The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.

"How does the brain represent our visual environment? Research has revealed brain areas that respond to specific information such as faces and objects, but how a representation of an entire visual scene is formed is still unclear. This thesis explores the idea that scene statistics play an important role in the formation of neural representations of real-world scenes. Scene statistics are statistical regularities resulting from the physical laws that govern our environment. The main hypothesis is that the brain uses these statistical regularities to rapidly categorize scenes. To test this hypothesis, we used an interdisciplinary approach, integrating methods from psychology, neuroscience and informatics (computer vision). In five chapters, we describe a total of 10 experiments in which we tested computational models of scene statistics against human behavioral and brain data obtained with electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI). Our results show that the visual system exhibits strong sensitivity to scene statistics. Focusing in particular on the temporal dynamics of this neural sensitivity, we examined in depth how scene statistics shape neural representations. We demonstrate a potential role for scene statistics in perceived similarity of naturalistic textures, scene gist perception and object recognition. Overall, the results suggest that visual cortex might compute scene statistics in order to estimate the degree of coherence, i.e. the amount of organized structure vs. chaos present in the visual input, and use this information for rapid scene categorization, or to dynamically adjust its processing of other visual information, such as objects."--Samenvatting auteur.

Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.

Knowledge, Concepts and Categories brings together an overview of recent research on concepts and knowledge that abstracts across a variety of specific fields of cognitive psychology. Readers will find data from many different areas: developmental psychology, formal modelling, neuropsychology, connectionism, philosophy, and so on. The book can be divided into three parts. Chapters 1 to 5 each contain a thorough and systematic review of a significant aspect of research on concepts and categories. Chapters 6 to 9 are concerned primarily with issues related to the taxonomy of human knowledge. Finally, Chapters 10 to 12 discuss formal models of categorization and function learning. The purpose of these three chapters is to provide a few examples of current formal modelling of conceptual behaviour. Knowledge, Concepts and Categories will be welcomed by students and researchers in cognitive psychology and related areas as an unusually wide-ranging and authoritative review of an important subfield of psychology.

To achieve the complex task of interpreting what we see, our brains rely on statistical regularities and patterns in visual data. Knowledge of these regularities can also be considerably useful in visual computing disciplines, such as computer vision, computer graphics, and image processing. The field of natural image statistics studies the regularities to exploit their potential and better understand human vision. With numerous color figures throughout, Image Statistics in Visual Computing covers all aspects of natural image statistics, from data collection to analysis to applications in computer graphics, computational photography, image processing, and art. The authors keep the material accessible, providing mathematical definitions where appropriate to help readers understand the transforms that highlight statistical regularities present in images. The book also describes patterns that arise once the images are transformed and gives examples of applications that have successfully used statistical regularities. Numerous references enable readers to easily look up more information about a specific concept or application. A supporting website also offers additional information, including descriptions of various image databases suitable for statistics. Collecting state-of-the-art, interdisciplinary knowledge in one source, this book explores the relation of natural image statistics to human vision and shows how natural image statistics can be applied to visual computing. It encourages readers in both academic and industrial settings to develop novel insights and applications in all disciplines that relate to visual computing.

The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.

Every object in the world has a physical size which is intrinsic to how we interact with it: we pick up small objects like coins with our fingers, we throw footballs and swing tennis rackets, we orient our body to bigger objects like chairs and tables and we navigate with respect to landmarks like fountains and buildings. Here I argue that the size of objects in the world is a basic property of object representation with both behavioral and neural consequences. Specifically, I suggest that objects have a canonical visual size based on their real-world size (Chapter 2), and that we automatically access real-world size information when we recognize an object (Chapter 3). Further, I present evidence that there are neural consequences of realworld size for the large-scale organization of object knowledge in ventral visual cortex (Chapter 4). Specifically, there are regions with differential selectivity for big and small objects, that span from along the dorsal and lateral surfaces of occipito-temporal cortex in a mirrored organization. Finally, I suggest that the empirical findings can be coherently explained by thinking about the experience of an observer situated in a three-dimensional world. This work provides testable predictions about retinal size biases in visual experience, and an approach in which to understand the neural representation of any object in the world.

Children are already learning at birth, and they develop and learn at a rapid pace in their early years. This provides a critical foundation for lifelong progress, and the adults who provide for the care and the education of young children bear a great responsibility for their health, development, and learning. Despite the fact that they share the same objective - to nurture young children and secure their future success - the various practitioners who contribute to the care and the education of children from birth through age 8 are not acknowledged as a workforce unified by the common knowledge and competencies needed to do their jobs well. Transforming the Workforce for Children Birth Through Age 8 explores the science of child development, particularly looking at implications for the professionals who work with children. This report examines the current capacities and practices of the workforce, the settings in which they work, the policies and infrastructure that set qualifications and provide professional learning, and the government agencies and other funders who support and oversee these systems. This book then makes recommendations to improve the quality of professional practice and the practice environment for care and education professionals. These detailed recommendations create a blueprint for action that builds on a unifying foundation of child development and early learning, shared knowledge and competencies for care and education professionals, and principles for effective professional learning. Young children thrive and learn best when they have secure, positive relationships with adults who are knowledgeable about how to support their development and learning and are responsive to their individual progress. Transforming the Workforce for Children Birth Through Age 8 offers guidance on system changes to improve the quality of professional practice, specific actions to improve professional learning systems and workforce development, and research to continue to build the knowledge base in ways that will directly advance and inform future actions. The recommendations of this book provide an opportunity to improve the quality of the care and the education that children receive, and ultimately improve outcomes for children.