Cognitive simulation along with neural adaptation explain effects of suggestions: a novel theoretical framework.

Hypnosis is an effective intervention with proven efficacy that is employed in clinical settings and for investigating various cognitive processes. Despite their practical success, no consensus exists regarding the mechanisms underlying well-established hypnotic phenomena. Here, we suggest a new framework called the Simulation-Adaptation Theory of Hypnosis (SATH). SATH expands the predictive coding framework by focusing on (a) redundancy elimination in generative models using intrinsically generated prediction errors, (b) adaptation due to amplified or prolonged neural activity, and (c) using internally generated predictions as a venue for learning new associations. The core of our treatise is that simulating proprioceptive, interoceptive, and exteroceptive signals, along with the top-down attenuation of the precision of sensory prediction errors due to neural adaptation, can explain objective and subjective hypnotic phenomena. Based on these postulations, we offer mechanistic explanations for critical categories of direct verbal suggestions, including (1) direct-ideomotor, (2) challenge-ideomotor, (3) perceptual, and (4) cognitive suggestions. Notably, we argue that besides explaining objective responses, SATH accounts for the subjective effects of suggestions, i.e., the change in the sense of agency and reality. Finally, we discuss individual differences in hypnotizability and how SATH accommodates them. We believe that SATH is exhaustive and parsimonious in its scope, can explain a wide range of hypnotic phenomena without contradiction, and provides a host of testable predictions for future research.

Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework.

Psychological treatments for persecutory delusions, particularly cognitive behavioral therapy for psychosis, are efficacious; however, mechanistic theories explaining why they work rarely bridge to the level of cognitive neuroscience. Predictive coding, a general brain processing theory rooted in cognitive and computational neuroscience, has increasing experimental support for explaining symptoms of psychosis, including the formation and maintenance of delusions. Here, we describe recent advances in cognitive behavioral therapy for psychosis-based psychotherapy for persecutory delusions, which targets specific psychological processes at the computational level of information processing. We outline how Bayesian learning models employed in predictive coding are superior to simple associative learning models for understanding the impact of cognitive behavioral interventions at the algorithmic level. We review hierarchical predictive coding as an account of belief updating rooted in prediction error signaling. We examine how this process is abnormal in psychotic disorders, garnering noisy sensory data that is made sense of through the development of overly strong delusional priors. We argue that effective cognitive behavioral therapy for psychosis systematically targets the way sensory data are selected, experienced, and interpreted, thus allowing for the strengthening of alternative beliefs. Finally, future directions based on these arguments are discussed.

Delusions are distressing and disabling psychiatric symptoms. Cognitive behavioral therapy for psychosis (CBTp) is the leading psychotherapeutic approach for treating delusions. Predictive coding is a contemporary cognitive neuroscience framework that is increasingly being used to explain mechanisms of delusions. In this article, we attempt to integrate CBTp within the predictive coding framework, outlining how effective CBTp techniques impact aspects of the predictive coding model to contribute to cutting-edge treatment and cognitive neuroscience research on delusions and inform recommendations for treatment advancement.

Associations of Cognitive Expectancies With Auditory Hallucinations and Hallucinatory-Like Experiences in Patients With Schizophrenia.

BACKGROUND: Various neurocognitive models explore perceptual distortions and hallucinations in schizophrenia and the general population. A variant of predictive coding account suggests that strong priors, like cognitive expectancy, may influence perception. This study examines if stronger cognitive expectancies result in more auditory false percepts in clinical and healthy control groups, investigates group differences, and explores the association between false percepts and hallucinations. STUDY DESIGN: Patients diagnosed with schizophrenia with current auditory hallucinations (n = 51) and without hallucinations (n = 66) and healthy controls (n = 51) underwent the False Perception Task under various expectancy conditions. All groups were examined for the presence and severity of hallucinations or hallucinatory-like experiences. STUDY RESULTS: We observed a main effect of condition across all groups, ie, the stronger the cognitive expectancy, the greater the ratio of auditory false percepts. However, there was no group effect for the ratio of auditory false percepts. Despite modest pairwise correlations in the hallucinating group, the ratio of auditory false percepts was not predicted by levels of hallucinations and hallucinatory-like experiences in a linear mixed model. CONCLUSIONS: The current study demonstrates that strong priors in the form of cognitive expectancies affect perception and play a role in perceptual disturbances. There is also a tentative possibility that overreliance on strong priors may be associated with hallucinations in currently hallucinating subjects. Possible, avoidable confounding factors are discussed in detail.

