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predictive coding and attention in developmental cognitive neuroscience and perspectives for neurodevelopmental disorders

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DEVELOPMENTAL COGNITIVE NEUROSCIENCE 2025年 72卷 101519页

作者： Marais, Anne-Lise Roche-Labarbe, Nadege Normandie Univ UNICAEN INSERM COMETEGIP CYCERON F-14000 Caen France

Sensory prediction and repetition suppression are closely related cognitive mechanisms that allow the brain to form predictions about the environment, and guide perception in synergy with attention. predictive coding is a theory of the fundamental role of predictive mechanisms in brain functions. Authors have proposed a central role of predictive impairments in autism and possibly other neurodevelopmental disorders. However, little is known about predictive mechanisms in typical development, and how they co-develop with attention. Here we review experimental support for predictive coding and its links with attention in healthy adults' brains, the first experimental works performed in typically developing children and infants, and theoretical accounts of neurodevelopmental disorders using a predictive coding framework. We propose future directions for predictive coding research in development. Finally, we describe the first predictive coding experiments in neonates and provide research perspectives for using this framework in searching for early markers of atypical neurodevelopment.

关键词： predictive coding Sensory prediction Attention Neurodevelopmental disorders Neonatal screening

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Design of Low-Complexity Quantized Compressive Sensing Using Measurement predictive coding

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第1期33卷 288-292页

作者： Konduru, Lakshmi Bhanuprakash Reddy Pudi, Vikramkumar Appina, Balasubramanyam Indian Inst Technol IIT Tirupati Dept Elect Engn Tirupati 517619 Andhra Pradesh India Indian Inst Technol Indore Dept Elect Engn Indore 453552 Madhya Pradesh India

Block-based compressive sensing (BCS) has evolved as a promising method for smart devices with limited bandwidth and computing capabilities, striking a balance between image/video quality and transmission efficiency. Despite its advantages, BCS falls short in reducing bitrate compared with traditional acquisition systems, because it increases the number of bits per measurement, which leads to high storage and transmission costs. In this context, we propose a measurement predictive coding (MPC) along with the quantization method in integration with BCS named BCS-MPC;here, we have performed the quantization with bit shifts only instead of binary division. The proposed method reduces the number of bits per compressive sensing (CS) measurement as well as the transmission of the quantization step size. Furthermore, it reduces the latency and hardware resources. The proposed method improved on average +3.44 to +8.28 dB in PSNR over the current works. From the synthesis results, the proposed BCS-MPC method requires 26.11\% , 18.89% , and 82.53% less area, power, and delay over the existing work. We have achieved a reduction in delay with bit-shift operations.

关键词： Image coding Quantization (signal) Hardware Q measurement predictive coding Current measurement Image reconstruction Compressive sensing (CS) intraprediction intrapredictive coding JPEG compression predictive coding

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PrediRep: Modeling hierarchical predictive coding with an unsupervised deep learning network

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NEURAL NETWORKS 2025年 185卷 107246页

作者： Hashim, Ibrahim C. Senden, Mario Goebel, Rainer Maastricht Univ Fac Psychol & Neurosci Dept Cognit Neurosci Maastricht Netherlands Maastricht Univ Fac Psychol & Neurosci Maastricht Brain Imaging Ctr Maastricht Netherlands

Hierarchical predictive coding (hPC) provides a compelling framework for understanding how the cortex predicts future sensory inputs by minimizing prediction errors through an internal generative model of the external world. Existing deep learning models inspired by hPC incorporate architectural choices that deviate from core hPC principles, potentially limiting their utility for neuroscientific investigations. We introduce PrediRep (Predicting Representations), a novel deep learning network that adheres more closely to architectural principles of hPC. We validate PrediRep by comparing its functional alignment with hPC to that of existing models after being trained on a next-frame prediction task. Our findings demonstrate that PrediRep, particularly when trained with an all-level loss function (PrediRepAll), exhibits high functional alignment with hPC. In contrast to other contemporary deep learning networks inspired by hPC, it consistently processes input- relevant information at higher hierarchical levels and maintains active representations and accurate predictions across all hierarchical levels. Although PrediRep was designed primarily to serve as a model suitable for neuroscientific research rather than to optimize performance, it nevertheless achieves competitive performance in next-frame prediction while utilizing significantly fewer trainable parameters than alternative models. Our results underscore that even minor architectural deviations from neuroscientific theories like hPC can lead to significant functional discrepancies. By faithfully adhering to hPC principles, PrediRep provides a more accurate tool for in silico exploration of cortical phenomena. PrediRep's lightweight and biologically plausible design makes it well-suited for future studies aiming to investigate the neural underpinnings of predictive coding and to derive empirically testable predictions.

