Shingo Murata
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View article: Active Inference with Dynamic Planning and Information Gain in Continuous Space by Inferring Low-Dimensional Latent States
Active Inference with Dynamic Planning and Information Gain in Continuous Space by Inferring Low-Dimensional Latent States Open
Active inference offers a unified framework in which agents can exhibit both goal-directed and epistemic behaviors. However, implementing policy search in high-dimensional continuous action spaces presents challenges in terms of scalabilit…
View article: Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks
Variational Adaptive Noise and Dropout towards Stable Recurrent Neural Networks Open
This paper proposes a novel stable learning theory for recurrent neural networks (RNNs), so-called variational adaptive noise and dropout (VAND). As stabilizing factors for RNNs, noise and dropout on the internal state of RNNs have been se…
View article: System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems
System 0/1/2/3: Quad-process theory for multi-timescale embodied collective cognitive systems Open
This paper introduces the System 0/1/2/3 framework as an extension of dual-process theory, employing a quad-process model of cognition. Expanding upon System 1 (fast, intuitive thinking) and System 2 (slow, deliberative thinking), we incor…
View article: Psychometric Properties of Hierarchical Psychiatric Symptoms on the General Population
Psychometric Properties of Hierarchical Psychiatric Symptoms on the General Population Open
Although psychiatric disorders have conventionally been treated categorically, recent research indicates a continuous and hierarchical structure among psychiatric symptoms, with a general psychopathology factor (p-factor) at the top and se…
View article: Real-World Robot Control Based on Contrastive Deep Active Inference With Demonstrations
Real-World Robot Control Based on Contrastive Deep Active Inference With Demonstrations Open
Despite significant advances in robotics and deep learning, the ability of robots to perceive and act remain far below that of humans. To bridge this gap, we utilize active inference, a framework based on the free-energy principle that acc…
View article: World models and predictive coding for cognitive and developmental robotics: frontiers and challenges
World models and predictive coding for cognitive and developmental robotics: frontiers and challenges Open
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View article: Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data
Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data Open
Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (f…
View article: World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges
World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges Open
Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Their learning processes should be based …
View article: Latent Representation in Human–Robot Interaction With Explicit Consideration of Periodic Dynamics
Latent Representation in Human–Robot Interaction With Explicit Consideration of Periodic Dynamics Open
This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critica…
View article: Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data
Interaction between Functional Connectivity and Neural Excitability in Autism: A Novel Framework for Computational Modeling and Application to Biological Data Open
Functional connectivity (FC) and neural excitability may interact to affect symptoms of autism spectrum disorder (ASD). We tested this hypothesis with neural network simulations, and applied it with functional magnetic resonance imaging (f…
View article: Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks
Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks Open
We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one f…
View article: Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder
Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder Open
Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-reactivity to sensory stimuli are diagnostic features of autism spectrum disorder and are re…
View article: Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework
Neural network modeling of altered facial expression recognition in autism spectrum disorders based on predictive processing framework Open
BackgroundThe mechanism underlying the emergence of emotional categories from visual facial expression information during the developmental process is largely unknown. Therefore, this study proposes a system-level explanation for understan…
View article: Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder
Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder Open
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. In particular, developmental learning a…
View article: Homogeneous intrinsic neuronal excitability induces overfitting to sensory noise: A robot model of neurodevelopmental disorder
Homogeneous intrinsic neuronal excitability induces overfitting to sensory noise: A robot model of neurodevelopmental disorder Open
Neurodevelopmental disorders, including autism spectrum disorder, have been intensively investigated at the neural, cognitive, and behavioral levels, but the accumulated knowledge remains fragmented. Here, we propose a mechanistic explanat…
View article: Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder
Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder Open
Hyper- and hyporeactivity to sensory stimuli is a diagnostic feature of autism spectrum disorder and has been reported in many neurodevelopmental disorders. However, the computational mechanisms underlying such paradoxical responses remain…
View article: A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision
A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision Open
Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder i…
View article: Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning
Detecting Features of Tools, Objects, and Actions from Effects in a Robot using Deep Learning Open
We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables robots to manipulate objects with tools, using infant learni…
View article: Acquisition of Viewpoint Transformation and Action Mappings via Sequence to Sequence Imitative Learning by Deep Neural Networks
Acquisition of Viewpoint Transformation and Action Mappings via Sequence to Sequence Imitative Learning by Deep Neural Networks Open
We propose an imitative learning model that allows a robot to acquire positional relations between the demonstrator and the robot, and to transform observed actions into robotic actions. Providing robots with imitative capabilities allows …
View article: Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions
Representation Learning of Logic Words by an RNN: From Word Sequences to Robot Actions Open
An important characteristic of human language is compositionality. We can efficiently express a wide variety of real-world situations, events, and behaviors by compositionally constructing the meaning of a complex expression from a finite …
View article: Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction
Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human–Robot Interaction Open
To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we …