Contextual expectations shape the motor coding of movement kinematics during the prediction of observed actions: A TMS study.

Contextual information may shape motor resonance and support intention understanding during observation of incomplete, ambiguous actions. It is unclear, however, whether this effect is contingent upon kinematics ambiguity or contextual information is continuously integrated with kinematics to predict the overarching action intention. Moreover, a differentiation between the motor mapping of the intention suggested by context or kinematics has not been clearly demonstrated. In a first action execution phase, 29 participants were asked to perform reaching-to-grasp movements towards big or small food objects with the intention to eat or to move; electromyography from the First Dorsal Interosseous (FDI) and Abductor Digiti Minimi (ADM) was recorded. Depending on object size, the intentions to eat or to move were differently implemented by a whole-hand or a precision grip kinematics, thus qualifying an action-muscle dissociation. Then, in a following action prediction task, the same participants were asked to observe an actor performing the same actions and to predict his/her intention while motor resonance was assessed for the same muscles. Of note, videos were interrupted at early or late action phases, and actions were embedded in contexts pointing toward an eating or a moving intention, congruently or incongruently with kinematics. We found greater involvement of the FDI or ADM in the execution of precision or whole-hand grips, respectively. Crucially, this pattern of activation was mirrored during observation of the same actions in congruent contexts, but it was cancelled out or reversed in the incongruent ones, either when videos were interrupted at either early or long phases of action deployment. Our results extend previous evidence by showing that contextual information shapes motor resonance not only under conditions of perceptual uncertainty but also when more informative kinematics is available.

Chronic pain - A maladaptive compensation to unbalanced hierarchical predictive processing.

The ability to perceive pain presents an interesting evolutionary advantage to adapt to an ever-changing environment. However, in the case of chronic pain (CP), pain perception hinders the capacity of the system to adapt to changing sensory environments. Similar to other chronic perceptual disorders, CP is also proposed to be a maladaptive compensation to aberrant sensory predictive processing. The local-global oddball paradigm relies on learning hierarchical rules and processing environmental irregularities at a local and global level. Prediction errors (PE) between actual and predicted input typically trigger an update of the forward model to limit the probability of encountering future PEs. It has been hypothesised that CP hinders forward model updating, reflected in increased local deviance and decreased global deviance. In the present study, we used the local-global paradigm to examine how CP influences hierarchical learning relative to healthy controls. As hypothesised, we observed that deviance in the stimulus characteristics evoked heightened local deviance and decreased global deviance of the stimulus-driven PE. This is also accompanied by respective changes in theta phase locking that is correlated with the subjective pain perception. Changes in the global deviant in the stimulus-driven-PE could also be explained by dampened attention-related responses. Changing the context of the auditory stimulus did not however show a difference in the context-driven PE. These findings suggest that CP is accompanied by maladaptive forward model updating where the constant presence of pain perception disrupts local deviance in non-nociceptive domains. Furthermore, we hypothesise that the auditory-processing based biomarker identified here could be a marker of domain-general dysfunction that could be confirmed by future research.

Understanding music and aging through the lens of Bayesian inference.

Bayesian inference has recently gained momentum in explaining music perception and aging. A fundamental mechanism underlying Bayesian inference is the notion of prediction. This framework could explain how predictions pertaining to musical (melodic, rhythmic, harmonic) structures engender action, emotion, and learning, expanding related concepts of music research, such as musical expectancies, groove, pleasure, and tension. Moreover, a Bayesian perspective of music perception may shed new insights on the beneficial effects of music in aging. Aging could be framed as an optimization process of Bayesian inference. As predictive inferences refine over time, the reliance on consolidated priors increases, while the updating of prior models through Bayesian inference attenuates. This may affect the ability of older adults to estimate uncertainties in their environment, limiting their cognitive and behavioral repertoire. With Bayesian inference as an overarching framework, this review synthesizes the literature on predictive inferences in music and aging, and details how music could be a promising tool in preventive and rehabilitative interventions for older adults through the lens of Bayesian inference.