关键词： predictive coding Deep learning Temporal prediction Unsupervised learning predictive processing

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The Mechanisms of Persisting Disability in Schizophrenia: Imprecise predictive coding via Corticostriatothalamic-Cortical Loop Dysfunction

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BIOLOGICAL PSYCHIATRY 2025年第2期97卷 109-116页

作者： Liddle, Peter F. Sami, Musa B. Univ Nottingham Inst Mental Hlth Nottingham England

Persisting symptoms and disability remain a problem for an appreciable proportion of people with schizophrenia despite treatment with antipsychotic medication. Improving outcomes requires an understanding of the nature and mechanisms of the pathological processes underlying persistence. Classical features of schizophrenia, which include disorganization and impoverishment of mental activity, are well-recognized early clinical features that predict poor long-term outcome. Substantial evidence indicates that these features reflect imprecise predictive coding. predictive coding provides an overarching framework for understanding efficient functioning of the nervous system. Imprecise predictive coding also has the potential to precipitate acute psychosis characterized by reality distortion (delusions and hallucinations) at times of stress. On the other hand, substantial evidence indicates that persistent reality distortion itself gives rise to poor occupational and social function in the long term. Furthermore, abuse of psychotomimetic drugs, which exacerbate reality distortion, contributes to poor long-term outcome in schizophrenia. Neural circuits involved in modulating volitional acts are well understood to be implicated in addiction. Plastic changes in these circuits may account for the association between psychotomimetic drug abuse and poor outcomes in schizophrenia. We propose a mechanistic model according to which unbalanced inputs to the corpus striatum disturb the precision of subcortical modulation of cortical activity supporting volitional action. This model accounts for the evidence that early classical symptoms predict poor outcome, while in some circumstances, persistent reality distortion also predicts poor outcome. This model has implications for the development of novel treatments that address the risk of persisting symptoms and disabilities in schizophrenia.

关键词： Disorganization predictive coding Psychomotor poverty Psychotomimetic drugs Reality distortion Schizophrenia

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Further N-Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2025年 19卷 1551960页

作者： Edwards, Darren J. Swansea Univ Dept Publ Hlth Swansea Wales

Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this paper introduces the functional contextual N-Frame model, a novel framework integrating predictive coding, quantum Bayesian (QBism), and evolutionary dynamics. This comprehensive model explicates how conscious observers, whether human or artificial, should update beliefs and interact within a quantum cognitive system. It provides a dynamic account of belief evolution through the interplay of internal observer states and external stimuli. By modeling decision-making fallacies such as the conjunction fallacy and conscious intent collapse experiments within this quantum probabilistic framework, the N-Frame model establishes structural and functional equivalence between cognitive processes identified within these experiments and traditional quantum mechanics (QM). It is hypothesized that consciousness serves as an active participant in wavefunction collapse (or actualization of the physical definite states we see), bridging quantum potentiality and classical outcomes via internal observer states and contextual interactions via a self-referential loop. This framework formalizes decision-making processes within a Hilbert space, mapping cognitive states to quantum operators and contextual dependencies, and demonstrates structural and functional equivalence between cognitive and quantum systems in order to address the measurement problem. Furthermore, the model extends to testable predictions about AI consciousness by specifying informational boundaries, contextual parameters, and a

关键词： predictive coding functional contextualism N-Frame quantum mechanics artificial intelligence

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Emergence of Hemispheric Asymmetries and predictive coding in the Neural Mechanism of Speech Perception 19th

Emergence of Hemispheric Asymmetries and Predictive Coding i...

