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Perception as Bayesian Inference
This 1996 book describes an exciting theoretical paradigm for visual perception based on experimental and computational insights.
Bayesian Models of Perception and Action
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
Bayesian Brain
Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.
ECAI 2020
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of ...
Sensory Cue Integration
This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The probabilistic approaches elaborated in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems. Others ask how uncertainty may be represented in the nervous system and used for cue combination. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed
Current Catalog
First multi-year cumulation covers six years: 1965-70.
Origins of Objectivity
Tyler Burge presents a substantial, original study of what it is for individuals to represent the physical world with the most primitive sort of objectivity. By reflecting on the science of perception and related psychological and biological sciences, he gives an account of constitutive conditions for perceiving the physical world, and thus aims to locate origins of representational mind. Origins of Objectivity illuminates several long-standing, central issues in philosophy, and provides a wide-ranging account of relations between human and animal psychologies.
Perceiving in Depth, Volume 3: Other Mechanisms of Depth Perception
The three-volume work Perceiving in Depth is a sequel to Binocular Vision and Stereopsis and to Seeing in Depth, both by Ian P. Howard and Brian J. Rogers. This work is much broader in scope than the previous books and includes mechanisms of depth perception by all senses, including aural, electrosensory organs, and the somatosensory system. Volume 1 reviews sensory coding, psychophysical and analytic procedures, and basic visual mechanisms. Volume 2 reviews stereoscopic vision. Volume 3 reviews all mechanisms of depth perception other than stereoscopic vision. The three volumes are extensively illustrated and referenced and provide the most detailed review of all aspects of perceiving the t...
Advances in Natural Computation
Annotation The three volume set LNCS 3610, LNCS 3611, and LNCS 3612 constitutes the refereed proceedings of the First International Conference on Natural Computation, ICNC 2005, held in Changsha, China, in August 2005 as a joint event in federation with the Second International Conference on Fuzzy Systems and Knowledge Discovery FSKD 2005 (LNAI volumes 3613 and 3614). The program committee selected 313 carefully revised full papers and 189 short papers for presentation in three volumes from 1887 submissions. The first volume includes all the contributions related to learning algorithms and architectures in neural networks, neurodynamics, statistical neural network models and support vector m...
