent downregulation of interneurons) in IFG, and the presence of positive symptoms
(Adams et al., 2022; Larsen et al., 2020, 2024; Rosch et al., 2019). Furthermore, a transcriptomic
study has uncovered a negative correlation between the expression of two genes in the adult
human cortex and MMN amplitude, indicating a need for further research to elucidate its
connection to the synaptic and cellular dysfunctions observed in psychosis (Bhat et al., 2021).
GENES MOLECULES CELLS CIRCUITS PHYSIOLOGY BEHAVIOR SELF-
REPORTS
PARADIGM
Larsen et al. (2024) NMM MMN Network EEG CAPE, PANSS Auditory oddball
Larsen et al. (2020) NMM MMN Network EEG CAPE, PANSS Stochastic
mismatch
negativity
Dzafic et al. (2021) NMM MMN Network EEG Statistical
learning
CAPE, PANSS Reversal
auditory oddball
Dzafic et al. (2020) NMM MMN Network EEG Statistical
learning
PQ Reversal
auditory oddball
Larsen et al. (2018) NMM MMN Network EEG SIPS Auditory roving
oddball
Rosch et al. (2019) Ketamine CMC MMN microcircuit EEG Auditory roving
oddball
Adams et al. (2022) CMC MMN microcircuit EEG, fMRI APSS Auditory oddball
Bhat et al. (2021) FAM89A
and ENGASE
EEG Auditory oddball
Donaldson et al. (2020) EEG SAPS Auditory oddball
Taylor et al. (2020) fMRI SAPS, SANS Auditory oddball
Weber et al. (2020) Ketamine EEG Statistical
learning
Auditory roving
oddball
Table 1 Recent Studies
Examining Mismatch Negativity
(MMN) as an Index of Auditory
Prediction Error Across Various
Units of Analysis.
Note. Abbreviations: CAPE =
Community Assessment of
Psychic Experiences;
MMN = Mismatch Negativity;
NMM = Neural Mass Models;
CMC = Canonical Microcircuit;
PANSS = Positive and Negative
Syndrome Scale; SAPS = Scale
for Assessment of Positive
Symptoms; SANS = Scale
for Assessment of Negative
Symptoms; APSS = Auditory
Perceptual State Score;
SIPS = Structured Interview for
Prodromal Symptoms,
PQ = Prodromal Questionnaire.

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We believe the claims of RDoC about the potentials of data-driven models (e.g., B-SNIP project),
in providing neurobiological targets for treatment development (Clementz et al., 2016, 2022; S. E.
Morris et al., 2020) are not sufficient as these models could not offer a mechanistic understanding
of molecular and regional connections between biomarkers and clinical characteristics (K. Friston,
2023). The PP framework, at least in some constructs with known underlying neural circuity, could
link these chains of evidence together to find a mechanistic implication for psychotic symptoms,
which could finally lead to reliable targets for treatment development.
5. CONCLUSION
The Research Domain Criteria (RDoC) framework significantly relies on computational models for
two primary objectives: first, to evaluate and validate its constructs by focusing on their underlying
neural circuitry, and second, to integrate different units of analysis by considering developmental
trajectories and environmental influences associated with psychopathologies. However, the RDoC
funding project mostly relies on data-driven approaches and ignores the most influential theory
in cognitive neuroscience.
A multitude of human and non-human studies over the past two decades, employing diverse
spatiotemporal scales such as neuroimaging, EEG, and extra-cellular recording, consistently
demonstrate that the human brain leverages prediction and subsequent prediction error for
efficient information processing. Still, from 120 projects funded by RDoC since its beginning, we
only found one project directly focusing on the PP framework (NIMH 2024). This project reveals
new insights about the predictive coding accounts for psychosis symptoms in broad clinical
and non-clinical groups, including healthy individuals with psychotic-like experience (Corlett et
al., 2023), clinical and non-clinical voice hearers (Gold et al., 2023; Leptourgos et al., 2022), and
trauma-related hallucinations (Lyndon & Corlett, 2020).
In this review, we tried to assess the potential of PP in addressing the two primary purposes. In
the first section of our study, we focus on the first objective of the RDoC, which involves validating
constructs. We aim to accomplish this by demonstrating how PP enhances our understanding of
constructs within the Cognitive Systems domain at the mesoscale level. To support our assertions,
we present empirical evidence from human studies that corroborates the explanatory power of PP.
While PP shows significant explanatory potential within the Cognitive System Domain, empirical
evidence in humans is largely lacking, with some exceptions of certain constructs (e.g., Perception
and Language). Based on this limited evidence, PP could not be effective for validating RDoC
constructs based on their underlying neural circuity. However, we believe future studies utilizing
multi-modal data acquisition methods, such as TMS-fMRI, could test the PP hypothesis in human
studies based on neural circuities underlying each construct and be valuable in validating the RDoC
constructs.
In the second section, we evaluate PP’s potential for integrating different units of analysis. In cases
where PP encompasses both interpretable physiological components (e.g., MMN as a prediction
error) and known underlying circuity (e.g., MMN network), it demonstrates a strong capability for
connecting different units of analysis. We highlight a few valuable studies that follow this approach,
contributing to our understanding of the mechanistic interactions among these units in psychosis.
It is worth noting that PP studies that try to explain psychopathologies heavily rely on the DSM
categorization system (see (K. J. Friston, 2017)). Systematic reviews that evaluate PP studies in
mental health disorders mostly come with mixed results (Angeletos Chrysaitis & Seriès, 2023;
Cannon et al., 2021). In one of these systematic reviews, the authors declared, “These ambiguities
cannot be resolved without a clear framework for the hierarchy of priors in the brain and possibly
its implementation in computational models” (Angeletos Chrysaitis & Seriès, 2023). In that sense,
we believe PP itself needs the RDoC framework to study psychopathologies multi-dimensionally
based on the circuit-based understanding of mental constructs.
In sum, these two lines of research heavily need each other to fill their gaps, toward reaching the
ultimate goal of psychiatry, “precision psychiatry” that both pursue.

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COMPETING INTERESTS
The authors have no competing interests to declare.
AUTHOR CONTRIBUTIONS
Conceptualization, A.K., A.T., A.M.; writing the manuscript, A.T.; review and editing, A.K., A.T., A.M.;
supervision, A.K. All authors have read and agreed to the published version of the manuscript.
AUTHOR AFFILIATIONS
Anahita Khorrami Banaraki orcid.org/0000-0003-3015-8186
Institute for Cognitive Science Studies, Tehran, Iran
Armin Toghi orcid.org/0009-0000-0049-8564
Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
Azar Mohammadzadeh orcid.org/0000-0002-2778-8032
Research Center for Cognitive and Behavioral Studies, Tehran University of Medical Science, Tehran, Iran
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TO CITE THIS ARTICLE:
Khorrami Banaraki, A., Toghi, A.,
& Mohammadzadeh, A. (2024).
RDoC Framework Through the
Lens of Predictive Processing:
Focusing on Cognitive Systems
Domain. Computational
Psychiatry, 5(1), pp. 178–201.
DOI: https://doi.org/10.5334/
cpsy.119
Submitted: 25 April 2024
Accepted: 11 October 2024
Published: 30 October 2024
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