Predictive Processing Approach to Schizophrenia Psychosis

The more than a century-long attempt to make sense of the elusive syndrome classically known as schizophrenia entered a new phase in the past two decades with the evolution of the predictive processing theoretical paradigm. A basic tenet in predictive processing is that the brain acts to minimize prediction errors (PE)—the discrepancy at a given time-point between an internal-model-based prediction and the actual sensory input (Clark, 2013; Friston, 2005). By assuming that, predictive processing conforms to the axiomatic Free Energy Principle that states that any living system strives to minimize free energy: namely to minimize uncertainty and surprise (Friston, 2010). Rooted in the Helmholtzian notion of unconscious inference (Von Helmholtz, 1925/1867), predictive processing construes perceptual, cognitive, and motor processes as sequences of multilevel hierarchical inferences wherein predictions derived from tentative models (i.e., priors) are tested against actual sensory inputs. At each level of processing, neural activity is representing a model of "reality" (perception), or of a motor action, at a given level of abstraction (level X). The testing of the models is purportedly implemented by means of a process termed predictive coding (PC) (Rao & Ballard, 1999). The model-derived predictions that concern neural (sensory/proprioceptive) inputs expected from a lower level (X-1) of the system propagate downward in the neural system. Prediction errors (PE)—discrepancies between the top-down-propagated predictions and the actual activity at the lower level (X-1) of the hierarchy—are forwarded to level X. The model at level X accommodates incoming PEs and is updated, which improves its future prediction accuracy. Simultaneously, the resulting activity pattern at level X is confronted with predictions flowing down from a still higher, more abstract level (X+1) of the hierarchy with the ensuing prediction errors propagating upwards to update the models at the (X+1) level, and so on. Consequently, a continuous stream of predictions is being propagated down from higher levels, and prediction errors are being fed forward. At each time point, the winning model at each level is inferred according to Bayesian rules that evaluate models’ relative likelihood, given their baseline probability within the current context and the prediction error data (Badcock et al., 2019).

Precision is a core concept in PC. Technically it has been defined as the inverse variance of the probability distributions and is applied to priors, sensory data, and prediction errors. Conceptually, it represents reliability, or the signal to noise ratio (SNR) of the respective data. In the PC context, precision serves to parameterize the quality of the data involved in predictive processing to determine its optimal weighting in the inference process. It has been proposed to be encoded as the post-synaptic gain of the neural pathways forwarding the predicting errors upwards to the prediction-generating models. The higher the precision of the prediction errors, the stronger is their updating effect on the corresponding models (Feldman & Friston, 2010). Within the PC framework, attention is typically conceived of as reflecting the enhancing of postsynaptic gain of PEs as a positive function of their actual (exogenous attention) or expected (endogenous attention) precision (Feldman & Friston, 2010; Hohwy, 2012).

The PC hypothesis reflects the influence of earlier theoretical proposals that stress the integration of anticipatory top-down inputs and actual bottom-up sensory inputs in perception and motor control (Held, 1961; Hochberg, 1981; MacKay, 1965, 1973; Neisser, 1976; Wolpert et al., 1995). These and related earlier proposals, known jointly as cancellation theories, explained perceptual adaptation to continuous changes in sensory (afferent) inputs that accompany self-action (such as head or eye movements), and motor control and automatization, as outcomes of the brain’s learning-based ability to predict reafference, namely the afferent consequences of issued efferent commands (Sperry, 1950; von Holst & Mittelstaedt, 1950). From the perceptual perspective, reafference is hypothesized to be cancelled out or attenuated, whereas unpredicted afference is assimilated and/or accommodated by the active perceptual model. This has been presumed to keep perceptual representation veridical and unaffected by redundant information (Blakemore et al., 1999; Frith, 1992/2015). From the motor control perspective, prediction of outcomes of self-action has been conjectured to enable controlled monitoring and real-time correction of ongoing movements, as well as the integration of motor elements into automatically executed ballistic routines (Kelso & Stelmach, 1976; MacKay, 1965).

The cancellation framework has served as the foundation for a group of theories, suggesting (Feinberg, 1978; Frith, 1987; Frith et al., 2000; Hemsley, 1998) and providing evidence (Ford et al., 2001; Shergill et al., 2005) that psychotic symptoms reflect the failure of the brain to predict reafference adequately and thus the failure to cancel or attenuate it. This has been said to result in false identification of self-initiated thoughts and actions as being externally generated, which could explain a range of symptoms, such as hallucinations, ideas of reference, passivity phenomena, thought insertion, etc.

A considerable number of papers account for the psychotic symptoms of schizophrenia (especially hallucinations and delusions) in terms of the PCFootnote 1 paradigm. In accord with cancellation theories of psychosis, these papers propose that psychotic symptoms in schizophrenia result from inadequate influence of unpredicted afference (i.e., PE) on the respective prior model. However, while cancellation theories have emphasized the failure to accurately predict the afferent consequences of self-action as leading to unjustified (mis)identification of reafferent inputs as externally generated ones, thus causing psychotic symptomatology, PC oriented accounts have typically interpreted psychotic symptoms as outcomes of imbalanced precision between priors and prediction errors, and thus of inadequate weighting of top-down and bottom-up influences on the inference process (Adams et al., 2013; Brown & Kuperberg, 2015; Corlett et al., 2019; Fletcher & Frith, 2009; Friston et al., 2014; Limongi et al., 2018a; Powers et al., 2017; Schmack et al., 2017; Sterzer et al., 2018). Moreover, in the PC oriented literature, positions and findings have differed regarding the specific nature of the imbalance. While some authors have suggested and offered evidence that psychotic symptoms result from PEs that are over-precise relative to priors, resulting in perceptual experience that is dominated by bottom up inputs whose meaning and behavioral significance are only weakly determined by current context and past experience (Friston et al., 2014; Sterzer et al., 2016), others (Corlett et al., 2019; Powers et al., 2017) have presented evidence for the opposite—an increased precision and therefore increased influence of prior beliefs relative to incoming evidence. In an attempt to reconcile the conflicting proposals and findings it has been proposed that as a compensatory reaction to weak (imprecise) perceptual priors, strong conceptual priors may evolve at higher or parallel levels of the inference hierarchy (Corlett et al., 2019; Sterzer et al., 2018).

In the following sections, we propose the Imbalanced Plasticity Hypothesis (IPH)—a mechanistic account of schizophrenia-related psychosis inspired by cancellation theories and implementing some central tenets of PC. It suggests that it is plasticity imbalance that underlies the proposed precision-encoding impairment in schizophrenia, which can account parsimoniously for an important distinction between manifestations of psychosis in schizophrenia: disorganization (DO) and reality distortion (RD). After discussing the utility of the IPH for understanding several schizophrenia-related psychotic symptoms, a discussion of common ground and differences between the IPH and the PC account will be presented, followed by some predictions derived from IPH. Notably, the importance of neuroplasticity and its modulation was central to a precursor of PC-related accounts of schizophrenia, the disconnection hypothesis (Stephan et al., 2006; Stephan et al., 2009) and has been acknowledged in more recent PC-based accounts (Adams et al., 2013; Friston et al., 2014), however, it has not been incorporated in an integrative theory of the varied manifestations of schizophrenia-related psychosis. In other words, we argue that more elaborate consideration of neuroplasticity is critical to a fuller application of the predictive perspective to the full range of symptomatology in schizophrenia.

