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Words and the World: predictive coding and the Language-Perception-Cognition Interface

CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2015年第4期24卷 279-284页

作者： Lupyan, Gary Clark, Andy Univ Wisconsin Dept Psychol Madison WI 53706 USA Univ Edinburgh Sch Philosophy Psychol & Language Sci Edinburgh EH8 9YL Midlothian Scotland

Can what we know change what we see? Does language affect cognition and perception? The last few years have seen increased attention to these seemingly disparate questions, but with little theoretical advance. We argue that substantial clarity can be gained by considering these questions through the lens of predictive processing, a framework in which mental representationsfrom the perceptual to the cognitivereflect an interplay between downward-flowing predictions and upward-flowing sensory signals. This framework provides a parsimonious account of how (and when) what we know ought to change what we see and helps us understand how a putatively high-level trait such as language can impact putatively low-level processes such as perception. Within this framework, language begins to take on a surprisingly central role in cognition by providing a uniquely focused and flexible means of constructing predictions against which sensory signals can be evaluated. predictive processing thus provides a plausible mechanism for many of the reported effects of language on perception, thought, and action, and new insights on how and when speakers of different languages construct the same reality in alternate ways.

关键词： perception language top-down effects predictive coding attention language and thought

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AnoPCN: Video Anomaly Detection via Deep predictive coding Network 19

AnoPCN: Video Anomaly Detection via Deep Predictive Coding N...

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27th ACM International Conference on Multimedia (MM)

作者： Ye, Muchao Peng, Xiaojiang Gan, Weihao Wu, Wei Qiao, Yu Chinese Acad Sci Shenzhen Key Lab CVPR Beijing Peoples R China Chinese Acad Sci SIAT SIAT SenseTime Joint Lab Beijing Peoples R China SenseTime Grp Ltd Beijing Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781450368896

Video anomaly detection is a challenging problem due to the ambiguity and complexity of how anomalies are defined. Recent approaches for this task mainly utilize deep reconstruction methods and deep prediction ones, but their performances suffer when they cannot guarantee either higher reconstruction errors for abnormal events or lower prediction errors for normal events. Inspired by the predictive coding mechanism explaining how brains detect events violating regularities, we address the Anomaly detection problem with a novel deep predictive coding Network, termed as AnoPCN, which consists of a predictive coding Module (PCM) and an Error Refinement Module (ERM). Specifically, PCM is designed as a convolutional recurrent neural network with feedback connections carrying frame predictions and feedforward connections carrying prediction errors. By using motion information explicitly, PCM yields better prediction results. To further solve the problem of narrow regularity score gaps in deep reconstruction methods, we decompose reconstruction into prediction and refinement, introducing ERM to reconstruct current prediction error and refine the coarse prediction. AnoPCN unifies reconstruction and prediction methods in an end-to-end framework, and it achieves state-of-the-art performance with better prediction results and larger regularity score gaps on three benchmark datasets including ShanghaiTech Campus, CUHK Avenue, and UCSD Ped2.

关键词： video anomaly detection video generation predictive coding

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Empirical Evaluations of Seed Set Selection Strategies for predictive coding

Empirical Evaluations of Seed Set Selection Strategies for P...

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IEEE International Conference on Big Data (Big Data)

作者： Mahoney, Christian J. Huber-Fliflet, Nathaniel Jensen, Katie Zhao, Haozhen Neary, Robert Ye, Shi Cleary Gottlieb Steen & Hamilton LLP E Discovery Washington DC 20037 USA Ankura Consulting Grp LLC Data & Technol Washington DC USA Ankura Consulting Grp LLC Data & Technol New York NY USA

ISBN: (纸本)9781538650356

Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the training document selection strategy - in particular, the strategy used to select the seed set, or the set of documents an attorney reviews first to establish an initial model. Since there is limited research on this important component of predictive coding, the authors of this paper set out to identify strategies that consistently perform well. Our research demonstrated that the seed set selection strategy can have a significant impact on the precision of a predictive model. Enabling attorneys with the results of this study will allow them to initiate the most effective predictive modeling process to comb through the terabytes of data typically present in modern litigation. This study used documents from four actual legal cases to evaluate eight different seed set selection strategies. Attorneys can use the results contained within this paper to enhance their approach to predictive coding.

