Abstract Processing of an obligatory phonotactic restriction outside the focus of the participants' attention was investigated by means of ERPs using (reversed) experimental oddball blocks. Dorsal fricative assimilation (DFA) is a phonotactic constraint in German grammar that is violated in *[ɛx] but not in [ɔx], [ɛ∫], and [ɔ∫]. These stimulus sequences engage the auditory deviance detection mechanism as reflected by the MMN component of the ERP. In Experiment 1 (n = 16), stimuli were contrasted pairwise such that they shared the initial vowel but differed with regard to the fricative. Phonotactically ill-formed deviants elicited stronger MMN responses than well-formed deviants that differed acoustically in the same way from the standard stimulation but did not contain a phonotactic violation. In Experiment 2 (n = 16), stimuli were contrasted such that they differed with regard to the vowel but shared the fricative. MMN was elicited by the vowel change. An additional, later MMN response was observed for the phonotactically ill-formed syllable only. This MMN cannot be attributed to any phonetic or segmental difference between standard and deviant. These findings suggest that implicit phonotactic knowledge is activated and applied in preattentive speech processing.

The neural mechanisms through which individuals lose sensory awareness of their environment during anesthesia remains poorly understood despite being of vital importance to the field. Prior research has not distinguished between sensory awareness of the environment (connectedness) and consciousness itself. In the current study, we investigated the neural correlates of sensory awareness by contrasting neural responses to an auditory roving oddball paradigm during consciousness with sensory awareness (connected consciousness) and consciousness without sensory awareness (disconnected consciousness). These states were captured using a serial awakening paradigm with the sedative alpha2 adrenergic agonist dexmedetomidine, chosen based on our published hypothesis that suppression of noradrenaline signaling is key to induce a state of sensory disconnection. High-density electroencephalography was recorded from 18 human subjects before and after administration of dexmedetomidine. By investigating event-related potentials and taking advantage of advances in Dynamic Causal Modeling (DCM), we assessed alterations in effective connectivity between nodes of a previously established auditory processing network. We found that during disconnected consciousness, the scalp-level response to standard tones produced a P3 response that was absent during connected consciousness. This P3 response resembled the response to oddball tones seen in connected consciousness. DCM showed that disconnection produced increases in standard tone feedback signaling throughout the auditory network. Simulation analyses showed that these changes in connectivity, most notably the increase in feedback from right superior temporal gyrus to right A1, can explain the new P3 response. Together these findings show that during disconnected consciousness there is a disruption of normal predictive coding processes, so that all incoming auditory stimuli become similarly surprising.

Can brief training on novel grammatical morphemes influence visual processing of nonlinguistic stimuli? If so, how deep is this effect? Here, an experimental group learned two novel morphemes highlighting the familiar concept of transitivity in sentences; a control group was exposed to the same input but with the novel morphemes used interchangeably. Subsequently, both groups performed two visual oddball tasks with nonlinguistic motion events. In the first (attentional) oddball task, relative to the control group, the experimental group showed decreased attention (P300) to infrequent changes in the morpheme‐irrelevant dimension (shape) but not the morpheme‐relevant dimension (motion transitivity); in the second (preattentive) oddball task, they showed enhanced preattentive responses (N1/visual mismatch negativity) to infrequent changes in motion transitivity but not shape. Our findings show that increasing attention to preexisting concepts in sentences through brief training on novel grammatical morphemes can influence both attentional and preattentive visual processing.