IPH Prospective Contribution to Prediction-Oriented Accounts of Psychosis

The PC approach represents an important contribution to and extension of earlier prediction-related theories of psychosis. However, despite the obvious appeal of a theory rooted in a wide-scope bio-philosophical rationale, elaborated and formalized at the algorithmic level and supported at the neurophysiological level, an important issue with PC-inspired theories of psychosis remains to be addressed. PC-related accounts of schizophrenia-related psychosis, such as those cited above, typically focus solely on symptoms such as hallucinations and delusions. However, these accounts have not addressed the distinction between hebephrenic/disorganized (DO) and the paranoid/reality distorting (RD) manifestations of the disorder (Liddle, 1987), the clinical pictures of which differs markedly. The RD syndrome is characterized by suspiciousness/paranoia and persistent systematic delusions and hallucinations. Patients with RD who have low levels of disorganization and negative symptoms typically have relatively preserved cognitive functioning (Bornstein et al., 1990; Hill et al., 2001; Liddle, 1987; Seltzer et al., 1997; Tsuang & Winokur, 1974; Ventura et al., 2010; Zalewski et al., 1998). In contrast, patients considered to be disorganized display primarily multiple forms of fragmentation in goal-directed, emotional, cognitive, and perceptual functioning, such as inappropriate affect, severe distractibility, formal thought disorder, and reduced perceptual organization (Phillips & Silverstein, 2003). They also display hallucinations and delusions, although, unlike in RD/paranoid patients, these are rather fluctuating and unstable (Garety et al., 1988; Pfohl & Winokur, 1982; Winokur et al., 1974). Moreover, a number of the features of disorganized schizophrenia are thought to reflect disruptions in processes involving self-organization within cortical regions (Phillips & Silverstein, 2003), a process that does not require the forms of top-down guidance emphasized by PC (Linsker, 1988; Phillips & Singer, 1997). While it could be argued that the PC account of psychosis was not developed to account for DO main symptoms, and therefore that absence of PC accounts of DO should not be grounds for criticism, we believe that highlighting this issue is important. This is because no theory of psychosis in schizophrenia can be considered complete without accounting for DO, which is both common, and the most heritable (McGrath et al., 2009; Rietkerk et al., 2008) and cognitively and functionally disabling (Eslami et al., 2011; Minor & Lysaker, 2014; Norman et al., 1999; Ventura et al., 2010) syndrome in the condition. Interestingly, theoretical accounts dating back to Kraepelin (Kendler, 2020) and Bleuler (Bleuler, 1911/1950; Peralta & Cuesta, 2011) but continuing into the modern era (Liddle, 2019; Taylor et al., 2010; Williams et al., 2000; Wright & Kydd, 1986) postulate that DO (i.e., fragmentation in associative and other mental functioning) is a core characteristic of schizophrenia, from which RD and negative symptoms may emerge as compensatory phenomena. In these views, DO and RD are related and share an original etiology, while at the same time the presence (or absence) of compensatory mechanisms are associated with important differences in pathophysiology, clinical presentation (symptoms and behaviors), and course of illness.

To address this concern, we suggest a somewhat different perspective that emphasizes the potential role of an imbalance in neural plasticity in the generation of psychosis in schizophrenia. This proposal was inspired both by earlier cancellation theories as well as the PC notion and its precursor, the disconnection hypothesis, which stressed the role of plasticity modulation failure in schizophrenia (Friston et al., 2016; Stephan et al., 2006; Stephan et al., 2009; Zmigrod et al., 2016). While emphasizing the neurophysiological construct of neuroplasticity regulation, the proposed perspective contributes to the discussion of the logic of the inference mechanism, failures in which are thought to cause psychotic symptoms. The proposed perspective acknowledges the central role of prediction in perceptual, motor, and cognitive functioning, as well as the importance of data precision and its adequate encoding in determining the weight of afferent inputs in updating models. However, we suggest that to explain individual differences in the ability to generate model-based predictions and in the precision-sensitive adaptability of these models to information provided by prediction errors, it is important to emphasize the dependence of these processes on neural plasticity. More specifically, we propose an imbalanced plasticity hypothesis (IPH), which posits that RD and DO manifestations of schizophrenia-related psychosis reflect specific forms of aberrant regulation of plasticity in perceptual and motor systems. As we will discuss, this significantly determines: a) the extent of the effects that precision of bottom-up inputs (i.e., PEs) has on the moment-to-moment accommodation of perceptual/cognitive/motor models to these inputs, and b) the rate and stability of long-term neural circuit changes necessary for associative learning and prediction of the afferent consequences of self-activity.

Neural Plasticity and its Role in Prediction Formation and Inference

Neural plasticity is the ability of single neurons, and thus of neural networks, to change their response to incoming inputs, based on previous exposure to particular input configurations and the consequences of those prior exposures (Kolb et al., 2003). One of its main mechanisms is the modification of synaptic strength—change in the efficacy of synaptic transmission between neurons involved in coincident activity (Hebb, 1949/2005). In general terms, plastic changes, as demonstrated experimentally in long-term potentiation (LTP) and long-term depression (LTD), are hypothesized to take place as result of synchronized binding of the glutamate neurotransmitter to both: 1) α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors (AMPARs); and 2) N-methyl-D-aspartate (NMDA) receptors (NMDARs) at the same dendritic spines. This circuit can be thought of as a coincidence detector (i.e., the neural basis of representing contextual relationships) in the sense that the NMDA receptor will only open (via removal of a magnesium block) when the cell is already depolarized as the result of nearly simultaneous input via the AMPA receptor. Calcium (Ca2+) influx that follows NMDAR activation initiates intracellular processes that include AMPAR trafficking to or from the postsynaptic membrane, stabilizing their new positioning and modifications in the size and shape of these spines, and resulting in relatively stable changes in synaptic efficacy (Forsyth & Lewis, 2017; Ho et al., 2011; Lüscher & Malenka, 2012).

Neural experience-dependent plasticity, in its long- (hours to years) and short-term (seconds to minutes) manifestations is widely accepted to underlie a wide range of learning and memory processes that can be described at levels that span from the single neuron to the neural network (Kolb & Gibb, 2014; Mongillo et al., 2008; Power & Schlaggar, 2017). These learning processes are considered a prerequisite for effective perceptual (Tsodyks & Gilbert, 2004), cognitive (Miller, 2000), and motor (Ostry et al., 2010) functioning and for the adaptive development of the organism as a whole (Stiles, 2000). A major feature of long-term synaptic plasticity (LTSP) has to do with its crucial role in enabling the brain to learn the statistical contingencies between different inputs, including between self-activity and its afferent consequences. This lays the foundation for the life-long buildup of experience-based predictions of bottom-up inputs that follow self-activity (Miller & Cohen, 2001).