关键词： predictive coding technology assisted review electronic discovery ediscovery e-discovery TAR CAL Seed Set

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A Feasibility Experiment on the Application of predictive coding to Instant Messaging Corpora 5

A Feasibility Experiment on the Application of Predictive Co...

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IEEE International Conference on Big Data (IEEE Big Data)

作者： Schoinas, Thanasis Qadir, Ghulam Navigant Consulting Europe Ltd Legal Technol Solut London England

ISBN: (纸本)9781538627150

predictive coding, the term used in the legal industry for document classification using machine learning, presents additional challenges when the dataset comprises instant messages, due to their informal nature and smaller sizes. In this paper, we exploit a data management workflow to group messages into day chats, followed by feature selection and a logistic regression classifier to provide an economically feasible predictive coding solution. We also improve the solution's baseline model performance by dimensionality reduction, with focus on quantitative features. We test our methodology on an Instant Bloomberg dataset, rich in quantitative information. In parallel, we provide an example of the cost savings of our approach.

关键词： predictive coding technology assisted review electronic discovery instant bloomberg dimensionality reduction

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Etude d'un algorithme hiérarchique de codage épars et prédictif: vers un modèle bio-inspiré de la perception visuelle: Sparse deep predictive coding: a bio-inspired model of visual perception

Etude d'un algorithme hiérarchique de codage épars et pré...

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作者： Boutin, Victor Aix Marseille Université

学位级别：博士

La représentation concise et efficace de l'information est un problème qui occupe une place centrale dans l'apprentissage machine. Le cerveau, et plus particulièrement le cortex visuel, ont depuis longtemps trouvé des solutions performantes et robustes afin de résoudre un tel problème. A l'échelle locale, le codage épars est l'un des mécanismes les plus prometteurs pour modéliser le traitement de l'information au sein des populations de neurones dans le cortex visuel. A l'échelle structurelle, le codage prédictif suggère que les signaux descendants observés dans le cortex visuel modulent l'activité des neurones pour inclure des détails contextuels au flux d'information ascendant. Cette thèse propose de combiner codage épars et codage prédictif au sein d'un modèle hiérarchique et convolutif. D'un point de vue computationnel, nous démontrons que les connections descendantes, introduites par le codage prédictif, permettent une convergence meilleure et plus rapide du modèle. De plus, nous analysons les effets des connections descendantes sur l'organisation des populations de neurones, ainsi que leurs conséquences sur la manière dont notre algorithme se représente les images. Nous montrons que les connections descendantes réorganisent les champs d'association de neurones dans V1 afin de permettre une meilleure intégration des contours. En outre, nous observons que ces connections permettent une meilleure reconstruction des images bruitées. Nos résultats suggèrent que l'inspiration des neurosciences fournit un cadre prometteur afin de développer des algorithmes de vision artificielles plus performants et plus robustes. Building models to efficiently represent images is a central and difficult problem in the machine learning community. The neuroscientific study of the early visual cortical areas is a great source of inspiration to find economical and robust solutions. For instance, Sparse coding (SC) is one of the most successful frameworks to model neural computation at t

关键词： Codage épars Codage prédictif Modèle hiérarchique Réseaux récurrents convolutifs Bio-Inspiration Perception visuelle Sparse coding predictive coding Hierarchical model Recurrent convolutional network Bio-Inspiration Visual perception

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The selective updating of working memory: a predictive coding account

The selective updating of working memory: a predictive codin...

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作者： Yen Yu University of London

学位级别：博士

Goal-relevant information maintained in working memory is remarkably robust and resistant to distractions. However, our nervous system is endowed with exceptional flexibility; therefore such information can be updated almost effortlessly. A scenario – not uncommon in our daily life – is that selective maintaining and updating information can be achieved concurrently. This is an intriguing example of how our brain balances stability and flexibility, when organising its knowledge. A possibility – one may draw upon to understand this capacity – is that working memory is represented as beliefs, or its probability densities, which are updated in a context-sensitive manner. This means one could treat working memory in the same way as perception – i.e., memories are based on inferring the cause of sensations, except that the time scale ranges from an instant to prolonged anticipation. In this setting, working memory is susceptible to prior information encoded in the brain’s model of its world. This thesis aimed to establish an interpretation of working memory processing that rests on the (generalised) predictive coding framework, or hierarchical inference in the brain. Specifically, the main question it asked was how anticipation modulates working memory updating (or maintenance). A novel working memory updating task was designed in this regard. Blood-oxygen-level dependent (BOLD) imaging, machine learning, and dynamic causal modelling (DCM) were applied to identify the neural correlates of anticipation and the violation of anticipation, as well as the causal structure generating these neural correlates. Anticipation induced neural activity in the dopaminergic midbrain and the striatum. Whereas, the fronto-parietal and cingulo-operculum network were implicated when an anticipated update was omitted, and the midbrain, occipital cortices, and cerebellum when an update was unexpected. DCM revealed that anticipation is a modulation of backward connections, whilst the associate

