Article
Neurosciences
Phan Luu, Don M. Tucker, Karl Friston
Summary: This paper proposes that the dorsal attention system's control is regulated by the dorsal division of the limbic system, while the ventral attention system is regulated by the ventral limbic division. These forms of cognitive control reflect the vertical integration of subcortical arousal control systems that evolved for specific forms of behavior control.
Article
Computer Science, Artificial Intelligence
Francesco Mannella, Federico Maggiore, Manuel Baltieri, Giovanni Pezzulo
Summary: Rodents actively use whisking to probe their environment and anticipate object locations. Whisking control involves minimizing prediction errors from unexpected contacts, driving an active inference process. The model may help explain how neural circuits underlie whisking behavior in rodents.
Article
Clinical Neurology
Zachary Pierce-Messick, Laura H. Corbit
Summary: Many people struggle to control weight by modifying their food-related behaviors, but typically only see short-term effects and regain lost weight. One possible explanation is that these behaviors have become habits that are not immediately sensitive to consequences.
PROGRESS IN NEURO-PSYCHOPHARMACOLOGY & BIOLOGICAL PSYCHIATRY
(2021)
Article
Energy & Fuels
Hamza Assia, Houari Merabet Boulouiha, William David Chicaiza, Juan Manuel Escano, Abderrahmane Kacimi, Jose Luis Martinez-Ramos, Mouloud Denai
Summary: The study proposes an active fault-tolerant control system that combines BADRC theory, ANFIS detector, and PCA method to address the issue of actuator generator torque failure in offshore wind farms. The results demonstrate the effectiveness of the proposed method in maintaining system performance and detecting false data.
Article
Psychology, Experimental
Swann Pichon, Benoit Bediou, Lia Antico, Rachael Jack, Oliver Garrod, Chris Sims, C. Shawn Green, Philippe Schyns, Daphne Bavelier
Summary: Action video game players show superior performance in cognition, but do not necessarily outperform non-video game players in perceiving facial emotions. Studies indicate that the perceptual benefits associated with action video game play do not extend to overlearned stimuli such as facial emotion, with equivalent skills observed in both groups.
Article
Acoustics
S. K. Lai, Y. T. Zhang, J. Q. Sun
Summary: This study investigates a modified active noise control system using Bayesian inference method, combining FxLMS algorithm and dynamic linear model to enhance low-frequency noise attenuation. The combination of Bayesian approach and DLM aids in raw signal pre-processing and generating reference signals, contributing to noise characteristics determination and feedback to the control system for better performance in time-domain signal control algorithms.
Article
Psychology, Multidisciplinary
Lotte P. P. Brinkhof, K. Richard Ridderinkhof, Maik Bieleke, Jaap M. J. Murre, Harm J. J. Krugers, Sanne de Wit
Summary: Based on evidence of age-related impairments in flexible control, forming habits has been recognized as a beneficial method for resilience among older adults. This study examined the relationship between quality of life and mental well-being in older adults and their inclination towards strategic planning and lifestyle regularity. The beneficial effects of habit predisposing factors on mental well-being and quality of life were mediated by conscientiousness, emphasizing its importance for resilience and suggesting potential targets for promoting conscientious behavior.
CURRENT PSYCHOLOGY
(2023)
Article
Psychology, Biological
Alexander Tschantz, Laura Barca, Domenico Maisto, Christopher L. Buckley, Anil K. Seth, Giovanni Pezzulo
Summary: This article uses the framework of active inference to simulate and study interoceptive control and its dysfunctions. Interoceptive control aims to minimize the discrepancy between expected and actual interoceptive sensations through different forms of control. The analysis of generative models within active inference provides predictions for physiological and brain signals, supporting empirical research.
BIOLOGICAL PSYCHOLOGY
(2022)
Article
Clinical Neurology
Thomas Parr, Jakub Limanowski, Vishal Rawji, Karl Friston
Summary: The paper introduces a computational neurology of movement that integrates theoretical neurobiology and clinical neurology, highlighting the brain functioning as a process of active inference. By simulating clinical examinations and neurological deficits, the significance of this approach is illustrated, providing insights into the neurocomputational architecture of movement control.
