Perception as Bayesian Inference

Perception as Bayesian Inference PDF Author: David C. Knill
Publisher: Cambridge University Press
ISBN: 9780521461092
Category : Computers
Languages : en
Pages : 534

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Book Description
This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.

Perception as Bayesian Inference

Perception as Bayesian Inference PDF Author: David C. Knill
Publisher: Cambridge University Press
ISBN: 9780521461092
Category : Computers
Languages : en
Pages : 534

Get Book Here

Book Description
This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.

Bayesian Models of Perception and Action

Bayesian Models of Perception and Action PDF Author: Wei Ji Ma
Publisher: MIT Press
ISBN: 0262047594
Category : Science
Languages : en
Pages : 409

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Book Description
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

Perception as Bayesian Inference

Perception as Bayesian Inference PDF Author: Adam Binch
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description


The Rationality of Perception

The Rationality of Perception PDF Author: Susanna Siegel
Publisher: Oxford University Press
ISBN: 0198797087
Category : Philosophy
Languages : en
Pages : 248

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Book Description
One of the most important divisions in the human mind is between perception and reasoning. We reason from information that we take ourselves to have already, but perception is a means of taking in new information. Reasoning can be better or worse, but perception is considered beyond reproach. The Rationality of Perception argues that these two aspects of the mind become deeply intertwined when beliefs, fears, desires, or prejudice influence what weperceive. When the influences reach all the way to perceptual appearances, we face a philosophical problem: is it reasonable to strengthen what one believes or fears or suspects on the basis of an experience that wasgenerated by those very same beliefs, fears, or suspicions? Drawing on examples involving racism, emotion, and scientific theories, Siegel argues that perception itself can be rational or irrational, and makes vivid the relationship between perception and culture.

Investigating the Role of Bayesian Inference in Duration Perception

Investigating the Role of Bayesian Inference in Duration Perception PDF Author: Reny Baykova
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Book Description


Inference and Consciousness

Inference and Consciousness PDF Author: Timothy Chan
Publisher: Routledge
ISBN: 1351366734
Category : Philosophy
Languages : en
Pages : 251

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Book Description
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.

Perception and the Physical World

Perception and the Physical World PDF Author: Dieter Heyer
Publisher: John Wiley & Sons
ISBN:
Category : Medical
Languages : en
Pages : 360

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Book Description
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.

Bayesian Brain

Bayesian Brain PDF Author: Kenji Doya
Publisher: MIT Press
ISBN: 026204238X
Category : Bayesian statistical decision theory
Languages : en
Pages : 341

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Book Description
Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

Probabilistic Models of the Brain

Probabilistic Models of the Brain PDF Author: Rajesh P.N. Rao
Publisher: MIT Press
ISBN: 9780262264327
Category : Medical
Languages : en
Pages : 348

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Book Description
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.

Shape Perception as Bayesian Inference of Modality-independent Part-based 3D Object-centered Shape Representations

Shape Perception as Bayesian Inference of Modality-independent Part-based 3D Object-centered Shape Representations PDF Author: Goker Erdogan
Publisher:
ISBN:
Category : Form perception
Languages : en
Pages : 211

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Book Description
"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.