Predictive sequence learning in the hippocampal formation.

The hippocampus receives sequences of sensory inputs from the cortex during exploration and encodes the sequences with millisecond precision. We developed a predictive autoencoder model of the hippocampus including the trisynaptic and monosynaptic circuits from the entorhinal cortex (EC). CA3 was trained as a self-supervised recurrent neural network to predict its next input. We confirmed that CA3 is predicting ahead by analyzing the spike coupling between simultaneously recorded neurons in the dentate gyrus, CA3, and CA1 of the mouse hippocampus. In the model, CA1 neurons signal prediction errors by comparing CA3 predictions to the next direct EC input. The model exhibits the rapid appearance and slow fading of CA1 place cells and displays replay and phase precession from CA3. The model could be learned in a biologically plausible way with error-encoding neurons. Similarities between the hippocampal and thalamocortical circuits suggest that such computation motif could also underlie self-supervised sequence learning in the cortex.

The fear-avoidance model as an embodied prediction of threat.

The fear-avoidance model is a well-established framework in the understanding of persistent pain. It proposes a dichotomous path: either the context is interpreted as safe; there is no fear reaction and, therefore, the individual engages in active (positive) coping; or the context is interpreted as threatening, leading to a self-reinforcing vicious circle of fear and (negative) avoidance. We propose an embodied interpretation of this phenomenon employing the joint framework of predictive coding and active inference. The key idea is that multisensory integration of exteroceptive, proprioceptive, and interoceptive sensory inputs can lead to dysfunctional experiences of threat in nonthreatening situations. Threat inference can promote fear responses, maladaptive strategies (i.e., avoidance) and self-provides evidence for threat in associated or future contexts, or both. Under this treatment, the prediction of nonrealized threat becomes self-evidencing and context-invariant, and hence self-perpetuating. Safety cues are unable to attenuate the interpretation of the negative context as the dominant inference of the context is threatful and gains more precision and becomes resistant over time. Our model provides an explanation for the emergence of a dysfunctional fear response in the clinical setting despite apparent safety based on modern concepts from theoretical (computational) neuroscience.

Neuromodulation of safety and surprise in the early stages of infant development: affective homeostatic regulation in bodily and mental functions.

Developing a sense of internal safety and security depends mainly on others: numerous neuromodulators play a significant role in the homeostatic process, regulating the importance of proximity to a caregiver and experiencing feelings that enable us to regulate our interdependence with our conspecifics since birth. This array of neurofunctional structures have been called the SEPARATION DISTRESS system (now more commonly known as the PANIC/ GRIEF system). This emotional system is mainly involved in the production of depressive symptoms. The disruption of this essential emotional balance leads to the onset of feelings of panic followed by depression. We will focus on the neuropeptides that play a crucial role in social approach behavior in mammals, which enhance prosocial behavior and facilitate the consolidation of social bonds. We propose that most prosocial behaviors are regulated through the specific neuromodulators acting on salient intersubjective stimuli, reflecting an increased sense of inner confidence (safety) in social relationships. This review considers the neurofunctional link between the feelings that may ultimately be at the base of a sense of inner safety and the central neuromodulatory systems. This link may shed light on the clinical implications for the development of early mother-infant bonding and the depressive clinical consequences when this bond is disrupted, such as in post-partum depression, depressive feelings connected to, addiction, neurofunctional disorders, and psychological trauma.

Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling?

Our brains are constantly adapting to changes in our visual environments. Neural adaptation exerts a persistent influence on the activity of sensory neurons and our perceptual experience, however there is a lack of consensus regarding how adaptation is implemented in the visual system. One account describes fatigue-based mechanisms embedded within local networks of stimulus-selective neurons (networked fatigue models). Another depicts adaptation as a product of stimulus expectations (predictive coding models). In this review, I evaluate neuroimaging and psychophysical evidence that poses fundamental problems for predictive coding models of neural adaptation. Specifically, I discuss observations of distinct repetition and expectation effects, as well as incorrect predictions of repulsive adaptation aftereffects made by predictive coding accounts. Based on this evidence, I argue that networked fatigue models provide a more parsimonious account of adaptation effects in the visual system. Although stimulus expectations can be formed based on recent stimulation history, any consequences of these expectations are likely to co-occur (or interact) with effects of fatigue-based adaptation. I conclude by proposing novel, testable hypotheses relating to interactions between fatigue-based adaptation and other predictive processes, focusing on stimulus feature extrapolation phenomena.

