Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

During the past 20 years, research in the behavioral sciences has become increasingly sophisticated. New findings concerning the brain and behavior have forced upon us the unpleasant necessity of reexamining our models. Concepts once useful, such as "the unconscious," "regression," and "motivation," no longer serve to adequately encompass the data. Ashby is a representative of this new order of sophistication in research, as he proceeds to investigate the problem of the brain's orderliness. He accepts, as fact, that the nervous system behaves adaptively and assumes that it is, in its essentials, mechanistic. These two data are approached as not irreconcilable. From this point, he proceeds to examine dynamic systems from the standpoint of rigorous scientific —dominantly mathematical—logic until he has evolved a theoretical counterpart of brain functioning. He then builds and experiments with a prototype electronic model, which he names the "Homeostat," and tests various aspects of his theories of

Design for a Brain

It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and interest in learning new behaviours. The adaptive value of such intrinsically motivated activities lies in the fact that they allow the cumulative acquisition of knowledge and skills that can be used later to accomplish tness-enhancing goals. Intrinsic motivations continue during adulthood, and in humans they underlie lifelong learning, artistic creativity, and scientific discovery, while they are also the basis for processes that strongly affect human well-being, such as the sense of competence, self-determination, and self-esteem. This book has two aims: to present the state of the art in research on intrinsically motivated learning, and to identify the related scientific and technological open challenges and most promising research directions. The book introduces the concept of intrinsic motivation in artificial systems, reviews the relevant literature, offers insights from the neural and behavioural sciences, and presents novel tools for research. The book is organized into six parts: the chapters in Part I give general overviews on the concept of intrinsic motivations, their function, and possible mechanisms for implementing them; Parts II, III, and IV focus on three classes of intrinsic motivation mechanisms, those based on predictors, on novelty, and on competence; Part V discusses mechanisms that are complementary to intrinsic motivations; and Part VI introduces tools and experimental frameworks for investigating intrinsic motivations.The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots. The contributing authors are among the pioneers carrying out fundamental work on this topic, drawn from related disciplines such as artificial intelligence, robotics, artificial life, evolution, machine learning, developmental psychology, cognitive science, and neuroscience. The book will be of value to graduate students and academic researchers in these domains, and to engineers engaged with the design of autonomous, adaptive robots.

Intrinsically Motivated Learning in Natural and Artificial Systems

The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network.

/pdf/sensorimotor-input-as-a-language-generalisation-tool-a-2n1tp99iw0.pdf

Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

Prosthetic and therapeutic devices have been developed to ameliorate the quality of daily living for people with amputation or neurological disorders. However, many of them fall short of functional benefits, and therefore, are frequently rejected by users due to awkward control, or no awareness of interaction during tasks. Traditional wisdom in the design of prosthetic and therapeutic devices may have emphasized the need to provide users with apparatus that replace or assist motor ability. Rather, the notion to achieve neural compatibility with the existing sensorimotor system has not been well recognized. We argue that providing biomimetic control and sensing capacity to prosthetic and therapeutic devices can enhance their neural compatibility, and therefore, can yield greater functionality in performing activities of daily lives, or in rehabilitation training. In this paper, the authors will present a range of neural technologies that may allow implementation of biomimetic sensorimotor control, including natural sensory feedback, neuromuscular like compliant control, natural module of synergy-based control, as well as advanced neural signal processing techniques. Based on the evidence in our research and in literature, we propose that achieving neural compatibility with the existing human sensorimotor system should be the ultimate goal of prosthetic and therapeutic devices.

/pdf/achieving-neural-compatibility-with-human-sensorimotor-3gsnxwey7y.pdf

Achieving Neural Compatibility With Human Sensorimotor Control in Prosthetic and Therapeutic Devices

This paper presents a novel open-source software application, Aquila, developed as a part of the ITALK and RobotDoC projects. The software provides many different tools and biologically-inspired models, useful for cognitive and developmental robotics research. Aquila addresses the need for high-performance robot control by adopting the latest parallel processing paradigm, based on the NVidia CUDA technology. The software philosophy, implementation, functionalities and performance are described together with three practical examples of selected modules.

Aquila: An open-source GPU-accelerated toolkit for cognitive and neuro-robotics research

This paper presents a novel open-source software Aquila developed as a part of the iTalk and RobotDoC projects. This software provides many different tools and biologically inspired systems that are useful for cognitive robotics research. Aquila addresses the need for high-performance robot control by adopting the latest parallel processing paradigm based on the NVidia CUDA technology. The software philosophy, implementation, functionalities and performance are described together with three practical examples of selected modules.

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Evolved active vision control systems have the ability to extract relevant information from an environment in order to solve a specific task [1]. This work has the objective to investigate an obstacle avoidance and navigation systems for space exploration rovers capable of performing autonomous tasks in challenging planetary terrains. The paper presents an evolutionary robotics approach applied to a Mars rover model that is equipped with an active vision camera and a neural network control system. Preliminary experimental results suggest that such an active vision system provides a powerful and yet computationally cheap way of developing important visual processing strategies to navigate in the environment.

Active Vision for Navigating Unknown Environments: An Evolutionary Robotics Approach for Space Research

We present a model for reachability assessment implemented in a simulated iCub humanoid robot. The robot uses a neural network both for estimating reachability and as a controller for the arm. During training, multi-modality information including vision and proprioception of the effector's length was provided, along with tactile and postural information. The task was to assess if a target in view was at reach range. After training with data from two different effector's lengths, the system generalised also for a third one, both for producing reaching postures and for assessing reachability. We present preliminary results that show good reachability predictions with a decrease in confidence that display a depth gradient.

Adaptive reachability assessment in the humanoid robot iCub

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Aquila: An open-source GPU-accelerated toolkit for cognitive and neuro-robotics research

Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research

Active Vision for Navigating Unknown Environments: An Evolutionary Robotics Approach for Space Research

Adaptive reachability assessment in the humanoid robot iCub