Hierarchical Predictive Coding Research Articles

Perceptual information processing in table tennis players: based on top-down hierarchical predictive coding

Perceptual information processing in table tennis players: based on top-down hierarchical predictive coding

The hierarchical predictive coding framework of post-traumatic stress disorder

Humans rely on internal models from sensory information to predict future action, and the default mode network (DMN) plays a pivotal role in this process. These models, however, can become distorted or disrupted in cases of post-traumatic stress disorder (PTSD) because traumatic stress can maladaptively alter how humans understand themselves and predict what happens in the world around them. Here, we examine the relationship between PTSD and this predictive phenomenon known as hierarchical predictive coding implicated in the DMN. We postulate that a hierarchical predictive coding framework will help researchers and clinicians more effectively explain the neural and psychological effects of PTSD among individuals who live with this condition, which may contribute to more targeted research and treatment outcomes.

Predicting and coding sound into action translation in spinal cord injured people.

Motor activation in response to perception of action-related stimuli may depend on a resonance mechanism subserving action understanding. The extent to which this mechanism is innate or learned from sensorimotor experience is still unclear. Here, we recorded EEG while people with paraplegia or tetraplegia consequent to spinal cord injury (SCI) and healthy control participants were presented with action sounds produced by body parts (mouth, hands or feet) that were or were not affected by SCI. Non-action sounds were used as further control. We observed reduced brain activation in subjects affected by SCI at both pre- and post-stimulus latencies specifically for those actions whose effector was disconnected by the spinal lesion (i.e., hand sound for tetraplegia and leg sound for both paraplegia and tetraplegia). Correlation analyses showed that these modulations were functionally linked with the chronicity of the lesion, indicating that the longer the time the lesion- EEG data acquisition interval and/or the more the lesion occurred at a young age, the weaker was the cortical activity in response to these action sounds. Tellingly, source estimations confirmed that these modulations originated from a deficit in the motor resonance mechanism, by showing diminished activity in premotor (during prediction and perception) and near the primary motor (during perception) areas. Such dissociation along the cortical hierarchy is consistent with both previous reports in healthy subjects and with hierarchical predictive coding accounts. Overall, these data expand on the notion that sensorimotor experience maintains the cortical representations relevant to anticipate and perceive action-related stimuli.

Behavior-relevant top-down cross-modal predictions in mouse neocortex.

Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.

Distinct roles of delta- and theta-band neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

The brain tracks and encodes multi-level speech features during spoken language processing. It is evident that this speech tracking is dominant at low frequencies (<8 Hz) including delta and theta bands. Recent research has demonstrated distinctions between delta- and theta-band tracking but has not elucidated how they differentially encode speech across linguistic levels. Here, we hypothesised that delta-band tracking encodes prediction errors (enhanced processing of unexpected features) while theta-band tracking encodes neural sharpening (enhanced processing of expected features) when people perceive speech with different linguistic contents. EEG responses were recorded when normal-hearing participants attended to continuous auditory stimuli that contained different phonological/morphological and semantic contents: (1) real-words, (2) pseudo-words and (3) time-reversed speech. We employed multivariate temporal response functions to measure EEG reconstruction accuracies in response to acoustic (spectrogram), phonetic and phonemic features with the partialling procedure that singles out unique contributions of individual features. We found higher delta-band accuracies for pseudo-words than real-words and time-reversed speech, especially during encoding of phonetic features. Notably, individual time-lag analyses showed that significantly higher accuracies for pseudo-words than real-words started at early processing stages for phonetic encoding (<100 ms post-feature) and later stages for acoustic and phonemic encoding (>200 and 400 ms post-feature, respectively). Theta-band accuracies, on the other hand, were higher when stimuli had richer linguistic content (real-words > pseudo-words > time-reversed speech). Such effects also started at early stages (<100 ms post-feature) during encoding of all individual features or when all features were combined. We argue these results indicate that delta-band tracking may play a role in predictive coding leading to greater tracking of pseudo-words due to the presence of unexpected/unpredicted semantic information, while theta-band tracking encodes sharpened signals caused by more expected phonological/morphological and semantic contents. Early presence of these effects reflects rapid computations of sharpening and prediction errors. Moreover, by measuring changes in EEG alpha power, we did not find evidence that the observed effects can be solitarily explained by attentional demands or listening efforts. Finally, we used directed information analyses to illustrate feedforward and feedback information transfers between prediction errors and sharpening across linguistic levels, showcasing how our results fit with the hierarchical Predictive Coding framework. Together, we suggest the distinct roles of delta and theta neural tracking for sharpening and predictive coding of multi-level speech features during spoken language processing.