<h4>Introduction</h4>The clinical assessment of patients with Disorders of Consciousness (DoC), ranging from the Vegetative State (VS/UWS) to the Minimally Conscious State (MCS), remains a significant challenge in neurology. Gold-standard behavioral tools are prone to high misdiagnosis rates because they depend on overt motor responses, which may be masked by physical impairments. Consequently, there is an urgent need for objective neurophysiological biomarkers to identify residual awareness. Predictive Processing (PP) is a leading theory that views the brain as a hierarchical inference engine. Under this framework, the brain minimizes "prediction errors" between internal generative models and sensory inputs. Neural signatures of these errors, such as the Mismatch Negativity (MMN), provide a window into the brain's automatic modeling of environmental regularities, serving as a proxy for conscious processing.<h4>Objective</h4>This systematic review aims to identify and appraise peer-reviewed studies from the past 15 years that apply computational PP models to non-invasive brain signals in DoC patients. It synthesizes evidence for their diagnostic and prognostic utility and identifies methodological hurdles to clinical translation.<h4>Methods</h4>A systematic synthesis was conducted on 30 peer-reviewed studies. Data regarding population demographics (total <i>N</i>≈2045), paradigms, and computational methods, including multivariate pattern analysis and deep learning, were extracted and appraised.<h4>Results</h4>The evidence reveals a transition from simple waveform averaging to high-dimensional decoding of hierarchical prediction errors. Global information-sharing markers effectively distinguish conscious states, while the temporal progression of prediction error signatures in the early stages of coma demonstrates high specificity for predicting awakening.<h4>Conclusion</h4>Computational PP models offer a transformative path toward reducing misdiagnosis. Future resear Under this framework, the brain minimizes “prediction errors” between internal generative models and sensory inputs. Neural signatures of these errors, such as the Mismatch Negativity (MMN), provide a window into the brain's automatic modeling of environmental regularities, serving as a proxy for conscious processing. Objective This systematic review aims to identify and appraise peer-reviewed studies from the past 15 years that apply computational PP models to non-invasive brain signals in DoC patients. It synthesizes evidence for their diagnostic and prognostic utility and identifies methodological hurdles to clinical translation. Current gold-standard assessments rely on behavioral observations, which serve as unreliable proxies for internal awareness ( Giacino et al., 2014 ). Diagnostic error is frequent, with consensus-based clinical evaluations misdiagnosing approximately 40% of patients as being in a Vegetative State (VS) when standardized assessment using the Coma Recovery Scale-Revised (CRS-R) reveals residual conscious awareness ( Schnakers et al., 2009 Neural signatures of these processes, such as the Mismatch Negativity (MMN), provide a window into residual brain function even in the absence of attention or overt behavior ( Tivadar et al., 2021 ). Evidence suggests that the integrity of these predictive mechanisms is closely linked to return of awareness and residual consciousness ( Tivadar et al., 2021 ). This systematic review aims to appraise 15 years of literature applying PP-based computational models to non-invasive signals in DoC, evaluating their efficacy in clinical diagnosis and outcome prediction. The studies reviewed employ three main classes of experimental paradigms. The query combined keywords and concepts from three domains, namely, Disorders of Consciousness : “vegetative state”, “unresponsive wakefulness syndrome (UWS)”, “minimally conscious state (MCS)”, “coma”; Predictive Processing : “predictive coding”, “active inference”, “free-energy”, “hierarchical inference”, “surprise”; and Methods and Modalities : “Dynamic Causal Modeling (DCM)”, “Hierarchical Gaussian Filter (HGF)”, “Mismatch Negativity (MMN)”, “EEG”, “MEG”, “machine learning”. To ensure that no studies were missed, a search through the reference lists of all included studies was also performed. The performance was subsequently quantified using the area under the receiver operating characteristic curve (AUC). Results showed that local novelty decoding (automatic mismatch response) was robust across all states (mean AUC ≈55.3–60%) and not significantly different between VS and MCS patients, whereas global novelty decoding (associated with conscious processing) was markedly reduced in VS patients (AUC = 50.2%, not significant) compared to MCS (AUC = 51.7%, p < 0.01) and CS patients (AUC = 56.2%, p < 0.001). Within this class, microstate-based MMN approaches ( Zhang et al., 2023 ; Li et al., 2025 ) achieve the highest diagnostic accuracy (AUC 0.89–0.92), while prognostic performance using MMN progression shows excellent specificity but variable sensitivity ( Rossetti et al., 2014 ; Tzovara et al., 2016 ). Notably, studies employing the Local-Global paradigm report more modest diagnostic performance (AUC 0.50–0.57 for global novelty) but higher theoretical specificity for conscious processing ( King et al., 2013 ). The appraisal of Event-Related Potentials, specifically through the Local-Global and Mismatch Negativity paradigms, has shown that the brain's ability to generate and update internal models of sensory regularities is a hallmark of conscious awareness. The clinical utility of these frameworks is evidenced by their high diagnostic and prognostic performance. Computational signatures of information integration, such as weighted symbolic mutual information, and the temporal progression of prediction error signals have provided clinicians with biomarkers that achieve near-perfect specificity in predicting awakening from coma.