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19th National Conference on Man-Machine Speech Communication

作者： Zhao, Bin Dang, Jianwu Li, Aijun Chinese Acad Social Sci Inst Linguist Beijing 100732 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

ISBN: (纸本)9789819610440;9789819610457

This study reveals the neural mechanisms underlying speech perception, particularly highlighting the hemispheric asymmetries and predictive coding mechanisms during the processing of spoken word stimuli compared to white noise. By employing electroencephalography (EEG), we conducted a two-dimensional (2D) ERP-image analysis to elucidate cortical responses on a single-trial basis, and utilized a multivariate autoregressive (MVAR) model with the renormalized partial directed coherence (rPDC) method, to construct effective brain network connections. Discriminative features of the EEG responses to word and white noise were identified in the primary auditory regions with a hemispheric bias and the higher cognitive regions with an early regulation. These findings corroborate the established left-hemisphere dominance in language processing and endorse the idea that the brain's engagement with auditory processing is an active process, involving the prediction of the upcoming input via an inhibitory copy along the top-down pathway, where the brain generates expectations based on internal linguistic knowledge to guide and constrain the interpretation of incoming sensory data.

关键词： speech perception EEG techniques predictive coding hemispheric asymmetries brain network dynamics

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EEG-indicators of predictive coding Impaired in Psychiatric Disorders (a Pilot Study of Auditory Evoked Potentials)

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JOURNAL OF EVOLUTIONARY BIOCHEMISTRY AND PHYSIOLOGY 2025年第1期61卷 226-240页

作者： Telesheva, K. Yu. Korobkova, A. A. Serbsky Natl Med Res Ctr Psychiat & Narcol Moscow Russia

The event-related potentials mismatch negativity (MMN) and P300 in the odd ball paradigm (standard and deviant stimuli presentation) reflect the stages of error detection and prediction correction signal in the processes of predictive coding. Changes in these components are a transdiagnostic indicator of disturbances in these processes, which may be the cause of psychopathologic symptomatology. The study includes active (with attention directed to stimuli) and ignore (without attention to stimuli) conditions of the odd ball paradigm with auditory stimulation, which complements existing data on component characteristics. The study also included psychological testing to assess the level of self-control, impulsivity, and anxiety, and their relationship to neurophysiologic measures of predictive coding. Individuals with personality disorders (n = 11) and schizophrenia (n = 11) were examined in comparison with a mental health group (n = 11). A decrease in the amplitude of the MMN in persons with schizophrenia in the active condition, and a decrease in the amplitude of the P300 in both groups were revealed. The MMN amplitude focus in the control group in the active condition is in the vertex area, in the ignore condition-in the frontal area. In individuals with personality disorders and schizophrenia a shift of the focus is noted. In the active condition, two P300 focuses were detected-in parietal and frontal areas, while in the ignore condition the focus in parietal area was retained only in the control group. Amplitudes in the control group were related to self-control, in individuals with personality disorders latent periods were related to self-control/impulsivity, and in individuals with schizophrenia a relationship of social self-control with the latent period of the P300 was revealed. The results complement the understanding of the brain mechanisms of the predictive coding in relation to attention and its impairment in psychiatric disorders.

关键词： event-related potentials mismatch negativity P300 predictive coding personality disorders schizophrenia

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Differing Pattern of Mismatch Negativity Responses in Clinical and Nonclinical Voice Hearers Challenge predictive coding Accounts of Psychosis

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BIOLOGICAL PSYCHIATRY: GLOBAL OPEN SCIENCE 2025年第1期5卷 100394-100394页

作者： Erickson, Molly A. Bansal, Sonia Li, Charlotte Waltz, James Corlett, Philip Gold, James Univ Chicago Med Ctr Dept Psychiat & Behav Neurosci Chicago IL 60637 USA Univ Maryland Sch Med Maryland Psychiat Res Ctr Baltimore MD USA Yale Sch Med Dept Psychiat New Haven CT USA

BACKGROUND: Among people with schizophrenia (PSZ), reduced mismatch negativity (MMN) is conceptualized as evidence of disrupted prediction error signaling that underlies positive symptoms. However, this conceptualization has been challenged by observations that MMN and positive symptoms are often uncorrelated. In the current study, we tested the hypothesis that reduced MMN is associated with the presence of hallucinations and delusions specifically rather than the presence of a psychiatric illness. A second aim was to determine whether the strength of the association with positive symptoms increases for indices that reflect predictions at higher levels of abstraction. METHODS: Fifty-six PSZ, 34 nonclinical voice hearers, and 48 healthy comparison subjects (HCs) completed 2 MMN paradigms: one with a simple duration deviant type, and one with a higher-level, pattern-violation deviant type. We also measured the repetition positivity, which reflects the formation of auditory memory traces. RESULTS: We observed that although PSZ exhibited the expected pattern of significantly reduced duration MMN and reduced pattern-violation MMN at the trend level compared with HCs, nonclinical voice hearers exhibited a pattern of duration MMN and pattern-violation MMN amplitude that was statistically similar to that of HCs (ps > .64). Similarly, PSZ exhibited a significantly reduced repetition positivity slope compared with HCs in the duration condition and a trend-level reduction compared with HCs in the pattern-violation condition. Nonclinical voice hearers did not differ from either group in repetition positivity slope in either condition. CONCLUSIONS: These results indicate that the MMN as a prediction error signal does not reflect processes relevant for the manifestation of hallucinations and delusions.