Sense of ownership influence on tactile perception: Is the predictive coding account valid for the somatic rubber hand Illusion?

According to the predictive coding account, the attenuation of tactile perception on the hand exposed to the visuo-tactile Rubber Hand Illusion (vtRHI) relies on a weight increase of visual information deriving from the fake hand and a weight decrease of tactile information deriving from the individual's hand. To explore if this diametrical modulation persists in the absence of vision when adopting the somatic RHI (sRHI), we recorded tactile acuity measures before and after both RHI paradigms in 31 healthy individuals, hypothesizing a weight decrease for somatosensory information deriving from the hand undergoing the illusion and a weight increase for those deriving from the contralateral hand in the sRHI. Our results showed a significant overall decrease in tactile acuity on the hand undergoing the illusion whilst no changes emerged on the contralateral hand during sRHI. Since the sRHI was not accompanied by the hand spatial remapping, despite the generation of the feeling of ownership toward the fake hand, we hypothesized spatial remapping might play a pivotal role in determining sensory information weight attribution.

Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision.

Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin-targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top-down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.

Imagination vs. routines: festive time, weekly time, and the predictive brain.

This paper examines the relationship between societal structures shaped by traditions, norms, laws, and customs, and creative expressions in arts and media through the lens of the predictive coding framework in cognitive science. The article proposes that both dimensions of culture can be viewed as adaptations designed to enhance and train the brain's predictive abilities in the social domain. Traditions, norms, laws, and customs foster shared predictions and expectations among individuals, thereby reducing uncertainty in social environments. On the other hand, arts and media expose us to simulated experiences that explore alternative social realities, allowing the predictive machinery of the brain to hone its skills through exposure to a wider array of potentially relevant social circumstances and scenarios. We first review key principles of predictive coding and active inference, and then explore the rationale of cultural traditions and artistic culture in this perspective. Finally, we draw parallels between institutionalized normative habits that stabilize social worlds and creative and imaginative acts that temporarily subvert established conventions to inject variability.

Investigating sensitivity to multi-domain prediction errors in chronic auditory phantom perception.

The perception of a continuous phantom in a sensory domain in the absence of an external stimulus is explained as a maladaptive compensation of aberrant predictive coding, a proposed unified theory of brain functioning. If this were true, these changes would occur not only in the domain of the phantom percept but in other sensory domains as well. We confirm this hypothesis by using tinnitus (continuous phantom sound) as a model and probe the predictive coding mechanism using the established local-global oddball paradigm in both the auditory and visual domains. We observe that tinnitus patients are sensitive to changes in predictive coding not only in the auditory but also in the visual domain. We report changes in well-established components of event-related EEG such as the mismatch negativity. Furthermore, deviations in stimulus characteristics were correlated with the subjective tinnitus distress. These results provide an empirical confirmation that aberrant perceptions are a symptom of a higher-order systemic disorder transcending the domain of the percept.

Suppression and omission effects in auditory predictive processing-Two of the same?

Recent theories describe perception as an inferential process based on internal predictive models that are adjusted by prediction violations (prediction error). Two different modulations of the auditory N1 event-related brain potential component are often discussed as an expression of auditory predictive processing. The sound-related N1 component is attenuated for self-generated sounds compared to the N1 elicited by externally generated sounds (N1 suppression). An omission-related component in the N1 time-range is elicited when the self-generated sounds are occasionally omitted (omission N1). Both phenomena were explained by action-related forward modelling, which takes place when the sensory input is predictable: prediction error signals are reduced when predicted sensory input is presented (N1 suppression) and elicited when predicted sensory input is omitted (omission N1). This common theoretical account is appealing but has not yet been directly tested. We manipulated the predictability of a sound in a self-generation paradigm in which, in two conditions, either 80% or 50% of the button presses did generate a sound, inducing a strong or a weak expectation for the occurrence of the sound. Consistent with the forward modelling account, an omission N1 was observed in the 80% but not in the 50% condition. However, N1 suppression was highly similar in both conditions. Thus, our results demonstrate a clear effect of predictability for the omission N1 but not for the N1 suppression. These results imply that the two phenomena rely (at least in part) on different mechanisms and challenge prediction related accounts of N1 suppression.