Corticofugal regulation of predictive coding

Sensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. A potential substrate for top-down predictive cues is the massive set of descending projections from the cortex to subcortical structures, although the role of these corticofugal neurons in predictive processing has never been directly assessed. We tested the effect of optogenetic inactivation of the auditory cortico-collicular feedback in awake mice on responses of auditory midbrain neurons to stimuli designed to test prediction error and repetition suppression. Inactivation of the cortico-collicular pathway led to a decrease in prediction error and repetition enhancement in these neurons. Overall, our results demonstrate that the auditory cortex provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

Invited Session III: Neural network models of the visual system: Robust information representation in hierarchical networks of the visual cortex.

The mammalian visual system has an exquisite ability to encode relevant information from noisy sensory inputs in natural scenes. Such robust yet adaptable computation is believed to emerge from the uniquely complex connectivity structure of biological neural networks. In this talk, I will focus on the hierarchical organization of the cortical network, and discuss how this unique network structure shapes robust representations of noisy visual inputs via predictive coding. Using a predictive coding model of hierarchically-related visual cortical areas, we link feedback connections to top-down predictions of the lower cortical neural activity. Our model shows that hierarchical predictive coding explains the mechanisms of robust recognition of noisy objects in intermediate cortical areas. Secondly, we study how context-specific information may be represented and modified across learning in populations of neurons in a recurrent neural network model of hierarchically-related cortical areas. Motivated by an experimental study investigating neural activities in response to expected and unexpected natural images in mouse visual cortex, we test how the divergence of feedforward and feedback connections underlie differential representations of expected and surprising sensory information across cortical areas and layers. In sum, our study provides insights into how robust and adaptable visual encoding arise from biologically-motivated connectivity across the cortical hierarchy.

Visual Information Is Predictively Encoded in Occipital Alpha/Low-Beta Oscillations.

Hierarchical predictive coding networks are a general model of sensory processing in the brain. Under neural delays, these networks have been suggested to naturally generate oscillatory activity in approximately the α frequency range (∼8-12 Hz). This suggests that α oscillations, a prominent feature of EEG recordings, may be a spectral "fingerprint" of predictive sensory processing. Here, we probed this possibility by investigating whether oscillations over the visual cortex predictively encode visual information. Specifically, we examined whether their power carries information about the position of a moving stimulus, in a temporally predictive fashion. In two experiments (N = 32, 18 female; N = 34, 17 female), participants viewed an apparent-motion stimulus moving along a circular path while EEG was recorded. To investigate the encoding of stimulus-position information, we developed a method of deriving probabilistic spatial maps from oscillatory power estimates. With this method, we demonstrate that it is possible to reconstruct the trajectory of a moving stimulus from α/low-β oscillations, tracking its position even across unexpected motion reversals. We also show that future position representations are activated in the absence of direct visual input, demonstrating that temporally predictive mechanisms manifest in α/β band oscillations. In a second experiment, we replicate these findings and show that the encoding of information in this range is not driven by visual entrainment. By demonstrating that occipital α/β oscillations carry stimulus-related information, in a temporally predictive fashion, we provide empirical evidence of these rhythms as a spectral "fingerprint" of hierarchical predictive processing in the human visual system.SIGNIFICANCE STATEMENT "Hierarchical predictive coding" is a general model of sensory information processing in the brain. When in silico predictive coding models are constrained by neural transmission delays, their activity naturally oscillates in roughly the α range (∼8-12 Hz). Using time-resolved EEG decoding, we show that neural rhythms in this approximate range (α/low-β) over the human visual cortex predictively encode the position of a moving stimulus. From the amplitude of these oscillations, we are able to reconstruct the stimulus' trajectory, revealing signatures of temporally predictive processing. This provides direct neural evidence linking occipital α/β rhythms to predictive visual processing, supporting the emerging view of such oscillations as a potential spectral "fingerprint" of hierarchical predictive processing in the human visual system.

