This both accessible and exhaustive book will help to improve modeling of attention and to inspire innovations in industry. It introduces the study of attention and focuses on attention modeling, addressing such themes as saliency models, signal detection and different types of signals, as well as real-life applications. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering and computer science, amongst other disciplines. What is attention? We all pay attention every single moment of our lives. Attention is how the brain selects and prioritizes information. The study of attention has become incredibly complex and divided: this timely volume assists the reader by drawing together work on the computational aspects of attention from across the disciplines. Those working in the field as engineers will benefit from this book’s introduction to the psychological and biological approaches to attention, and neuroscientists can learn about engineering work on attention. The work features practical reviews and chapters that are quick and easy to read, as well as chapters which present deeper, more complex knowledge. Everyone whose work relates to human perception, to image, audio and video processing will find something of value in this book, from students to researchers and those in industry.

The derivation, exposition, and justification of the Selective Tuning model of vision and attention.

This book presents state-of-the-art computational attention models that have been successfully tested in diverse application areas and can build the foundation for artificial systems to efficiently explore, analyze, and understand natural scenes. It gives a comprehensive overview of the most recent computational attention models for processing visual and acoustic input. It covers the biological background of visual and auditory attention, as well as bottom-up and top-down attentional mechanisms and discusses various applications. In the first part new approaches for bottom-up visual and acoustic saliency models are presented and applied to the task of audio-visual scene exploration of a robot. In the second part the influence of top-down cues for attention modeling is investigated.

Consciously or unconsciously, humans always pay attention to a wide variety of stimuli. Attention is part of daily life and it is the first step to understanding. The proposed thesis deals with a computational approach to the human attentional mechanism and with its possible applications mainly in the field of computer vision. In a first stage, the text introduces a rarity-based three-level attention model handling monodimensional signals as well as images or video sequences. The concept of attention is defined as the transformation of a huge acquired unstructured data set into a smaller structured one while preserving the information: the attentional mechanism turns rough data into intelligence. Afterwards, several applications are described in the fields of machine vision, signal coding and enhancement, medical imaging, event detection and so on. These applications not only show the applicability of the proposed computational attention method, but they also support the idea that similarly to the fact that attention is the beginning of intelligence in humans, computational attention may be the starting point of artificial intelligence in engineering applications. Several databases containing different kinds of signals were used to test the model and its applications: audio signals of natural complex ambiences and events, real-life video sequences as well as simulated sequences and finally natural scenes, textured or synthetic images. Results are presented in a clear and comprehensive way within each application providing the relevance of the use of the computational attention model. Finally, a large discussion is opened based on the theoretical and practical achievements and future extensions are proposed.

A cutting-edge reference source for the interdisciplinary field of computational cognitive modeling.

Attention has represented a core scienti?c topic in the design of AI-enabled systems in the last few decades. Today, in the ongoing debate, design, and c- putationalmodelingofarti?cialcognitivesystems,attentionhasgainedacentral position as a focus of research. For instance, attentional methods are considered in investigating the interfacing of sensory and cognitive information processing, for the organization of behaviors, and for the understanding of individual and social cognition in infant development. Whilevisualcognitionplaysacentralroleinhumanperception,?ndingsfrom neuroscience and experimental psychology have provided strong evidence about the perception–action nature of cognition. The embodied nature of senso- motor intelligence requires a continuous and focused interplay between the c- trolofmotoractivitiesandtheinterpretationoffeedbackfromperceptualmod- ities. Decision making about the selection of information from the incoming sensory stream – in tune with contextual processing on a current task and an agent’s global objectives – becomes a further challenging issue in attentional control. Attention must operate at interfaces between a bottom-up-driven world interpretationandtop-down-driveninformationselection,thusactingatthecore of arti?cial cognitive systems. These insights have already induced changes in AI-related disciplines, such as the design of behavior-based robot control and the computational modeling of animats. Today, the development of enabling technologiessuch as autonomous robotic systems,miniaturizedmobile–evenwearable–sensors,andambientintelligence systems involves the real-time analysis of enormous quantities of data. These data have to be processed in an intelligent way to provide “on time delivery” of the required relevant information. Knowledge has to be applied about what needs to be attended to, and when, and what to do in a meaningful sequence, in correspondence with visual feedback.

Are we paying enough attention? At least since the nineteenth century, critics have alleged a widespread and profound failure of attentiveness—to others, to ourselves, to the world around us, to what is truly worthy of focus. Why is there such great anxiety over attention? What is at stake in understanding attention and the challenges it faces? This book investigates attention from a range of disciplinary perspectives, including philosophy, history, anthropology, art history, and comparative literature. Each chapter begins with a concrete scene whose protagonists are trying—and often failing—to attend. Authors examine key moments in the history of the study of attention; pose attention as a philosophical problem; explore the links between attention, culture, and technology; and consider the significance of attention for conceptualizations of human subjectivity. Readers encounter nineteenth-century experiments in boredom, ornithologists conveying sound through field notations, wearable attention-enhancing prosthetics, students using online learning platforms, and inquiries into attention as a cognitive state and moral virtue. Amid mounting concern about digital mediation of experience, the rise of “surveillance capitalism,” and the commodification of attention, Scenes of Attention deepens the thinking that is needed to protect the freedom of attention and the forms of life that make it possible.