As noted above, precision is conceptually equivalent to SNR, or in other words to data reliability. As such, it bounds the extent to which data can be correlated and thus limits the learning of the regularities by which data distributions are associated with each other. As long-term neural associative learning underlies the development and maintenance of predictive ability in perceptual, motor, and cognitive systems (Friston, 2005), these processes would be affected by SNR. Moreover, the effect of SNR on learning is moderated by the long-term modifiability of the involved networks (Abraham & Bear, 1996; Abraham & Robins, 2005), in other words, by LTSP.

Short-term synaptic plasticity (STSP) is the neural capability to undergo changes in synaptic efficacy that result from previous activity in the synapse, and lasts for a few minutes at most (Zucker & Regehr, 2002). The short-term memory traces left by earlier inputs have been proposed to tune the state of synaptic connections and thus to play a role in working memory maintenance of information, and to influence the processing of subsequent inputs (Buonomano & Maass, 2009; Masse et al., 2020; Mongillo et al., 2008). In this way, STSP presumably enables passive information storage by continuous adjustment of synaptic efficacies in working memory networks. In the inference process memories thus stored may be reactivated by subsequent inputs, even if the inputs are relatively weak and nonspecific. Such recurrent working memory reactivations will keep reconfiguring the network’s internal synaptic weights, which will in turn affect the way the network responds to subsequent inputs (Stokes et al., 2013). In line with the notion of a synaptic plasticity/stability tradeoff (Abraham & Robins, 2005; Grossberg, 1987), we suggest that the ease in which such changes in synaptic efficacy take place affects not only the sensitivity to changes in afferent inputs, but also the effectiveness of top-down regulation of lower level processing. That is, following top down inputs, the extent and stability of the resulting synaptic efficacy changes in perceptual and motor implementing systems varies as a function of STSP. The higher the plasticity, the more malleable is the synaptic efficacy at any given point in time. This would result in faster, yet relatively unstable top-down priming of motor programs, as well as in perceptual interpretation of subsequent sensory input that is less driven by overlearned conceptual knowledge. Conversely, the lower the plasticity, the more resistant the lower level systems are to real-time top-down modulation of motor and perceptual processes. This would lead to relatively context-insensitive cognitive functioning and motor behavior, as well as to stronger perceptual dependence on rigid cognitive schemata and emotional states.

In this way, the levels of long- and short-term neuroplasticity at a given processing level, along with the SNR of its inputs, modulate the effectiveness of prediction formation as well as of prediction testing and updating (inference). Neuroplasticity is thus a critical factor in predictive operations, and an important determinant of the effects of PE precision on learning and adaptation.

We propose that the impact of PE precision—a characteristic of neural activity determined by internal and external factors—is dependent on the plasticity level of the system processing that input. In low-plasticity systems that are relatively nonresponsive to low SNR data, low-precision inputs are discarded as noise. In PC terms, only precise inputs, and thus precise prediction errors, would be amplified, and given a chance to challenge prior models and update them. In high-plasticity systems, which are relatively sensitive to low-precision data, even low-precision inputs, and thus low precision PEs, are acknowledged as information, amplified, and take part in updating the priors. This perspective leads to a three-factor theoretical model in which the interaction of PE size and SNR (precision) effects on prior model updating is moderated by the prior’s plasticity. As argued below, the three-factor model can account for the DO/RD theoretical distinction as well as a substantial part of the respective symptoms.

Metaplasticity

An important aspect of plasticity is its optimization. Abraham and Bear (1996) pointed to findings indicating that previous synaptic activation may leave an enduring trace that affects the possible degree of subsequent plasticity. They coined the term metaplasticity to refer to the plasticity of plasticity, or the mechanism by which the level of plasticity is regulated in response to experience, as well as to a variety of internal and external factors. In line with Grossberg (1987), Abraham and Robins (2005) stressed that retention of old memories along with acquiring new information “requires a regulated balance between stability and plasticity to solve the trade-off between the stability required to retain information and the plasticity required for new learning within the network…” (p. 74). In other words, while excessive plasticity, secondary to imperfect metaplasticity, may facilitate the acquisition of new information, it will nevertheless make that information less stable in the face of new inputs and random noise. Conversely, insufficiently plastic networks, while steadily retaining already acquired information, will be resistant to new learning.

A number of mechanisms have been proposed to underlie plasticity regulation, some of them, such as synaptic scaling, overlapping with mechanisms of plasticity itself. The metaplasticity term has been largely reserved for the process by which the effects of a previous priming activity persist and affect the extent of synaptic efficacy changes induced by a subsequent activity (Abraham, 2008). An example would be NMDAR activation that, while triggering swift LTP induction, is also priming metaplastic changes that inhibit subsequent induction of LTP in the time range of 60-90 minutes (Huang et al., 1992). This perspective seems to fit well a prominent theory of plasticity regulation that has been put forward by Bienenstock et al. (1982). The theory, coined BCM after its authors, construed regulation of LTP/LTD dynamics as relying on changes in a “sliding” plasticity threshold modified by the recent course of cell firing. While a high level of action potential firing heightens the cell’s threshold for subsequent LTP induction and lowers it for LTD induction, a low level of prior firing has the converse effect. Further elaborations of the theory proposed that intracellular levels of free CA2+, determined by cell firing-dependent NMDAR, determines the extent and direction of plasticity-threshold changes (Shouval et al., 2002), possibly by affecting intracellular AMPAR trafficking and dendritic spine dynamics (Forsyth & Lewis, 2017). Other candidate mechanisms, activated by priming of metaplasticity of a single synapse as well as affecting neighboring synapses, include inhibition of presynaptic γ-aminobutyric acid (GABA) transmission, caused by endocannabinoid release by the postsynaptic cell and the synthesis of plasticity-related proteins (PRPs) mechanisms of plasticity itself (for a review see Abraham, 2008).

An important role in regulation of neuroplasticity has been ascribed to brain-derived neurotropic factor (BDNF)—a neurotrophin released via CA2+-dependent mechanisms that acts at both presynaptic (Balkowiec & Katz, 2002) and postsynaptic sites (Hartmann et al., 2001). There, it induces (presynaptically) release of glutamate and GABA (Matsumoto et al., 2006) and influences (postsynaptically) the activation of glutamatergic NMDA and inhibitory GABA receptors (Rose et al., 2004). The role of BDNF in plasticity regulation, learning, and memory, purportedly via its effects on maintaining excitatory-inhibitory balance (Hensch & Fagiolini, 2005) has been well supported (Turrigiano, 2007) with excessive and insufficient levels suggested to disrupt excitatory and inhibitory neurotransmission, respectively (see Cunha et al., 2010 for a review).

