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Author: David C. Knill
Publisher: Cambridge University Press
Published: 1996-09-13
Total Pages: 534
ISBN-13: 9780521461092
DOWNLOAD EBOOK →This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.
Author: Wei Ji Ma
Publisher: MIT Press
Published: 2023-08-08
Total Pages: 409
ISBN-13: 0262047594
DOWNLOAD EBOOK →An accessible introduction to constructing and interpreting Bayesian models of perceptual decision-making and action. Many forms of perception and action can be mathematically modeled as probabilistic—or Bayesian—inference, a method used to draw conclusions from uncertain evidence. According to these models, the human mind behaves like a capable data scientist or crime scene investigator when dealing with noisy and ambiguous data. This textbook provides an approachable introduction to constructing and reasoning with probabilistic models of perceptual decision-making and action. Featuring extensive examples and illustrations, Bayesian Models of Perception and Action is the first textbook to teach this widely used computational framework to beginners. Introduces Bayesian models of perception and action, which are central to cognitive science and neuroscience Beginner-friendly pedagogy includes intuitive examples, daily life illustrations, and gradual progression of complex concepts Broad appeal for students across psychology, neuroscience, cognitive science, linguistics, and mathematics Written by leaders in the field of computational approaches to mind and brain
Author: Timothy Chan
Publisher: Routledge
Published: 2019-12-20
Total Pages: 251
ISBN-13: 1351366734
DOWNLOAD EBOOK →Inference has long been a central concern in epistemology, as an essential means by which we extend our knowledge and test our beliefs. Inference is also a key notion in influential psychological accounts of mental capacities, ranging from problem-solving to perception. Consciousness, on the other hand, has arguably been the defining interest of philosophy of mind over recent decades. Comparatively little attention, however, has been devoted to the significance of consciousness for the proper understanding of the nature and role of inference. It is commonly suggested that inference may be either conscious or unconscious. Yet how unified are these various supposed instances of inference? Does either enjoy explanatory priority in relation to the other? In what way, or ways, can an inference be conscious, or fail to be conscious, and how does this matter? This book brings together original essays from established scholars and emerging theorists that showcase how several current debates in epistemology, philosophy of psychology and philosophy of mind can benefit from more reflections on these and related questions about the significance of consciousness for inference.
Author: Dieter Heyer
Publisher: John Wiley & Sons
Published: 2002-05-22
Total Pages: 360
ISBN-13:
DOWNLOAD EBOOK →Perception is a subject of great current interest and one that is is likely to escalate over coming years. The focus of this book is on conceptual and philosophical issues of perception, including the classic notion of unconscious inferences in perception. The book consists of contributions from a group of international researchers who spent a year together as distinguished fellows at the German Centre for Advanced Study.
Author: Susanna Siegel
Publisher: Oxford University Press
Published: 2017
Total Pages: 248
ISBN-13: 0198797087
DOWNLOAD EBOOK →There is an important division in the human mind between perception and reasoning. We reason from information that we have already, but perception is a means of taking in new information. Susanna Siegel argues that these two aspects of the mind become deeply intertwined when beliefs, fears, desires, or prejudice influence what we perceive.
Author: Kenji Doya
Publisher: MIT Press
Published: 2007
Total Pages: 341
ISBN-13: 026204238X
DOWNLOAD EBOOK →Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.
Author: Rajesh P.N. Rao
Publisher: MIT Press
Published: 2002-03-29
Total Pages: 348
ISBN-13: 9780262264327
DOWNLOAD EBOOK →A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.
Author: Goker Erdogan
Publisher:
Published: 2017
Total Pages: 211
ISBN-13:
DOWNLOAD EBOOK →"Shape is a fundamental property of physical objects. It provides crucial information for various critical behaviors from object recognition to motor planning. The fundamental question here for cognitive science is to understand object shape perception, i.e., how our brains extract shape information from sensory stimuli and make use of it. In other words, we want to understand the representations and algorithms our brains use to achieve successful shape perception. This thesis reports a computational theory of shape perception that uses modality-independent, part-based, 3D, object-centered shape representations and frames shape perception as Bayesian inference over such representations. In a series of behavioral, neuroimaging and computational studies reported in the following chapters, we test various aspects of this proposed theory and show that it provides a promising approach to understanding shape perception."--Page xi.