Recurrent predictive coding models for associative memory employing covariance learning.

The computational principles adopted by the hippocampus in associative memory (AM) tasks have been one of the most studied topics in computational and theoretical neuroscience. Recent theories suggested that AM and the predictive activities of the hippocampus could be described within a unitary account, and that predictive coding underlies the computations supporting AM in the hippocampus. Following this theory, a computational model based on classical hierarchical predictive networks was proposed and was shown to perform well in various AM tasks. However, this fully hierarchical model did not incorporate recurrent connections, an architectural component of the CA3 region of the hippocampus that is crucial for AM. This makes the structure of the model inconsistent with the known connectivity of CA3 and classical recurrent models such as Hopfield Networks, which learn the covariance of inputs through their recurrent connections to perform AM. Earlier PC models that learn the covariance information of inputs explicitly via recurrent connections seem to be a solution to these issues. Here, we show that although these models can perform AM, they do it in an implausible and numerically unstable way. Instead, we propose alternatives to these earlier covariance-learning predictive coding networks, which learn the covariance information implicitly and plausibly, and can use dendritic structures to encode prediction errors. We show analytically that our proposed models are perfectly equivalent to the earlier predictive coding model learning covariance explicitly, and encounter no numerical issues when performing AM tasks in practice. We further show that our models can be combined with hierarchical predictive coding networks to model the hippocampo-neocortical interactions. Our models provide a biologically plausible approach to modelling the hippocampal network, pointing to a potential computational mechanism during hippocampal memory formation and recall, which employs both predictive coding and covariance learning based on the recurrent network structure of the hippocampus.

Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.

Hierarchical predictive coding in distributed pain circuits.

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

Evidence of a predictive coding hierarchy in the human brain listening to speech

Considerable progress has recently been made in natural language processing: deep learning algorithms are increasingly able to generate, summarize, translate and classify texts. Yet, these language models still fail to match the language abilities of humans. Predictive coding theory offers a tentative explanation to this discrepancy: while language models are optimized to predict nearby words, the human brain would continuously predict a hierarchy of representations that spans multiple timescales. To test this hypothesis, we analysed the functional magnetic resonance imaging brain signals of 304 participants listening to short stories. First, we confirmed that the activations of modern language models linearly map onto the brain responses to speech. Second, we showed that enhancing these algorithms with predictions that span multiple timescales improves this brain mapping. Finally, we showed that these predictions are organized hierarchically: frontoparietal cortices predict higher-level, longer-range and more contextual representations than temporal cortices. Overall, these results strengthen the role of hierarchical predictive coding in language processing and illustrate how the synergy between neuroscience and artificial intelligence can unravel the computational bases of human cognition.

Neural Mechanisms and Psychology of Psychedelic Ego Dissolution.

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries-known as ego dissolution-surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A receptors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a disarming of ego resistance that increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging. SIGNIFICANCE STATEMENT: Classic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging findings by bridging the role between the 5-HT2A receptors and large-scale brain networks.

Predictive coding across the left fronto-temporal hierarchy during language comprehension.

We used magnetoencephalography (MEG) and event-related potentials (ERPs) to track the time-course and localization of evoked activity produced by expected, unexpected plausible, and implausible words during incremental language comprehension. We suggest that the full pattern of results can be explained within a hierarchical predictive coding framework in which increased evoked activity reflects the activation of residual information that was not already represented at a given level of the fronto-temporal hierarchy ("error" activity). Between 300 and 500ms, the three conditions produced progressively larger responses within left temporal cortex (lexico-semantic prediction error), whereas implausible inputs produced a selectively enhanced response within inferior frontal cortex (prediction error at the level of the event model). Between 600 and 1,000ms, unexpected plausible words activated left inferior frontal and middle temporal cortices (feedback activity that produced top-down error), whereas highly implausible inputs activated left inferior frontal cortex, posterior fusiform (unsuppressed orthographic prediction error/reprocessing), and medial temporal cortex (possibly supporting new learning). Therefore, predictive coding may provide a unifying theory that links language comprehension to other domains of cognition.