Causes and Consequences of Plasticity Imbalance in Schizophrenia

Much evidence supporting the notion of plasticity dysfunction in schizophrenia has been reported from research paradigms, such as Direct Current Stimulation (Hasan et al., 2011; Mondino et al., 2015), Transcranial Magnetic Stimulation (Mehta et al., 2019 for a meta-analysis), High-Frequency Stimulation (Çavuş et al., 2012; Mears & Spencer, 2012), as well as from neurophysiological/neuropharmacological studies assessing NMDAR functioning (see below). The number and complexity of the cellular and molecular processes involved in plasticity and its regulation suggest several potential mechanisms for its impairment in schizophrenia. Because a comprehensive discussion of these options is beyond the scope of this article, we point out only the most commonly accepted accounts of this failure. Dysfunction of synaptic plasticity in schizophrenia has been typically attributed to aberrant glutamate binding due to dysregulation of NMDA receptors by various postsynaptic and presynaptic processes (Javitt, 2012; Moghaddam, 2003). The role of neuromodulatory transmitters, such as dopamine, acetylcholine, and serotonin, has been stressed by Stephan et al. (2009), with different symptom patterns being related to dysfunction of different neuromodulators at different sites. A considerable number of findings support the notion of a central role of NMDAR impairment in schizophrenia-related psychosis: Blockade of NMDARs by ketamine and phencyclidine has been reported to result in a wide range of psychotic symptoms (Allen & Young, 1978; Javitt & Zukin, 1991; Steeds et al., 2015), as well as laboratory findings similar to those found in schizophrenia patients (Kreitschmann-Andermahr et al., 2001; Umbricht et al., 2000), which included excessive network flexibility suggestive of excitatory/inhibitory imbalance (Braun et al., 2016). BDNF level has been reported to be reduced in patients with unmedicated first-episode psychosis (Green et al., 2011; Yang et al., 2019). Its low levels have been associated with cognitive impairments in chronic schizophrenia—in first-episode patients and in individuals considered to be at high risk for developing a psychotic disorder (Heitz et al., 2019; Man et al., 2018; Yang et al., 2019). Considering the role of BDNF in glutamatergic and GABAergic-dependent excitation/inhibition balancing noted above (Cunha et al., 2010), this could be an additional factor related to this imbalance in schizophrenia patients (Uhlhaas, 2013).

Genetic and environmental factors have been proposed to play a role in NMDAR dysfunction in schizophrenia. Postmortem findings indicate that both common and rare genetic variants seem to be disproportionally involved in the regulation of NMDAR-related neural plasticity and stability in schizophrenia patients (Forsyth & Lewis, 2017; Fromer et al., 2014; Purcell et al., 2014). Stress, social withdrawal, and isolation—the relationship of which to schizophrenia has been well documented (Howes et al., 2017)—have been reported to modify neuroplasticity in the hippocampus (Abraham, 2008; Gan et al., 2014; Garcia et al., 1997; Kim et al., 1996; Schmidt et al., 2013). Taken together, this picture is consistent with the vulnerability/stress perspective widely adopted in psychopathology theorizing (Ingram & Luxton, 2005).

As discussed above, LTSP-based, long-term associative learning of the statistical contingencies between self-produced actions and their afferent consequences underlies the capability to predict the afferent consequences of self-actions. With dysfunctional metaplasticity, networks may become transiently or chronically hyper-/hypo-plastic (Guterman, 2007; Keshavan et al., 2015). In both cases neural learning of efference-reafference regularities is impeded, leading to a deficient ability to generate valid model-based predictions of the afferent consequences of self-activity, with detrimental results in motor and mental domains. In DO patients, we hypothesize that long-term hyperplasticity results in oversensitivity to random signal fluctuations (noise) and therefore to learning of spurious regularities that will lead to the generation of extremely inconsistent predictions. On the other hand, short-term hyperplasticity leads to extremely high sensitivity to low SNR prediction errors.Footnote 2 As a result, the inference process will be disrupted, because models/priors will be malleable by haphazard and noisy prediction errors that often would be based on inadequate predictions due to aberrant hyperplastic LTSP. This will lead to confused and disorganized perception, speech, and behavior that appear to be frequently “out of context.” Conversely in RD patients, we hypothesize that long-term hypoplasticity results in a system that is extremely insensitive to low SNR data and excessively guided by learning history (i.e., by priors, in the Bayesian sense). This will lead to the generation of highly consistent, yet poorly updated predictions. In RD patients, with reduced STSP, sensitivity to bottom-up inputs is limited to those with the highest SNR, resulting in scarceness of effective prediction errors. Prior models themselves thus will remain relatively intact and resistant to updating, laying the groundwork for an inflexible belief system and to the generation of perceptual representations that are excessively driven by that system.

In addition, because STSP is purported to affect the effectiveness of top-down biasing of lower level processing (Ahveninen et al., 2011; Anwar et al., 2017; Jääskeläinen & Ahveninen, 2014; Stokes et al., 2013), metaplasticity dysfunction would upset—in both RD and DO patients—the balance between automatic implementation of overlearned responding and its transient restriction prompted by changing contexts. Adaptively flexible context-bounded cognitive control would thus be difficult to achieve as is suggested in works focusing on executive dysfunction in schizophrenia (Cohen & Servan-Schreiber, 1992; Lesh et al., 2013; Minzenberg et al., 2002). Hypoplasticity would bring about context-resistant highly prioritized prepotent responding to proximal stimulation. Due to its high functional “inertia,” a brain in this state will be relatively sensitive to patterns of sensory input that can be easily assimilated by existing models without requiring their modification but relatively unresponsive to sensory input incompatible with them. This may explain paranoid oversensitivity to certain stimuli along with resistance to others, consistent with the rigid epistemic and behavioral style of RD patients. In contrast, hyperplasticity, would lead to enhanced sensitivity and susceptibility to emergent spontaneous activity with models being readily modifiable. This would result in the dismantling and deprioritization of responses, thus accounting for the deficient capability to preserve proactively external context-guided coherent behavior observed in DO patients (Guterman, 2007). In summary, the concept of metaplasticity dysfunction, along with the different effects that emerge from its hyper- and hypo-plastic expressions, may help to account for the both the commonalities and the differences between the DO and RD manifestations of psychosis in schizophrenia.

Common Ground and Differences Between IPH and the Predictive Coding Perspectives

The plasticity imbalance hypothesis shares two important components with the PC account of psychosis. First, it subscribes to the crucial role of both LTSP and STSP in the learning and inference processes. This role is presumed in PC (Friston, 2005), but it has not been integrated in accounts of schizophrenia-related psychotic symptomatology (Adams et al., 2013; Friston et al., 2016). More specifically, IPH suggests that the basic capability to generate effective predictions based on a current perceptual/motor model is compromised in psychosis due to aberrant long-term associative learning of efferent-reafferent regularities. Thus, while embracing the position of cancellation theories that emphasize the role of prediction failure in explaining psychotic symptoms, IPH offers an account of that failure that although not being a focus of PC-related theories, is not inconsistent with them. It also offers a mechanism that suggests how STSP imbalance impedes the (passive and active) inference processes that shape the dynamics of perception and motor action in schizophrenia.