Article
Computer Science, Hardware & Architecture
Dapeng Qu, Jun Wu, Jiankun Zhang, Chengxi Gao, Haiying Shen, Keqin Li
Summary: As a pioneering network architecture, Named Data Networking (NDN) leverages the content-centric model and connectionless transmission mode to enhance network capacity. C3NDN is a congestion control scheme that utilizes caching strategy in NDN, formulating a One-Interest-Multiple-Data model to improve network efficiency and employing a probabilistic caching strategy to optimize NDN's in-network caching characteristic. Additionally, C3NDN incorporates a congestion control algorithm based on the One-Interest-Multiple-Data model, considering the bandwidth and delay information of the transmission path.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Firat Ozdemir, Zixuan Peng, Philipp Fuernstahl, Christine Tanner, Orcun Goksel
Summary: This paper proposes an active learning framework that optimally utilizes expert clinician time in medical image analysis, generating improved segmentation performance. By combining representativeness with uncertainty, the method iteratively estimates ideal samples to be annotated from a given dataset.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Statistics & Probability
Matias D. Cattaneo, Yingjie Feng, Rocio Titiunik
Summary: This study addresses the fundamental problem of uncertainty quantification in synthetic control methods by developing conditional prediction intervals with finite-sample probability guarantees. The method allows for covariate adjustment and nonstationary data. By considering two distinct sources of randomness in the SC prediction, the proposed prediction intervals are constructed taking into account the statistical uncertainty, leading to principled sensitivity analysis methods. Empirical applications and a small simulation study demonstrate the numerical performance of the proposed methodology.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Mathematics, Interdisciplinary Applications
Sarah Schwoebel, Dimitrije Markovic, Michael N. Smolka, Stefan J. Kiebel
Summary: Recent evidence suggests that habit formation and relearning of habits operate in a context-dependent manner. The proposed hierarchical Bayesian approach enables conjoint learning of habits and reward structure in a context-specific manner. This model offers insights into how the brain may balance contributions of habitual and goal-directed control.
JOURNAL OF MATHEMATICAL PSYCHOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Cristian Meo, Giovanni Franzese, Corrado Pezzato, Max Spahn, Pablo Lanillos
Summary: This article presents a novel multisensory active inference (AIF) torque controller for adaptation in robotic systems. The controller improves upon current AIF approaches by incorporating learning and multimodal integration of sensor inputs, resulting in improved control accuracy and noise rejection capabilities.
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
(2023)
Article
Psychology, Multidisciplinary
Mark Miller, Mahault Albarracin, Riddhi J. Pitliya, Alex Kiefer, Jonas Mago, Claire Gorman, Karl J. Friston, Maxwell J. D. Ramstead
Summary: This article aims to conceptualize and formalize the construct of resilience using active inference, and offers a threefold distinction between inertia, elasticity, and plasticity. It situates these three senses of resilience within the framework of active inference.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Psychiatry
Yukiko Matsumoto, Satoshi Nishida, Ryusuke Hayashi, Shuraku Son, Akio Murakami, Naganobu Yoshikawa, Hiroyoshi Ito, Naoya Oishi, Naoki Masuda, Toshiya Murai, Karl Friston, Shinji Nishimoto, Hidehiko Takahashi
Summary: This study used functional magnetic resonance imaging (fMRI) to evaluate the large-scale network structures of concept representations in patients with schizophrenia and found that their semantic networks exhibited differences and were associated with thought disorders. This provides pathophysiological evidence for the loosening of associations in schizophrenia.
SCHIZOPHRENIA BULLETIN
(2023)
Article
Computer Science, Artificial Intelligence
Natalie Kastel, Casper Hesp, K. Richard Ridderinkhof, Karl J. Friston
Summary: This paper proposes a testable deep active inference formulation of social behavior and conducts simulations of cumulative culture. By considering cultural transmission as a bi-directional process of communication and social exchange as a process of active inference, the study discovers that cumulative culture emerges from belief updating through a joint minimization of uncertainty.
FRONTIERS IN NEUROROBOTICS
(2023)
Article
Public, Environmental & Occupational Health
Cam Bowie, Karl Friston
Summary: This study analyzed the COVID-19 epidemic in the past 12 months and made predictions for the next year based on this analysis. It found that changes in transmissibility and public behavior led to an underestimation of the severity of the epidemic in previous predictions. The projections indicate that the number of infections in the coming year will be three times larger than last year, leading to more deaths and economic consequences.
FRONTIERS IN PUBLIC HEALTH
(2023)
Review
Neurosciences
Thomas Parr, Karl Friston, Giovanni Pezzulo
Summary: A central theme of theoretical neurobiology is that most cognitive operations require processing of discrete sequences, and this processing is driven by continuous neuronal dynamics. From the perspective of active inference, we explore sequential brain processing by assuming a generative model of the sensed world. This model includes central pattern generators, narratives, or well-defined sequences, and can account for various aspects of motor control, perception, planning, and understanding.
COGNITIVE NEURODYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Lancelot Da Costa, Noor Sajid, Thomas Parr, Karl Friston, Ryan Smith
Summary: Active inference is a probabilistic framework based on the principle of minimizing free energy, used for modeling the behavior of biological and artificial agents. It has been successfully applied to various situations involving reward maximization, often yielding comparable or superior results to alternative approaches. This article explores the connection between reward maximization and active inference and demonstrates the conditions under which active inference produces the optimal solution to the Bellman equation, a fundamental equation in reinforcement learning and control. Additionally, it introduces a new recursive active inference scheme that can produce Bellman optimal actions on any finite temporal horizon.