A Predictive Processing Model of Episodic Memory and Time Perception

Human perception and experience of time are strongly influenced by ongoing stimulation, memory of past experiences, and required task context. When paying attention to time, time experience seems to expand; when distracted, it seems to contract. When considering time based on memory, the experience may be different than what is in the moment, exemplified by sayings like "time flies when you're having fun." Experience of time also depends on the content of perceptual experience-rapidly changing or complex perceptual scenes seem longer in duration than less dynamic ones. The complexity of interactions among attention, memory, and perceptual stimulation is a likely reason that an overarching theory of time perception has been difficult to achieve. Here, we introduce a model of perceptual processing and episodic memory that makes use of hierarchical predictive coding, short-term plasticity, spatiotemporal attention, and episodic memory formation and recall, and apply this model to the problem of human time perception. In an experiment with approximately 13,000 human participants, we investigated the effects of memory, cognitive load, and stimulus content on duration reports of dynamic natural scenes up to about 1 minute long. Using our model to generate duration estimates, we compared human and model performance. Model-based estimates replicated key qualitative biases, including differences by cognitive load (attention), scene type (stimulation), and whether the judgment was made based on current or remembered experience (memory). Our work provides a comprehensive model of human time perception and a foundation for exploring the computational basis of episodic memory within a hierarchical predictive coding framework.

The sound of silence: Predictive error responses to unexpected sound omission in adults.

The human auditory system excels at detecting patterns needed for processing speech and music. According to predictive coding, the brain predicts incoming sounds, compares predictions to sensory input and generates a prediction error whenever a mismatch between the prediction and sensory input occurs. Predictive coding can be indexed in electroencephalography (EEG) with the mismatch negativity (MMN) and P3a, two components of event-related potentials (ERP) that are elicited by infrequent deviant sounds (e.g., differing in pitch, duration and loudness) in a stream of frequent sounds. If these components reflect prediction error, they should also be elicited by omitting an expected sound, but few studies have examined this. We compared ERPs elicited by infrequent randomly occurring omissions (unexpected silences) in tone sequences presented at two tones per second to ERPs elicited by frequent, regularly occurring omissions (expected silences) within a sequence of tones presented at one tone per second. We found that unexpected silences elicited significant MMN and P3a, although the magnitude of these components was quite small and variable. These results provide evidence for hierarchical predictive coding, indicating that the brain predicts silences and sounds.

Corticofugal regulation of predictive coding.

Sensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. Prediction error incrementally increases along the auditory hierarchy from the inferior colliculus (IC) to the auditory cortex (AC), suggesting that these regions may engage in hierarchical predictive coding. A potential substrate for top-down predictive cues is the massive set of descending projections from the AC to subcortical structures, although the role of this system in predictive processing has never been directly assessed. We tested the effect of optogenetic inactivation of the auditory cortico-collicular feedback in awake mice on responses of IC neurons to stimuli designed to test prediction error and repetition suppression. Inactivation of the cortico-collicular pathway led to a decrease in prediction error in IC. Repetition suppression was unaffected by cortico-collicular inactivation, suggesting that this metric may reflect fatigue of bottom-up sensory inputs rather than predictive processing. We also discovered populations of IC units that exhibit repetition enhancement, a sequential increase in firing with stimulus repetition. Cortico-collicular inactivation led to a decrease in repetition enhancement in the central nucleus of IC, suggesting that it is a top-down phenomenon. Negative prediction error, a stronger response to a tone in a predictable rather than unpredictable sequence, was suppressed in shell IC units during cortico-collicular inactivation. These changes in predictive coding metrics arose from bidirectional modulations in the response to the standard and deviant contexts, such that the units in IC responded more similarly to each context in the absence of cortical input. We also investigated how these metrics compare between the anesthetized and awake states by recording from the same units under both conditions. We found that metrics of predictive coding and deviance detection differ depending on the anesthetic state of the animal, with negative prediction error emerging in the central IC and repetition enhancement and prediction error being more prevalent in the absence of anesthesia. Overall, our results demonstrate that the AC provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

Constructing the hierarchy of predictive auditory sequences in the marmoset brain.

