Cognitive neuroscientists have started to uncover the neural substrates, systems, and mechanisms enabling us to prioritize the processing of certain sensory information over other, currently less-relevant, inputs. However, there is still a large gap between the knowledge generated in the laboratory and its application to real-life problems of attention as when, for example, interface operators are multi-tasking. In this Element, laboratory studies on crossmodal attention (both behavioural/psychophysical and cognitive neuroscience) are situated within the applied context of driving. We contrast the often idiosyncratic conditions favoured by much of the laboratory research, typically using a few popular paradigms involving simplified experimental conditions, with the noisy, multisensory, real-world environments filled with complex, intrinsically-meaningful stimuli. By drawing attention to the differences between basic and applied studies in the context of driving, we highlight a number of important issues and neglected areas of research as far as the study of crossmodal attention is concerned.

How does the human brain manage to integrate all the information coming from different sensory outputs? The first book by two of the leading stars in cognitive neuroscience, this book addresses one of the hottest topics in the field.

Attention: Theory and Practice provides a balance between a readable overview of attention and an emphasis on how theories and paradigms for the study of attention have developed. The book highlights the important issues and major findings while giving sufficient details of experimental studies, models, and theories so that results and conclusions are easy to follow and evaluate. Rather than brushing over tricky technical details, the authors explain them clearly, giving readers the benefit of understanding the motivation for and techniques of the experiments in order to allow readers to think through results, models, and theories for themselves. Attention is an accessible text for advanced undergraduate and graduate students in psychology, as well as an important resource for researchers and practitioners interested in gaining an overview of the field of attention.

During the last three decades, there have been enormous advances in our understanding of the neural mechanisms of selective attention at the network as well as the cellular level. The Oxford Handbook of Attention brings together the different research areas that constitute contemporary attention research into one comprehensive and authoritative volume. In 40 chapters, it covers the most important aspects of attention research from the areas of cognitive psychology, neuropsychology, human and animal neuroscience, computational modelling, and philosophy. The book is divided into 4 main sections. Following an introduction from Michael Posner, the books starts by looking at theoretical models of attention. The next two sections are dedicated to spatial attention and non-spatial attention respectively. Within section 4, the authors consider the interactions between attention and other psychological domains. The last two sections focus on attention-related disorders, and finally, on computational models of attention. Aimed at both scholars and students, the Oxford Handbook of Attention provides a concise and state-of-the-art review of the current literature in this field.

Attention in Action provides state-of-the-art discussion of the role of attention in action and of action in constraining attention.

This collection of essays forms a comprehensive overview of this crucial component of human cognitive function.

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

In the past two decades, attention has been one of the most investigated areas of research in perception and cognition. However, the literature on the field contains a bewildering array of findings, and empirical progress has not been matched by consensus on major theoretical issues. The Psychology of Attention presents a systematic review of the main lines of research on attention; the topics range from perception of threshold stimuli to memory storage and decision making. The book develops empirical generalizations about the major issues and suggests possible underlying theoretical principles. Pashler argues that widely assumed notions of processing resources and automaticity are of limited value in understanding human information processing. He proposes a central bottleneck for decision making and memory retrieval, and describes evidence that distinguishes this limitation from perceptual limitations and limited-capacity short-term memory.

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

The early years of modern experimental psychology were marked by a considerable amount of research on attention, and much work was carried out in the laboratories of Wundt, Titchener and Helmholtz. For various reasons, research on attention declined from 1920 until the 1950s. Under the early philosophy of behaviourism, attention became suspect as a ‘mentalistic’ concept. At the time of original publication in 1969, however, much work had been done to quantify and make objective research in this area. This was of increasing importance in a world dominated by communication networks, and ‘man-machine’ systems, in which the human element is the weakest link due to the limits on the rate at which man can handle information. Following the publication of Broadbent’s Perception and Communication in 1958, work on attention had begun to pour from an ever increasing number of laboratories. This book is dedicated to summarising what we knew, and attempts to survey the behavioural research in vision and hearing which throw light on how we share and direct attention, what are the limits of attention, to make some general methodological recommendations, to review current theories of the time, and to provide a guide to the relevant physiological work. As far as possible, work on memory has been omitted. A bibliography of the major work to the spring of 1969 is included.