Mismatch negativity (MMN) is an event-related potential component automatically elicited by events that violate predictions based on prior events. To elicit this component, researchers use stimulus repetition to induce predictions, and the MMN is obtained by subtracting the brain response to rare or unpredicted stimuli from that of frequent stimuli. Under the Predictive Processing framework, one increasingly popular interpretation of the mismatch response postulates that MMN represents a prediction error. In this context, the reduced MMN amplitude to auditory stimuli has been considered a potential biomarker of Schizophrenia, representing a reduced prediction error and the inability to update the mental model of the world based on the sensory signals. It is unclear, however, whether this amplitude reduction is specific for auditory events or if the visual MMN reveals a similar pattern in schizophrenia spectrum disorder. This review and meta-analysis aimed to summarise the available literature on the vMMN in schizophrenia. A systematic literature search resulted in 10 eligible studies that resulted in a combined effect size of g = -.63, CI [-.86, -.41], reflecting lower vMMN amplitudes in patients. These results are in line with the findings in the auditory domain. This component offers certain advantages, such as less susceptibility to overlap with components generated by attentional demands. Future studies should use vMMN to explore abnormalities in the Predictive Processing framework in different stages and groups of the SSD and increase the knowledge in the search for biomarkers in schizophrenia.

Somatosensory mismatch negativity (sMMN) constitutes an electrophysiological marker that initially reflects preattentional processing and subsequently indexes automatic somatosensory deviance detection. While its application in adult populations is gradually expanding, the establishment of a standardized and reproducible methodology for eliciting and analyzing sMMN in pediatric populations remains uncertain. To determine whether the published literature provides a clear, consistent, and standardized methodology for sMMN assessment in individuals under 18 years. A search was conducted in PubMed (11), Scopus (6), Web of Science (6), DOAJ (1), Europe PMC (6), Embase (0), ClinicalKey (0), Cochrane Library (0) and ClinicalTrials.gov (0) database from inception to 18 August 2025. Eligible studies included research assessing sMMN using somatosensory oddball paradigms in participants <18 years. Inclusion criteria (participants younger than 18 years, the use of the oddball paradigm to evaluate somatosensory mismatch negativity, and clinical reports, single-case reports or experimental studies including both typically developing children and children with neurological conditions, published in English), and exclusion criteria (exclusivity for adult participants, and narrative reviews or editorials) were defined a priori, as well as the screening procedures and quality assessment methods. Two reviewers independently performed study selection and data extraction, with a third reviewer resolving disagreements. Risk of bias was assessed using the MMAT (mixed methods appraisal tool). Due to substantial heterogeneity in paradigms and outcome reporting, results were synthesized narratively. Four studies met inclusion criteria. Methodological diversity was pronounced across everything except task type that was passive. There is not a consensus regarding stimulation parameters. Risk of bias assessment revealed frequent concerns related to incomplete reporting and variability in analytic choices. The small number of studies, inconsistent methodological reporting, and absence of harmonized protocols limited comparability. Current evidence does not support the existence of a standardized methodology for assessing sMMN in children. Future studies should adopt harmonized stimulation paradigms and transparent, reproducible reporting standards to enable cross-study comparability and clinical translation.

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major