Second, IPH acknowledges the critical role of PE (im)precision (SNR) level in determining the (in)effectiveness of model updating (Adams et al., 2013; Corlett et al., 2019; Friston et al., 2016). However, it goes one step further by spelling out the mechanism that may underlie precision anomalies in priors and PEs. As in PC, in IPH precision is considered an (inversed) index of the noisiness of priors- and PEs-related neural activity. Yet, in IPH it is the plasticity of the underlying networks that largely controls the extent to which input noise will alter the noisiness (i.e. imprecision) of the prior and PE, and therefore the informational content and the relative strength of those two signals in a given context. In other words, we suggest that focusing the discussion on plasticity regulation would better serve a reasoned elucidation of the causal links involved in the interplay of predictions, afferent data, and the reliability of both, in a way that may offer an account for the different manifestations of psychosis in schizophrenia.

Thus, instead of a prior’s precision being the factor that moderates the model-updating effect of the interaction between PE precision and prediction (in)accuracy (i.e., PE size), in IPH it is the prior model’s STSP that moderates this interaction effect. This results in a three-way interaction: prior STSP * PE-precision * PE-size. See Figure 1 for a graphic description of the factors proposed by the two accounts to underlie psychosis. One might argue that the IPH does not substantially differ from other PC inspired theories of psychosis. That is because, as noted above, PC-related theories often point to deficiency in precision encoding, namely in increasing or reducing the postsynaptic gain on PE signals as function of their precision, as the underlying cause of psychotic symptoms. Apparently, such a deficiency could be interpreted as a consequence of hyper- or hypo-plasticity of the prior models. However, the IPH does not hold that the result of imbalanced STSP is a failure to regulate the generalized amplification of the raw PE signals, noise included. Rather, as discussed below, it argues that imbalanced STSP disrupts the ongoing inference process by dysregulation of the selective tuning (“contrast gain”) of the networks towards expected inputs (see Summerfield & Egner, 2009 for the relevant distinctions) and thus of distilling information from the raw (often noisy) signal and accumulating the informative evidence.

Fig. 1
figure 1

Schematic presentation of factors affecting inference as well as long-term experience-based learning and their bearing on psychosis, according to PC (a) and IPH (b). Under both accounts, long-term associative learning of efferent-afferent regularities is moderated by long-term Hebbian plasticity. Importantly, prediction accuracy (and thus PE size) is influenced by both long-term learning, as well as by moment-to-moment prior model updating. Pink ellipses mark factors playing a distinctive role in psychosis according to each of the two accounts. (a) PC: In psychosis, the effect of prediction accuracy on model updating is moderated by imbalanced prior model’s and PE precisions. (b) IPH: In psychosis, the effect of prediction accuracy on model updating is moderated by PE precision and imbalanced short-term synaptic plasticity (STSP). Also, aberrant long-term synaptic plasticity (LTSP) hampers long-term associative learning and thus affects prediction accuracy

Full size image

Building on the commonalities and differences discussed above, we suggest that by referring to DO and RD as distinct manifestations of plasticity regulation failure in psychosis in schizophrenia, IPH can reconcile the above-mentioned conflicting PC-based theoretical stances regarding the specific nature of precision imbalance in schizophrenia (Corlett et al., 2019; Sterzer et al., 2018). More specifically, with IPH, the apparently conflicting PC-related positions stating that psychosis is the result of the imprecision of priors relative to high precision of PEs,Footnote 3 or the converse, can be reinterpreted as referring to two different syndromes, DO and RD, respectively.

The notion of "weak" priors in psychosis paints an image of a representation system in which, due to its imprecision, the current prior model is unable to ignore even highly imprecise sensory input that is unpredicted (i.e., PE), and thus left unattenuated. This leaves the representation system ever surprised by unselectively salient afferent inputs (Adams et al., 2013), which, when internally generated, may underlie hallucinations (Zhuo et al., 2020). The IPH interprets this scenario as the outcome of hyperplastic representation models in DO patients. On the other hand, the PC inspired idea of "strong" priors implies a representation system that is excessively dominated by prior knowledge or by feeling states that generate mental representations in a rigid and stereotyped fashion, the high precision of which sharply reduces the ability of unpredicted sensory input to update its content. As a result, the representation system has been suggested to provide perceptual experience that is relatively disconnected from actual sensory input. The IPH interprets this state of affairs as the result of hypoplastic representation models in RD patients. We argue that by suggesting pathologic dominance of either bottom-up or top-down processes in schizophrenia-related psychosis, the two types of theories model in fact two different syndromes. It appears that the emphasis put in these theories on symptoms, such as hallucinations or delusions, which in fact occur in both syndromes, albeit at different levels of systematization (Garety et al., 1988; Tsuang & Winokur, 1974), may be obscuring the important distinction between them. An attempt to combine the two conflicting PC-oriented explanations into one that accounts parsimoniously for the two different syndromes would require pointing to a mechanism that determines the nature of the respective manifestations of schizophrenia-related psychosis. Plasticity (dys)regulation is proposed to be such a mechanism.

Direct evidence for (or against) the IPH is lacking so far. Also, most research thought to support either of the PC-related explanations of psychosis does not provide information regarding clinical characteristics of the participants that map on the RD/DO distinction. However, following the next section that offers an interpretation of some central schizophrenia-related psychotic symptoms, several IPH-derived predictions will be presented.

IPH account of schizophrenia psychosis symptoms

Auditory Hallucinations—Auditory hallucinations (AH) are prevalent in schizophrenia-related psychosis. Failures in several mechanisms have been proposed, along with supportive evidence, to account for their occurrence. This includes impairments in the efference copy/corollary discharge mechanism (Feinberg, 1978, 2011); failure of self-monitoring and impaired integration of top-down and bottom-up inputs into the perception system (Frith, 1987); failure to suppress intruding thoughts/memories, and misattribution of internally generated signals due to a deficit in reality (source) monitoring (see Thakkar et al., 2020; Zmigrod et al., 2016 for review and discussion). We suggest that the IPH can reconcile central aspects of these perspectives by differentially accounting for AH appearance in RD and DO patients.

A basic tenet of our account is that conscious experience of auditory (and other) representations depends on sufficiently stable and distinctive sensory evidence accumulation that leads to a sufficiently strong activation of respective functional neural ensembles (Cleeremans, 2011). These ensembles develop in a gradual process by which patterns of synaptic connectivity are being established based on LTSP, recurrent co-exposure to corollary discharges (that accompany self-generated motor and mental actions) and particular sensory or memory-derived input configurations (Stephan et al., 2009). The modulation of these ensembles by sensory data is regulated by STSP, which determines the speed with which the network updates its connection weights, and thus fine-tunes itself to represent incoming evidence, filter out noise and accumulate information (Stokes et al., 2013). Afferent data based on self-generated inputs normally will be predicted and attenuated, thus causing relatively weak and transient activations of the respective functional ensembles, experienced as imagery and not as external stimuli (Feinberg, 2011).

