Toward A Unified View Of The Speed Accuracy Trade Off Behaviour Neurophysiology And Modelling

Everyone is familiar with the speed-accuracy trade-off (SAT). To make good choices, we need to balance the conflicting demands of fast and accurate decision making. After all, hasty decisions often lead to poor choices, but accurate decisions may be useless if they take too long. This notion is intuitive because it reflects a fundamental aspect of cognition: not only do we deliberate over the evidence for decisions, but we can control that deliberative process. This control raises many questions for the study of choice behaviour and executive function. For example, how do we figure out the appropriate balance between speed and accuracy on a given task? How do we impose that balance on our decisions, and what is its neural basis? Researchers have addressed these and related questions for decades, using a variety of methods and offering answers at different levels of abstraction. Given this diverse methodology, our aim is to provide a unified view of the SAT. Extensive analysis of choice behaviour suggests that we make decisions by accumulating evidence until some criterion is reached. Thus, adjusting the criterion controls how long we accumulate evidence and therefore the speed and accuracy of decisions. This simple framework provides the platform for our unified view. In the pages that follow, leading experts in decision neuroscience consider the history of SAT research, strategies for determining the optimal balance between speed and accuracy, conditions under which this seemingly ubiquitous phenomenon breaks down, and the neural mechanisms that may implement the computations of our unifying framework.

An examination of the link between the vigor with which we move and the value that the brain assigns to the goal of the movement. Why do we reflexively run toward people we love, but only walk toward others? In Vigor, Reza Shadmehr and Alaa Ahmed examine the link between how the brain assigns value to things and how it controls our movements. They find that brain regions thought to be principally involved in decision making also affect movement vigor--and that brain regions thought to be principally responsible for movement also bias patterns of decision making.

Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations performed by associated circuits, but then moves on to in-depth examinations on how goal-directed learning interacts with other forms of choice and response selection. This is the only book that embraces the multidisciplinary nature of this area of decision-making, integrating our knowledge of goal-directed decision-making from basic, computational, clinical, and ethology research into a single resource that is invaluable for neuroscientists, psychologists and computer scientists alike. The book presents discussions on the broader field of decision-making and how it has expanded to incorporate ideas related to flexible behaviors, such as cognitive control, economic choice, and Bayesian inference, as well as the influences that motivation, context and cues have on behavior and decision-making. Details the neural circuits functionally involved in goal-directed decision-making and the computations these circuits perform Discusses changes in goal-directed decision-making spurred by development and disorders, and within real-world applications, including social contexts and addiction Synthesizes neuroscience, psychology and computer science research to offer a unique perspective on the central and emerging issues in goal-directed decision-making

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applications Problem-based approach with many examples from neuroscience and cognitive psychology using real data Illustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience

An authoritative, up-to-date survey of the state of the art in cognitive science, written for non-specialists.

Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird. Herbert Simon's classic and influential The Sciences of the Artificial declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. For this third edition, originally published in 1996, Simon added new material that takes into account advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. Simon won the Nobel Prize for Economics in 1978 for his research into the decision-making process within economic organizations and the Turing Award (considered by some the computer science equivalent to the Nobel) with Allen Newell in 1975 for contributions to artificial intelligence, the psychology of human cognition, and list processing. The Sciences of the Artificial distills the essence of Simon's thought accessibly and coherently. This reissue of the third edition makes a pioneering work available to a new audience.

Gregory Bateson was a philosopher, anthropologist, photographer, naturalist, and poet, as well as the husband and collaborator of Margaret Mead. This classic anthology of his major work includes a new Foreword by his daughter, Mary Katherine Bateson. 5 line drawings.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

This book provides insights into the principles of operation of the cerebral cortex. These principles are key to understanding how we, as humans, function. The book includes Appendices on the operation of many of the neuronal networks described in the book, together with simulation software written in Matlab.