NEURAL COMPUTATION
(2023)
Letter
Neurosciences
Brett J. Kagan, Adeel Razi, Anjali Bhat, Andy C. Kitchen, Nhi T. Tran, Forough Habibollahi, Moein Khajehnejad, Bradyn J. Parker, Ben Rollo, Karl J. Friston
Article
Neurosciences
R. L. Carhart-Harris, S. Chandaria, D. E. Erritzoe, A. Gazzaley, M. Girn, H. Kettner, P. A. M. Mediano, D. J. Nutt, F. E. Rosa, L. Roseman, C. Timmermann, B. Weiss, R. J. Zeifman, K. J. Friston
Summary: This theoretical article proposes a new model of a general factor of psychopathology, using the concept of 'canalization'. It distinguishes between two types of plasticity: 'TEMP' and 'canalization', which can be differentiated by their relationship to precision or inverse variance. The authors argue that 'pathological' phenotypes develop through mechanisms of canalization and increased model precision, as a response to adversity and distress. They suggest that TEMP, along with psychological support, can counter canalization and offer experiments and measures to test their hypotheses.
Correction
Computer Science, Artificial Intelligence
Natalie Kastel, Casper Hesp, K. Richard Ridderinkhof, Karl J. Friston
FRONTIERS IN NEUROROBOTICS
(2023)
Article
Pediatrics
Zoe McParlin, Francesco Cerritelli, Andrea Manzotti, Karl J. Friston, Jorge E. Esteves
Summary: Therapeutic affective touch is crucial for survival, nurturing supportive interactions, and promoting overall health. This paper presents an integrative model that combines therapeutic touch and communication to achieve biobehavioural synchrony. It explains the neurophysiological and behavioural mechanisms of developing synchronous relationships through touch and emphasizes the importance of therapeutic touch in building a solid therapeutic alliance.
FRONTIERS IN PEDIATRICS
(2023)
Article
Neurosciences
Christini Katsanevaki, Andre M. Bastos, Hayriye Cagnan, Conrado A. Bosman, Karl J. Friston, Pascal Fries
Summary: Selective attention enhances the influence of specific synaptic inputs on higher-area neurons, enabling preferential routing of attended stimuli. Presynaptic circuits, influenced by top-down attentional signals, play a crucial role in selective routing by selectively entraining postsynaptic neurons. The study demonstrates that attentional modulation of intrinsic connections in the visual cortex mediates selective entrainment, providing an explanation for the observed phenomenon.
Review
Biology
Antonella Maselli, Jeremy Gordon, Mattia Eluchans, Gian Luca Lancia, Thomas Thiery, Riccardo Moretti, Paul Cisek, Giovanni Pezzulo
Summary: Psychology and neuroscience should adopt innovative experimental designs, measurement methods, analysis techniques, and computational models to study rich, ecologically valid forms of behavior. Studying restricted behaviors in laboratory settings risks missing key aspects of cognitive and neural functions. This article highlights the challenges and opportunities of studying rich forms of behavior and emphasizes the importance of developing sophisticated formal models to understand cognitive and neural processes.
PHYSICS OF LIFE REVIEWS
(2023)
Article
Psychology, Experimental
Denis Brouillet, Karl Friston
Summary: The brain is known to be a predictive organ that predicts sensory content and the accuracy of its predictions. It must infer the reliability of its own beliefs in order to predict the precision of its predictions. This recognition process leads to the concept of "fluency", which is the perception of having a precise understanding of sensory processes. Changes in fluency, from unfelt to felt, are recognized and realized when updating predictions about accuracy.
CONSCIOUSNESS AND COGNITION
(2023)
Article
Psychology
Douglas G. Lee, Marco D'Alessandro, Pierpaolo Iodice, Cinzia Calluso, Aldo Rustichini, Giovanni Pezzulo
Summary: This study provides evidence for the framework where information about individual attributes independently impacts decision-making. The results suggest that risky decisions are resolved by running parallel comparisons between separate attributes, contradicting the assumption of standard economic theory. The study also reveals that attribute salience affects risk preference types differently.
COGNITIVE PSYCHOLOGY
(2023)
Article
Automation & Control Systems
Domenico Maisto, Francesco Donnarumma, Giovanni Pezzulo
Summary: This research proposes a computational model based on active inference for multi-agent cooperative joint actions. The model utilizes interactive inference to probabilistically infer the joint goal and updates beliefs and strategies through observation of each other's movements. The results of simulations demonstrate that interactive inference supports successful multi-agent joint actions and replicates key dynamics observed in human-human experiments.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Review
Biology
Karl Friston, Lancelot Da Costa, Dalton A. R. Sakthivadivel, Conor Heins, Grigorios A. Pavliotis, Maxwell Ramstead, Thomas Parr
Summary: This paper introduces a path integral formulation of the free energy principle to describe the trajectories of particles over time. By employing the principle of least action, it is possible to simulate the behavior of particles in exchange with their external environment. The paper discusses various types of particles and their different levels of inference or sentience.
PHYSICS OF LIFE REVIEWS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.