The lower the LTSP, the slower will be the establishment of the assemblies and the more stable and resistant they will be to change. Prior models based on these assemblies will generate consistent predictions regarding self-generated sensory inputs. The lower the STSP, the more selectively and consistently they will be updated by input patterns that contain even weak contextual relationships and/or weak associations with well-established perceptual/cognitive/motor models. The higher the LTSP, the faster will be the establishment of new assemblies and the less stable and the more amenable they will be to change. As a result, prior models based on these assemblies will generate less consistent predictions. The higher the STSP, with the scarcity of strong and stable models acting as dominant attractors to sensory inputs, the less selective and the less consistent the responding to incoming input patterns will be.

In sum, while present in both RD and DO patients (Pfohl & Winokur, 1982; Winokur et al., 1974), AH in these groups are suggested to differ in their structure and dynamics. In RD patients, the stable and sharp tuning of the functional ensembles to internally generated triggering inputs will overcome the attenuation of these inputs caused by their predictability. The resulting strong activation of these assemblies will evoke false auditory experiences (i.e., AHs) that will tend to be repetitive in content and form. In contrast, in DO patients internally generated inputs will go unpredicted, due to the erratic learning and prediction formation process, and thus not be attenuated. AHs will result from the strong activation of transiently existing functional ensembles by these inputs. Hallucinations in DO thus will be temporary, volatile, and not systematically structured (Kendler, 2020). The purported different characteristics of AH in RD and DO clearly call for further study.

By this account, AHs in schizophrenia-related psychosis are not a unitary phenomenon. While in both RD and DO they represent misattribution of internally generated auditory signals to external sources due to metaplasticity disfunction, they differ in their proximal causes. Whereas in RD patients they reflect the dominance of inflexible prior models, as suggested by some PC oriented theorists (Corlett et al., 2019; Powers et al., 2017), in DO patients they reflect bottom-up dominance caused by inconsistency in the prediction generation process, resulting in unwarranted predictions, as argued by corollary-discharge focused theories (Blakemore et al., 2000; Frith, 1987; Hemsley, 1998; Mathalon & Ford, 2008).

Delusions of Alien Control—LTSP underlies learning of the contingencies between self-initiation of motor and mental actions and the afferent consequences of these actions and thus the valid prediction of the flow of kinesthetic and mental representations (Friston, 2010; Friston et al., 2016). We propose that with well-balanced plasticity, the regularities thus learned, and the ensuing predictions, while preserving their basic characteristics, are continuously updated by changing internal (e.g., age, fatigue, mood…) and external (e.g., stimulation, physical medium features) circumstances.

With extremely low LTSP, which we propose characterizes RD patients, the pattern of synaptic weights, and thus the knowledge of efference-reafference regularities embedded in it, will be resistant to updating. Consequently, predictions will be often invalid, with large PEs resulting in the false experience of externally imposed kinesthetic sensations and mental representations (thoughts). With overly high LTSP, the knowledge embedded in the network will be extremely unstable with ensuing inconsistent and thus inaccurate predictions and large PEs. Consequently, DO patients will also experience loss of agency over their movements and thoughts. In both cases, the motivation to make sense of the self-alienating experience may result in the formation of delusions of alien control (Frith, 1992/2015; Maher, 1974). The structure and the specific content of these delusions would reflect cultural and personal idiosyncrasies. In PC terms, this process can be construed as updating of higher-level conceptual models of reality (i.e., priors) by upward propagating prediction errors. Importantly, due to the difference in the stability of the networks involved, delusions of RD patients will be more coherent and stable compared to those of DO patients (Tsuang & Winokur, 1974).

Formal Thought Disorder—With overly high STSP, the hyper-plastic synaptic connections in the semantic associative networks of DO patients, are extremely affected by external and internal noise. Consequently, the patterns of synaptic weights in lexical and semantic networks that underlie different meanings of words and their associations are likely to be unstable and weakly differentiated from each other.Footnote 4 One result of this is that the activation levels of firing patterns representing subordinate (i.e., out of context) meanings of a word may become comparable to those representing context-appropriate meanings (Goldberg & Weinberger, 2000; Spitzer et al., 1993). Overlearned semantic associations, as well as context-related ones, become less effective in guiding the individual’s line of thought and behavior, and competing weak associations gain access to consciousness and guide behavior more frequently. The result would be tangential and incoherent speech expressing the looseness of underlying associations.

Attention Disorders—In the case of hyper-STSP, any context-bound top-down prioritizations of perceptual activation patterns are difficult to sustain. Excessive sensitivity to low SNR inputs makes attention-driven perceptual priorities unstable and weakly determined. In computational terms, in a flat and unstable perceptual attractor landscape, the existence of competing shallow basins weakens the stability of attentional allocation to the winning coalition (or even to the stable formation of a winning coalition) (Lerner et al., 2012). DO (typically hebephrenic) patients' attentional focus would therefore be unstable in terms of foreground/background relations, and poorly controlled willfully (McGhie et al., 1965). RD (typically paranoid) patients, being insensitive to low SNR signals, would be better than DO patients at handling distracting noisy input (Rund, 1982). Yet, IPH implies that they would tend to be hyper-focused on inputs that can be readily assimilated as a result of chronically highly prioritized models. This prediction has yet to be tested. However, data exist demonstrating hyper-focusing in schizophrenia patients. While these studies did not specifically explore symptom correlates of task performance, it is unlikely that the patients studied were characterized as DO, since they were clinically stable outpatients who were able to complete working memory and other cognitive tests (for a review see Luck et al., 2019). In addition, as follows from the purported cognitive control aberrance in both patient types, RD as well as DO patients will have trouble adapting attentional focus to contextually changing attentional priorities.

Motor Abnormalities—If indeed neural networks underlying motor models of DO patients are hyperplastic, they are likely to be weakly differentiated in terms of the relative strength of synaptic connections, and nondistinct relative to competing models. Disrupted long-term learning of efference-reafference contingencies hampers the generation of valid proprioceptive predictions, which are crucial to guiding action selection (Friston et al., 2010). This leads to haphazard motor behavior that is inadequate in terms of the individual's motor goals and changing external circumstances. DO patients are left with a sense of reduced control over their motor behavior, which can be compensated for by de-automatized, slow, tedious, and consciously monitored movements (Chapman, 1966). Such patients also are characterized by abnormal voluntary movements (e.g., mannerisms and posturing), which may represent compensatory efforts to gain and experience control over motor behavior (Lindenmayer et al., 1995).

In the section below, we present several predictions derived from the hypothesis that psychosis in schizophrenia is the outcome of deficient metaplastic regulation in perceptual, cognitive, and motor systems.

Some IPH-Derived Predictions

Mismatch Negativity—Mismatch negativity (MMN) is a negative event-related potential (ERP) deflection. It is observed at frontocentral electrodes 100-200 msec after the presentation of an unattended (typically) auditory stimulus that is deviant in one or more of several parameters (e.g., loudness, frequency, duration) from a preceding string of uniform standard sounds (Näätänen, 1990). In an MMN paradigm known as the roving-standard paradigm, a train of standard tones is presented until interrupted by a deviant tone. As the deviant keeps being presented, it becomes the new standard and so on. MMN magnitude is typically measured as the difference between the amplitude of the deviant in each train, and the amplitude of the last standard in that train (Baldeweg et al., 2004).

