The Influence Of Attention Learning And Motivation On Visual Search

The Influence of Attention, Learning, and Motivation on Visual Search will bring together distinguished authors who are conducting cutting edge research on the many factors that influence search behavior. These factors will include low-level feature detection; statistical learning; scene perception; neural mechanisms of attention; and applied research in real world settings.

Use of visual information is used to augment our knowledge, decide on our actions, and keep track of our environment. Even with eyes closed, people can remember visual and spatial representations, manipulate them, and make decisions about them. The chapters in Volume 42 of Psychology of Learning and Motivation discuss the ways cognition interacts with visual processes and visual representations, with coverage of figure-ground assignment, spatial and visual working memory, object identification and visual search, spatial navigation, and visual attention.

Knowledge and Vision, Volume 70, the latest release in the Psychology of Learning and Motivation, features empirical and theoretical contributions in cognitive and experimental psychology, ranging from classical and instrumental conditioning, to complex learning and problem-solving. Topics in this new release include Memorability: How what we see influences what we remember, The impact of prior knowledge on visual memory, Neural dynamics of visual and semantic object processing, Comprehending and developing the meaning of visual narratives, Attention and vision, The role of learning and memory in early visual development, The Information Content of Visual Categories, What do neurons really want?, and more. Contains coverage of an unusually broad set of emerging topics in language, spanning comprehension and production and both speech and reading

This collection of essays, intended as a text for students, examines the different facets of research into attention. The book is divided into two sections: one deals with psychological research into such areas as visual search, dual-task interference and attentional bottleneck; the other deals with approaches to neural-network modelling and the effects of brain damage on attention.

The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.

Anyone interested in the study of attention will have had some exposure to the work of Anne Treisman. Anne Treisman has been one of the most influential cognitive psychologists in the last 50 years. Her research and theoretical insights have influenced a variety of disciplines, including vision sciences, auditory sciences, cognitive psychology, cognitive neurosciences, philosophy, psychiatry, neuropsychology, and behavioral neurology. She is best known for her work on attention. Early in her career, much of that work involved auditory stimuli. Her later work has been primarily in the realm of visual attention. She has been especially concerned with the interactions among visual perception, attention, and memory as they relate to conscious and unconscious experience. Her Feature Integration Theory has been one of the organizing ideas in the field for three decades. While still a graduate student at Oxford, she helped launch the modern study of attention. In the present volume, several of her most influential papers are reprinted (including some of the harder to find early work). To accompany these reprints, the editors invited experts to comment and/or to show how their own work had been shaped by Treisman's ideas and findings. The result is a scientifically rich ride through the world of ideas inspired by Treisman's work. The contributed chapters include discussions of auditory and visual attention, the role of features in selection, parallel and serial processing, and automaticity. They describe the roots and evolution of Feature Integration Theory and related models like Guided Search. They explore the interactions of attention and perception at the cognitive, neuropsychological, and biological levels. Readers can consider the critical role of binding in perception, the role of attention in scene perception, as well as the influence of cognitive load, memory, reflection, and perceptual learning on early and late processing. They will see how methods to study conscious perceptual awareness have evolved over the years.

This volume constitutes the proceedings of the 11th International Conference on Augmented Cognition, AC 2017, held as part of the International Conference on Human-Computer Interaction, HCII 2017, which took place in Vancouver, BC, Canada, in July 2017. HCII 2017 received a total of 4340 submissions, of which 1228 papers were accepted for publication after a careful reviewing process. The papers thoroughly cover the entire field of Human-Computer Interaction, addressing major advances in knowledge and effective use of computers in a variety of application areas. The two volumes set of AC 2017 presents 81 papers which are organized in the following topical sections: electroencephalography and brain activity measurement, eye tracking in augmented cognition, physiological measuring and bio-sensing, machine learning in augmented cognition, cognitive load and performance, adaptive learning systems, brain-computer interfaces, human cognition and behavior in complex tasks and environments.

Featuring contributions from world-leading researchers, this book explores the relationship between visual perception and memory. It bridges the traditionally separate fields of vision science and recognition memory and deals with an interdisciplinary set of perspectives combining research in psychology, neuroscience, and artificial intelligence. The book makes new connections between the wealth of research from each respective field, developing the idea that visuospatial memory is our best memory system. This volume traverses topics grounded in both empirical study and real-world applications, including working (short-term) memory, long-term memory, the neuroscience of memory, development of memory over the lifespan, autobiographical memories, false memories, and eyewitness testimony. It argues that an increased knowledge of how visuospatial memory works can lead to an improved understanding of the basic features of memory, as well as providing strategies for memory improvement. The book features cutting edge visual memory research, where converging methods in psychophysics, cognitive neuroscience, and computational modeling have been propelling the field forward. Visual Memory is an essential read for all students and researchers of memory and visual perception. It will also be useful for researchers and students in related fields including human-computer interaction, data visualization, cognitive science, and cognitive enhancement.