Laboratory research on human attention has often been conducted under conditions that bear little resemblance to the complexity of our everyday lives. Although this research has yielded interesting discoveries, few scholars have truly connected these findings to natural experiences. This book bridges the gap between “laboratory and life” by bringing together cutting-edge research using traditional methodologies with research that focuses on attention in everyday contexts.
Emergence—the formation of global patterns from solely local interactions—is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames—phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning)—underlie the emergence of cognition.
The vast differences between the brain’s neural circuitry and a computer’s silicon circuitry might suggest that they have nothing in common. In fact, as Dana Ballard argues in this book, computational tools are essential for understanding brain function. Ballard shows that the hierarchical organization of the brain has many parallels with the hierarchical organization of computing; as in silicon computing, the complexities of brain computation can be dramatically simplified when its computation is factored into different levels of abstraction.
The event-related potential (ERP) technique, in which neural responses to specific events are extracted from the EEG, provides a powerful noninvasive tool for exploring the human brain. This volume describes practical methods for ERP research along with the underlying theoretical rationale. It offers researchers and students an essential guide to designing, conducting, and analyzing ERP experiments. This second edition has been completely updated, with additional material, new chapters, and more accessible explanations.
This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals.
Vision is one of the most active areas in biomedical research, and visual psychophysical techniques are a foundational methodology for this research enterprise. Visual psychophysics, which studies the relationship between the physical world and human behavior, is a classical field of study that has widespread applications in modern vision science. Bridging the gap between theory and practice, this textbook provides a comprehensive treatment of visual psychophysics, teaching not only basic techniques but also sophisticated data analysis methodologies and theoretical approaches.
Crucial to understanding how the brain works is connectivity, and the centerpiece of brain connectivity is the connectome, a comprehensive description of how neurons and brain regions are connected. The human brain is a network of extraordinary complexity—a network not by way of metaphor, but in a precise and mathematical sense: an intricate web of billions of neurons connected by trillions of synapses. How this network is connected is important for virtually all facets of the brain’s integrative function.
The consideration of time or dynamics is fundamental for all aspects of mental activity—perception, cognition, and emotion—because the main feature of brain activity is the continuous change of the underlying brain states even in a constant environment. The application of nonlinear dynamics to the study of brain activity began to flourish in the 1990s when combined with empirical observations from modern morphological and physiological observations. This book offers perspectives on brain dynamics that draw on the latest advances in research in the field.
Over the past sixty years, powerful methods of model-based control engineering have been responsible for such dramatic advances in engineering systems as autolanding aircraft, autonomous vehicles, and even weather forecasting. Over those same decades, our models of the nervous system have evolved from single-cell membranes to neuronal networks to large-scale models of the human brain. Yet until recently control theory was completely inapplicable to the types of nonlinear models being developed in neuroscience.
The results of fMRI brain scanning require extensive analysis in the laboratory. In Handling Digital Brains, Morana Alač shows that fMRI researchers do not sit passively staring at computer screens but actively involve their bodies in laboratory practice. Discussing fMRI visuals with colleagues, scientists animate the scans with gestures, and talk as they work with computers. Alač argues that to understand how digital scientific visuals take on meaning we must consider their dynamic coordination with gesture, speech, and working hands.