In the PC framework, MMN has been conceptualized as reflecting a prediction error resulting from violation of the expectation established by the repeated presentation of standard tones. The repeated presentation of the standard tones has been said to lead to STSP-based learning resulting in fine-tuning of the predictions regarding the forthcoming tones and progressive reduction of prediction errors. This reduction is expressed as gradual emergence of a suppression of the response to repeated standards (termed repetition positivity [RP]), until the emergence of a new MMN (prediction error) when a deviant and thus unpredicted stimulus is presented (Garrido et al., 2008). MMN magnitude has been shown to increase as trains include increased number of preceding standard tones, which has been referred to as the memory trace effect or MMN slope (Baldeweg et al., 2004).

MMN absolute magnitude has been typically shown to be diminished in schizophrenia (Javitt et al., 1998). Some PC oriented authors have suggested that this reflects aberrant top-down predictive signaling related to disordered STSP (Baldeweg et al., 2004; Garrido et al., 2008) or LTSP (Wacongne, 2016). Todd and colleagues (2011) proposed that it is the hindered PE estimation due to imprecise stimulus encoding that underlies decreased MMN magnitude in people with schizophrenia. The IPH embraces the notion that plasticity dysfunctions may underlie the reduced magnitude of MMN evoked responses in schizophrenia. Consistent with the PC account of MMN, it also assumes that the precision of the sensory representation moderates the effect of PE that is operative during the generation of MMN. Yet, it proposes that the mechanism and the expression of MMN abnormality in schizophrenia differs between RD and DO patients. Whereas in RD patients hypoplasticity is hypothesized to slow down learning-based evolution of model-based predictions, in DO patients hyperplasticity is conceived to impede incremental buildup of learning-based expectancies. Consequently, IPH predicts that RD patients will be more sensitive than DO patients to the number of standards in a train, which will result in stronger repetition positivity and memory trace effects. Conversely, for DO patients IPH would ascribe insensitivity to the number of standards in a train, since their incremental adjustment of synaptic strengths (i.e., learning) is hypothesized to be often reset by noise. This should result in weak repetition positivity and memory trace effects.

Interestingly, two recent studies provided results that support the notion of differential MMN patterns among distinct schizophrenia subgroups. Baldeweg and colleagues (2015) compared a schizophrenia patients’ group with healthy controls and Alzheimer disease patients in a roving MMN paradigm with standard trains of 2, 6, and 36 predeviant repetitions. While patients with low cognitive functioning (which is a correlate of DO) showed no memory trace effect whatsoever, patients with high cognitive functioning showed an effect that did not differ from the one shown by controls (Fig. 2). Because cognition in schizophrenia is typically better preserved in patients with a primarily paranoid clinical presentation (i.e., RD) (Magaro, 1980; Silverman, 1964; Venables, 1964; Zalewski et al., 1998), and because poor cognitive functioning is a characteristic of the DO syndrome (Minor & Lysaker, 2014; Ventura et al., 2010), this finding seems to be consistent with IPH.

Fig. 2
figure 2

Mean duration MMN amplitudes across groups, including the higher and lower cognition schizophrenia groups, showing preservation of MMN memory trace effect with repetition in the higher cognition group. This effect was abolished in the lower cognition group (adapted by permission from Baldeweg et al. (2015), Fig. 3b)

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In another study, McCleerey and colleagues (2018) compared recently hallucinating with matched recently not-hallucinating schizophrenia patients in a roving MMN paradigm with standard trains of 3, 8, and 33 repetitions. While the combined patient group did not differ from the control group in the repetition positivity and memory trace effects, the two patient subgroups differed markedly from each other. The hallucinating group showed a significantly (p < 0.003) weaker memory trace effect and a weaker (with marginal significance: p < 0.08) repetition positivity than the nonhallucinating group (Fig. 3). Unfortunately, separate patient groups’ comparisons with the control group were not reported. As hallucinations are prevalent in schizophrenia among both hebephrenic (DO) and paranoid (RD) patients (Pfohl & Winokur, 1982; Winokur et al., 1974), it is not possible to determine from the information given in the paper how the participant groups map onto the RD-DO spectrum. Nevertheless, these data suggest the utility of further exploring symptom-linked heterogeneity in the mechanisms causing abnormal performance on tests used to define endophenotypes.

Fig. 3
figure 3

Mean amplitude and 95% confidence interval to standard and deviant tones (a) Healthy comparison subjects (HC); (b) schizophrenia–hallucinations group (Sz-H); and (c) schizophrenia–no hallucinations group (Sz-NH). (Adapted by permission from McCleery et al. (2018), Fig. 2.)

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Task switching with masked priming—Studies using factor analysis to draw the outlines of schizophrenia subtypes typically report negligible correlations between DO and RD factors (Chen et al., 2020), suggesting orthogonality between them. That could pose a problem to IPH, unless a central feature of the hypothesis is taken into account. According to IPH, both DO and RD patients should suffer from deficient cognitive control. This may increase the covariance between DO and RD factors and, if not controlled for, suppress any negative relationship between them, or even generate a mild positive correlation. To test this, one should dissociate the differential direct effects of hyper-/hypo-plasticity on low-level processing from those related to the failure, in both syndromes, of high-level systems to effectively regulate that processing. A masked-primed task-switching categorization paradigm is proposed to serve this purpose.

In contrast to earlier theories of cognitive control (Posner et al., 2004; Schneider & Shiffrin, 1977), more recent evidence suggests that high-level cognitive activity and unconscious automatic processing affect each other (Ansorge et al., 2014). Accordingly, masked priming has been shown to exert transient effects on high level cognitive operations such as response activation, semantic processing, and attention (Kiefer et al., 2019; Kiefer & Martens, 2010). On the other hand, as demonstrated by Kunde et al. (2003), it seems that unconscious processing of subliminal primes can be adapted to current conscious goals, with priming effects changing according to changing task instructions.

In a masked-prime, task-switching, categorization paradigm, the participant is required to categorize a target word (e.g., fly/bus) according to a certain dimension (e.g., small/big). Each target stimulus is preceded by a subliminal double-masked prime consisting of a word designating a small/big object (e.g., button/elephant). Occasionally, an unpredictable task change is signaled by a changing mask color, which requires switching from one categorization rule to another, e.g., from small/big to animate/inanimate. Given the purportedly increased STSP in DO patients, these patients are expected to show, in a masked primed object-categorization task, stronger priming effects compared with controls as well as RD patients. However, despite a crucial difference in underlying pathology, both DO and RD patients are hypothesized to have impaired cognitive control. Thus, compared with controls, both patient subtypes should show a weaker influence of task-manipulation on priming effects when categorization criteria are randomly switched by instructions.