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, shorter-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, longer-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putative bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical top-down process of predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural networks and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

A new methodology for assessing human contributions to occurrences (MAHCO) in Air Traffic Management utilising a Bayesian hierarchical predictive coding approach to the brain, and the benefits for just culture

The traditional approaches to classifying Human Error have provided a strong and beneficial approach to understanding the roles of humans in safety occurrences. However, as the demand for human performance has increased and with the rise of more automation, the line between man and machine continues to become increasingly blurred. Traditional Human Error analysis techniques are no longer sufficient for extracting the most valuable learning data from occurrences. Furthermore, there is a continuing need to strengthen Safety and Just culture. One means to achieving this is through developing ever greater trust in the ability of organisations to accurately investigate and report occurrences. This paper outlines a new 10 step approach to investigating human contribution to occurrences which is based on a version of predictive situational awareness in the brain that is derived from hierarchical predictive coding approaches. Utilising statistically based induction, the steps allow investigators to make an objective reconstruction of sub-conscious human error analysis and integrate it with traditional subjective feedback on conscious decisions made. Examples of system data that can provide further evidence to support the reconstructions is also discussed. This approach has been tested on more than 50 human factors related occurrences in Air Traffic Control in 2021/2022 and the results are introduced. It is further argued that using a refined approach and educating operators on the approach can significantly benefit Just Culture and lead to increases in trust and frequency of reporting because it will allow for the removal of biases by individual investigators. Future research should focus on validating the approach against current methods and testing it for occurrences further away from front line operators such as in non-safety related decision making.

Predisposition to domain-wide maladaptive changes in predictive coding in auditory phantom perception

Tinnitus is hypothesised to be a predictive coding problem. Previous research indicates lower sensitivity to prediction errors (PEs) in tinnitus patients while processing auditory deviants corresponding to tinnitus-specific stimuli. However, based on research with patients with hallucinations and no psychosis we hypothesise tinnitus patients may be more sensitive to PEs produced by auditory stimuli that are not related to tinnitus characteristics. Specifically in patients with minimal to no hearing loss, we hypothesise a more top-down subtype of tinnitus that may be driven by maladaptive changes in an auditory predictive coding network. To test this, we use an auditory oddball paradigm with omission of global deviants, a measure that is previously shown to empirically characterise hierarchical prediction errors (PEs).We observe: (1) increased predictions characterised by increased pre-stimulus response and increased alpha connectivity between the parahippocampus, dorsal anterior cingulate cortex and parahippocampus, pregenual anterior cingulate cortex and posterior cingulate cortex; (2) increased PEs characterised by increased P300 amplitude and gamma activity and increased theta connectivity between auditory cortices, parahippocampus and dorsal anterior cingulate cortex in the tinnitus group; (3) increased overall feed-forward connectivity in theta from the auditory cortex and parahippocampus to the dorsal anterior cingulate cortex; (4) correlations of pre-stimulus theta activity to tinnitus loudness and alpha activity to tinnitus distress. These results provide empirical evidence of maladaptive changes in a hierarchical predictive coding network in a subgroup of tinnitus patients with minimal to no hearing loss. The changes in pre-stimulus activity and connectivity to non-tinnitus specific stimuli suggest that tinnitus patients not only produce strong predictions about upcoming stimuli but also may be predisposed to stimulus a-specific PEs in the auditory domain. Correlations with tinnitus-related characteristics may be a biomarker for maladaptive changes in auditory predictive coding.