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities.

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants from the expected patterns of sensory input. Studies show that MMN diminishes when sequence patterns become more predictable over a longer timescale. This implies that MMN is composed of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms, with one peaking early in the central-frontal area and the other late in a more frontal area. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multitiered neural marker applicable in clinical settings.

Phantom perception as a Bayesian inference problem: a pilot study.

Tinnitus is the perception of a continuous sound in the absence of an external source. Although the role of the auditory system is well investigated, there is a gap in how multisensory signals are integrated to produce a single percept in tinnitus. Here, we train participants to learn a new sensory environment by associating a cue with a target signal that varies in perceptual threshold. In the test phase, we present only the cue to see whether the person perceives an illusion of the target signal. We perform two separate experiments to observe the behavioral and electrophysiological responses to the learning and test phases in 1) healthy young adults and 2) people with continuous subjective tinnitus and matched control subjects. We observed that in both parts of the study the percentage of false alarms was negatively correlated with the 75% detection threshold. Additionally, the perception of an illusion goes together with increased evoked response potential in frontal regions of the brain. Furthermore, in patients with tinnitus, we observe no significant difference in behavioral or evoked response in the auditory paradigm, whereas patients with tinnitus were more likely to report false alarms along with increased evoked activity during the learning and test phases in the visual paradigm. This emphasizes the importance of integrity of sensory pathways in multisensory integration and how this process may be disrupted in people with tinnitus. Furthermore, the present study also presents preliminary data supporting evidence that tinnitus patients may be building stronger perceptual models, which needs future studies with a larger population to provide concrete evidence on.NEW & NOTEWORTHY Tinnitus is the continuous phantom perception of a ringing in the ears. Recently, it has been suggested that tinnitus may be a maladaptive inference of the brain to auditory anomalies, whether they are detected or undetected by an audiogram. The present study presents empirical evidence for this hypothesis by inducing an illusion in a sensory domain that is damaged (auditory) and one that is intact (visual). It also presents novel information about how people with tinnitus process multisensory stimuli in the audio-visual domain.

An EEG-Based Neuroplastic Approach to Predictive Coding in People With Schizophrenia or Traumatic Brain Injury.

Despite different etiologies, people with schizophrenia (SCZ) or with traumatic brain injury (TBI) both show aberrant neuroplasticity. One neuroplastic mechanism that may be affected is prediction error coding. We used a roving mismatch negativity (rMMN) paradigm which uses different lengths of standard tone trains and is optimized to assess predictive coding. Twenty-five SCZ, 22 TBI (mild to moderate), and 25 healthy controls were assessed. We used a frequency-deviant rMMN in which the number of standards preceding the deviant was either 2, 6, or 36. We evaluated repetition positivity to the standard tone immediately preceding a deviant tone (repetition positivity [RP], to assess formation of the memory trace), deviant negativity to the deviant stimulus (deviant negativity [DN], which reflects signaling of a prediction error), and the difference wave between the 2 (the MMN). We found that SCZ showed reduced DN and MMN compared with healthy controls and with people with mild to moderate TBI. We did not detect impairments in any index (RP, DN, or MMN) in people with TBI compared to controls. Our findings suggest that prediction error coding assessed with rMMN is aberrant in SCZ but intact in TBI, though there is a suggestion that severity of head injury results in poorer prediction error coding.

Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data.

Many recent studies have shown that representations drawn from neural network language models are extremely effective at predicting brain responses to natural language. But why do these models work so well? One proposed explanation is that language models and brains are similar because they have the same objective: to predict upcoming words before they are perceived. This explanation is attractive because it lends support to the popular theory of predictive coding. We provide several analyses that cast doubt on this claim. First, we show that the ability to predict future words does not uniquely (or even best) explain why some representations are a better match to the brain than others. Second, we show that within a language model, representations that are best at predicting future words are strictly worse brain models than other representations. Finally, we argue in favor of an alternative explanation for the success of language models in neuroscience: These models are effective at predicting brain responses because they generally capture a wide variety of linguistic phenomena.