Visual Search—Visual search (VS) has been long used to study attention. In a typical paradigm, the participant’s task is to locate a target in a display with a varied number of distracters. The target may differ from the distracters in any number of physical or semantic dimensions (Eckstein, 2011). Compared with controls, schizophrenia patients have been reported to show slower and less accurate VS performance (Fuller et al., 2006). One of the variables that can be manipulated in VS is the SNR of targets and distracters. To enable differential predictions for IPH- and PC-related theories, a design is suggested in which half of the items in each display are salient (high SNR) and half of them are degraded (low SNR). At each trial, the target can unpredictably change its allocation to one of these groups, of which the participant is informed before the trial starts. As IPH suggests that cognitive control in schizophrenia patients, regardless of the dominant syndrome, is deficient relative to healthy controls, both DO and RD patients are expected to have trouble with conforming to the changing target designation. In consequence, their VS performance is expected to be lower than that of control participants. In addition, because DO patients are presumed to be more sensitive than RD ones to low SNR signals, it can be predicted that they will have higher hit rates, but also higher false alarms rates than the RD patients in the condition in which the target is part of the low-SNR group.

As noted above, PC suggests that attention is the maximization of gain of sensory input, the precision of which is, or is-expected-to-be, high (Feldman & Friston, 2010; Hohwy, 2012). If so, in the above described VS task, control participants should enhance the gain of PEs related to high SNR items. Since according to the canonical PC account of psychosis precision of PEs in patients (regardless of syndrome) is overly precise (e.g. Friston et al., 2014; Sterzer et al., 2016), it can be predicted that patients will perform at least as well as controls in the condition in which targets belong to the high SNR group of items. On the other hand, other PC-related views suggest that in psychosis, PE precision is relatively low (e.g. Corlett et al., 2019; Powers et al., 2017). If this is indeed the case, patients should fail to enhance the gain on PEs in the low SNR target condition, as stimuli in this condition will be typically regarded as noise. This would predict both lower hit rate and lower false-alarm rate for them, compared with controls—interestingly, a prediction that is similar to the IPH prediction for RD patients.

Conclusions

IPH offers an explanation for two distinct clinical facets of schizophrenia. While doing so, IPH refers to two neural mechanisms: LTSP and STSP. Each of these underlies a crucial process that needs to be addressed in any prediction-oriented theory of psychosis: the generation of predictions from existing models based on experience-dependent learning, and real-time updating of these models by prediction error signals. The IPH hypothesis suggests that disequilibrium in either of these processes can account for the genesis of a range of psychotic symptoms and task performance abnormalities in schizophrenia. At a general level, this assertion is certainly not new. As already noted, impaired regulation of neural plasticity has been previously suggested to contribute to psychosis in schizophrenia (Forsyth & Lewis, 2017; Friston & Frith, 1995; Haracz, 1984; Stephan et al., 2006). Stephan et al. (2009) were explicit in emphasizing the role of aberrance in LTSP and STSP as a setting condition for the disruption of prediction generation and updating in psychosis. Keshavan et al. (2015) as well as Voss et al. (2019) pointed to neurophysiological indications of both diminished and excessive neuroplasticity in schizophrenia. However, none of these discussions highlighted how different forms of plasticity disturbance could account for clinical differences and laboratory findings in patients with RD versus DO presentations.

Amongst the many questions left unanswered by the IPH account, one seems to stand out: What is the mechanism that determines the nature of the plasticity imbalance in schizophrenia-related psychosis, and thus of the respective clinical profiles? A comprehensive treatment of this question is beyond the scope of this article. However, it may be worthwhile to consider the previously mentioned possibility raised by Kraepelin, Bleuler (as reviewed by Kendler, 2020; Peralta & Cuesta, 2011) and others (Mishara, 2010; Wright & Kydd, 1986), that the fundamental impairment in schizophrenia is a type of disorganization/fragmentation/loosening, and that some patients can compensate for this by imposing a structure on the novel mental state and constructing a meaningful narrative (or insight) out of what is happening, even if the conceptual reorganization of experience does not correspond well to reality (i.e., it involves a delusion).

In this context, a malleable piece of plasticine would be a useful metaphor for normal network functioning. A psychotic process may be conceived as excessive heat that causes the plasticine to melt, i.e., to enter a hyperplastic state (leading to DO). Many patients can partially compensate for this by “freezing” or lowering the temperature at certain points on the plasticine. When that happens, whatever dis-configuration exists at that part of the plasticine is now stable, but also supports an aspect of RD. Importantly, whether frozen or melted, the plasticine is resistant to purposeful reshaping, which provides an analogy for the above discussed relative inability of cognitive control to effectively regulate activity of lower-level hyper-/hypo-plastic systems. As mentioned above, Braun et al. (2016) found in an fMRI study that schizophrenia patients show increased “network flexibility”—a measure of the network’s pace of functional reconfiguration during working memory performance. Interestingly, in that study network flexibility correlated positively with the perseveration score of the Wisconsin Card Sorting Task—a finding that fits the idea that perseveration is a compensatory response to an internal experience of instability. See Palaniyappan (2019) for a an extensive discussion of this notion.

Despite the historical continuity of this hypothesis in psychiatry, the factors that could account for the compensatory ability to generate the “insights” that drive delusional thinking remain unknown. Importantly, disorganization is evident at the outset of schizophrenia (Silva et al., 2020) and also is found in people with schizotypal characteristics (Angers et al., 2021)—a syndrome that is statistically independent of RD (Dodell-Feder et al., 2019; Reynolds et al., 2000). Therefore, it should not be considered secondary to positive symptoms, nor an aspect of illness progression, neurodegeneration, or other chronicity-related factors, even though its presence is correlated with poorer outcomes in both patients (Farmer et al., 1983; Harrow & Marengo, 1986) and at-risk individuals (Cotter et al., 2014). An intriguing question concerns the simultaneous or intermittent presence of RD and DO in the same patient, which can occur, despite clear evidence for their separability as syndromes (Williams et al., 1993). This clinical reality speaks to the complexity of the issues and to the undoubtedly additional and perhaps unknown (at this point) factors that determine the final expression of symptoms of schizophrenia in an individual, as well as to the still poorly understood heterogeneity within symptom factors in schizophrenia (Peralta & Cuesta, 1998; Strauss et al., 2018), all of which will affect the construct validity of our syndrome categories and our ability to study them. Nevertheless, it is clear that just as positive and negative symptoms can occur in the same person, despite differences in presumed pathophysiology and their conceptualization as different syndromes, and that hyperdopaminergia and hypodopaminergia, in different brain regions, can exist in the same patient and give rise to positive and negative symptoms, respectively (Davis et al., 1991; Weinberger & Berman, 1988; Weinstein et al., 2017), clarification of the pathways to DO and RD is likely to increase our understanding of heterogeneity within schizophrenia to help move the field toward a dimensional approach to the disorder (Picardi et al., 2012). In addition, IPH may stimulate further development of cognitive, perceptual learning, brain stimulation, physical exercise, and pharmacological treatments targeting the regulation of neuroplasticity (for reviews see Keshavan et al., 2015; Voss et al., 2019). Thus far, currently explored treatments typically monitor the behavioral and neurophysiological indications of posttreatment increase in plasticity. Acknowledgement of the potential benefits of increasing or decreasing neuroplasticity depending on the symptom and brain region/network targeted may motivate a more nuanced therapeutic approach aimed at differentially regulating plasticity in DO and RD patients.