关键词： working memory updating predictive coding fMRI dynamic causal modelling MVPA

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Scene Text Recognition with Self-supervised Contrastive predictive coding 26

Scene Text Recognition with Self-supervised Contrastive Pred...

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Jiang, Xinzhe Zhang, Jianshu Du, Jun Zhang, Zhenrong Wu, Jiajia Univ Sci & Technol China Natl Engn Res Ctr Speech & Language Informat Proc Hefei Anhui Peoples R China iFLYTEK Res Hefei Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Self-supervised visual pre-training has recently emerged in scene text recognition (STR), which designs the pretext tasks and takes unlabeled data as input to obtain useful representations for STR. However, most current self-supervised methods do not pay special attention to the importance of sequence awareness. Accordingly, we propose a novel self-supervised STR method based on contrastive predictive coding (STR-CPC), which regards a text instance as a sequence from left to right and captures the visual sequence correlation. Considering the information overlap problem within the feature map induced by the deep convolutional neural network (CNN) encoder, we design a widthwise causal convolution during model pre-training and a progressive recovery training strategy (PRTS) during model fine-tuning to improve the STR performance. Experiments on scene text show that our STR-CPC method outperforms the existing self-supervised methods, which testifies the advantage of visual sequence correlation for STR. Additionally, STR-CPC observably boosts performance compared with supervised training when the amount of labeled data decreases.

关键词： Training Visualization Correlation Text recognition Convolution Self-supervised learning predictive coding

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A predictive coding using Markov chain

A predictive coding using Markov chain

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7th International Conference on Signal Processing

作者： Lu, PF Gu, BJ Lu, WH So Yangtze Univ Dept Commun Engn Wuxi 214036 Peoples R China

ISBN: (纸本)7121002159

Based on the relationship among the peak points and valley points of the probability density function (p.d.f) of a stochastic process, whose p.d.f. may e multimodal. the drift coefficient of its associated diffusion process, the 'shift back to center' property of the Markov chain and the state transitive value of the chain, the paper introduces the algorithm for constructing the approximating model of the Markov chain of an Ito stochastic differential equation (AMMC). The results of simulations demonstrate that the variance of the prediction error of the AMMC is not only far smaller than that of the Burg lattice predictor, but also very close to constant. These properties of the algorithm are beneficial to predictor and predictive coding.

关键词： stochastic process approximation Markov chain nonlinear modeling prediction algorithni data compression predictive coding

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Understanding Cognitive Behavioral Therapy for Psychosis Through the predictive coding Framework

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BIOLOGICAL PSYCHIATRY: GLOBAL OPEN SCIENCE 2024年第4期4卷 100333-100333页

作者： Sheffield, Julia M. Brinen, Aaron P. Feola, Brandee Heckers, Stephan Corlett, Philip R. Vanderbilt Univ Med Ctr Dept Psychiat & Behav Sci Nashville TN 27599 USA Yale Sch Med Dept Psychiat Clin Neurosci Res Unit New Haven CT USA

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.

关键词： Belief updating CBTp Persecutory delusions predictive coding Psychotherapy Volatility

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CNN-based Image predictive coding 14

CNN-based Image Predictive Coding

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14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA)

作者： Tang, Tang Tetzlaff, Ronald Tech Univ Dresden Fac Elect Eng & Informat Technol D-01069 Dresden Germany

ISBN: (纸本)9781479960071

In this work the feasibility of implementing Cellular Neural Networks (CNN) for image predictive coding is investigated. Various CNN structures as predictors are proposed. The performances are compared to the existing predictive coding methods. Thanks to their massive parallel nature, CNN have been proven well suitable for image predictive coding application.

关键词： Cellular Neural Networks predictive coding

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