关键词： Delusions Hallucinations Hierarchical gaussian filtering Mismatch negativity predictive coding Psychosis

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An implemented predictive coding model of lexico-semantic processing explains the dynamics of univariate and multivariate activity within the left ventromedial temporal lobe during reading comprehension

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NEUROIMAGE 2025年 308卷 120977-120977页

作者： Wang, Lin Eddine, Samer Nour Brothers, Trevor Jensen, Ole Kuperberg, Gina R. Harvard Med Sch Massachusetts Gen Hosp Dept Psychiat Charlestown MA 02129 USA Harvard Med Sch Massachusetts Gen Hosp Athinoula A Martinos Ctr Biomed Imaging Charlestown MA 02129 USA Tufts Univ Dept Psychol Medford MA 02155 USA North Carolina Agr & Tech State Univ Greensboro NC 27411 USA Univ Oxford Dept Expt Psychol Oxford OX2 6GG England Univ Oxford Oxford Ctr Human Brain Act Wellcome Ctr Integrat Neuroimaging Dept Psychiat Oxford OX3 7JX England

During language comprehension, the larger neural response to unexpected versus expected inputs is often taken as evidence for predictive coding-a specific computational architecture and optimization algorithm proposed to approximate probabilistic inference in the brain. However, other predictive processing frameworks can also account for this effect, leaving the unique claims of predictive coding untested. In this study, we used MEG to examine both univariate and multivariate neural activity in response to expected and unexpected inputs during word-by-word reading comprehension. We further simulated this activity using an implemented predictive coding model that infers the meaning of words from their orthographic form. Consistent with previous findings, the univariate analysis showed that, between 300 and 500 ms, unexpected words produced a larger evoked response than expected words within a left ventromedial temporal region that supports the mapping of orthographic word-forms onto lexical and conceptual representations. Our model explained this larger evoked response as the enhanced lexico-semantic prediction error produced when prior top-down predictions failed to suppress activity within lexical and semantic "error units". Critically, our simulations showed that despite producing minimal prediction error, expected inputs nonetheless reinstated top-down predictions within the model's lexical and semantic "state" units. Two types of multivariate analyses provided evidence for this functional distinction between state and error units within the ventromedial temporal region. First, within each trial, the same individual voxels that produced a larger response to unexpected inputs between 300 and 500 ms produced unique temporal patterns to expected inputs that resembled the patterns produced within a pre- activation time window. Second, across trials, and again within the same 300-500 ms time window and left ventromedial temporal region, pairs of expected words produce

关键词： predictive coding Language comprehension Modeling MEG N400 RSA

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Efficient Learning in predictive coding Networks Using Global Error Signals

Efficient Learning in Predictive Coding Networks Using Globa...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Soha Lee Hyeyoung Park School of Electrical Engineering and Computer Science Kyungpook National University Daegu KOREA

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

The error backpropagation is a representative learning algorithm widely adopted across deep learning networks. Despite its success in achieving high learning performance and generalization, error backpropagation has been criticized for its biological implausibility. One of its primary limitations lies in the weight transport problem, where, unlike the human brain, error backpropagation requires the same weights used in forward propagation to be accessible in the backward propagation. In addition, the sequential calculation of error backpropagation restricts parallelization that results in reduced efficiency. To solve these problems, predictive coding networks have emerged as an alternative. Inspired by predictive coding in computational neuroscience, it offers a biologically plausible learning framework. However, as network depth increases, predictive coding can suffer from significant computational costs, which decreases efficiency. In this paper, we propose an approach that enhances the efficiency of predictive coding by using global error signals. We also evaluate the effectiveness of our method on benchmark datasets, demonstrating improved learning performance and computational efficiency.

关键词： Backpropagation Training Deep learning Scalability Natural languages Learning (artificial intelligence) predictive coding Biology Computational efficiency Signal resolution

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