Inference on the Low Level
Inference on the Low Level

Author: Hannes Leitgeb

Publisher: Springer Science & Business Media

Published: 2012-11-02

Total Pages: 376

ISBN-13: 1402028067

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In contrast to the prevailing tradition in epistemology, the focus in this book is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences. Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.

Statistical Inference as Severe Testing
Statistical Inference as Severe Testing

Author: Deborah G. Mayo

Publisher: Cambridge University Press

Published: 2018-09-20

Total Pages: 503

ISBN-13: 1108563309

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Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Diagrammatic Representation and Inference
Diagrammatic Representation and Inference

Author: Amrita Basu

Publisher: Springer Nature

Published: 2021-09-21

Total Pages: 570

ISBN-13: 3030860620

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This book constitutes the refereed proceedings of the 12th International Conference on the Theory and Application of Diagrams, Diagrams 2021, held virtually in September 2021. The 16 full papers and 25 short papers presented together with 16 posters were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: design of concrete diagrams; theory of diagrams; diagrams and mathematics; diagrams and logic; new representation systems; analysis of diagrams; diagrams and computation; cognitive analysis; diagrams as structural tools; formal diagrams; and understanding thought processes. 10 chapters are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Robotics Research
Robotics Research

Author: Sebastian Thrun

Publisher: Springer Science & Business Media

Published: 2007-02-05

Total Pages: 582

ISBN-13: 3540481109

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This volume contains 50 papers presented at the 12th International Symposium of Robotics Research, which took place October 2005 in San Francisco, CA. Coverage includes: physical human-robot interaction, humanoids, mechanisms and design, simultaneous localization and mapping, field robots, robotic vision, robot design and control, underwater robotics, learning and adaptive behavior, networked robotics, and interfaces and interaction.

Neural Networks for Knowledge Representation and Inference
Neural Networks for Knowledge Representation and Inference

Author: Daniel S. Levine

Publisher: Psychology Press

Published: 2013-04-15

Total Pages: 526

ISBN-13: 1134771614

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The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation, Emotion, and Goal Direction in Neural Networks (LEA, 1992) -- this book addresses the controversy between symbolicist artificial intelligence and neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the higher cognitive functions that are more often associated with symbolic approaches. This controversy has a long history, but recently erupted with arguments against the abilities of renewed neural network developments. More broadly than other attempts, the diverse contributions presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the history of assumed epistemologies -- both neural networks and AI -- and include several neurobiological studies of human cognition as a real system to guide the further development of artificial ones. Organized into four major sections, this volume: * outlines the history of the AI/neural network controversy, the strengths and weaknesses of both approaches, and shows the various capabilities such as generalization and discreetness as being along a broad but common continuum; * introduces several explicit, theoretical structures demonstrating the functional equivalences of neurocomputing with the staple objects of computer science and AI, such as sets and graphs; * shows variants on these types of networks that are applied in a variety of spheres, including reasoning from a geographic database, legal decision making, story comprehension, and performing arithmetic operations; * discusses knowledge representation process in living organisms, including evidence from experimental psychology, behavioral neurobiology, and electroencephalographic responses to sensory stimuli.

Computational Models of Brain and Behavior
Computational Models of Brain and Behavior

Author: Ahmed A. Moustafa

Publisher: John Wiley & Sons

Published: 2017-11-13

Total Pages: 586

ISBN-13: 1119159067

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A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

Inference, Explanation, and Other Frustrations
Inference, Explanation, and Other Frustrations

Author: John Earman

Publisher: Univ of California Press

Published: 2023-07-28

Total Pages: 314

ISBN-13: 0520309871

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These provocative essays by leading philosophers of science exemplify and illuminate the contemporary uncertainty and excitement in the field. The papers are rich in new perspectives, and their far-reaching criticisms challenge arguments long prevalent in classic philosophical problems of induction, empiricism, and realism. By turns empirical or analytic, historical or programmatic, confessional or argumentative, the authors' arguments both describe and demonstrate the fact that philosophy of science is in a ferment more intense than at any time since the heyday of logical positivism early in the twentieth century. Contents: “Thoroughly Modern Meno,” Clark Glymour and Kevin Kelly “The Concept of Induction in the Light of the Interrogative Approach to Inquiry,” Jaakko Hintikka “Aristotelian Natures and Modern Experimental Method,” Nancy Cartwright “Genetic Inference: A Reconsideration of “David Hume's Empiricism,” Barbara D. Massey and Gerald J. Massey “Philosophy and the Exact Sciences: Logical Positivism as a Case Study,” Michael Friedman “Language and Interpretation: Philosophical Reflections and Empirical Inquiry,” Noam Chomsky “Constructivism, Realism, and Philosophical Method,” Richard Boyd “Do We Need a Hierarchical Model of Science?” Diderik Batens “Theories of Theories: A View from Cognitive Science,” Richard E. Grandy “Procedural Syntax for Theory Elements,” Joseph D. Sneed “Why Functionalism Didn't Work,” Hilary Putnam “Physicalism,” Hartry Field This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1992.

Analysis and Interpretation of Ethnographic Data
Analysis and Interpretation of Ethnographic Data

Author: Margaret D. LeCompte

Publisher: Rowman Altamira

Published: 2012-09-05

Total Pages: 359

ISBN-13: 0759122083

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This is Book 5 of 7 in the Ethnographer's Toolkit, Second Edition. Treating analysis as both a mechanical and a cognitive process, Book 5 begins by describing why analysis and interpretation of data are necessary. In the first two chapters the book points out the importance of beginning ethnographic analysis in the field, during the earliest stages of data collection, and how to move between induction and deduction, the concrete and the abstract, in a process informed by an emerging and increasingly refined conceptual model. The middle section tackles the challenge of transforming huge piles of text, audio, and visual information into an ethnographic whole through generic and specific coding and quantification of qualitative data, using multiple extended examples. Chapters show how to use computers in analysis of qualitative data and ways to integrate the results of quantitative and qualitative data into a comprehensive picture of a complex whole. Chapter 9 presents a rare and comprehensive description of the statistics regularly used by ethnographers to analyze ethnographic surveys. Chapters 10 and 11 show how researchers create and then fine-tune preliminary results into an integrated whole, display them for multiple audiences, and write them up. The final chapter illustrates how ethnographers can share the meaning of results with local communities and constituents and with other professional researchers. Other books in the set: Book 1: Designing and Conducting Ethnographic Research: An Introduction, Second Edition by Margaret D. LeCompte and Jean J. Schensul 9780759118690 Book 2: Initiating Ethnographic Research: A Mixed Methods Approach by Stephen L. Schensul, Jean J. Schensul, and Margaret D. LeCompte 9780759122017 Book 3: Essential Ethnographic Methods: A Mixed Methods Approach, Second Edition by Jean J. Schensul and Margaret D. LeCompte 9780759122031 Book 4: Specialized Ethnographic Methods: A Mixed Methods Approach edited by Jean J. Schensul and Margaret D. LeCompte 9780759122055 Book 6: Ethics in Ethnography: A Mixed Methods Approach by Margaret D. LeCompte and Jean J. Schensul 9780759122093 Book 7: Ethnography in Action: A Mixed Methods Approach by Jean J. Schensul and Margaret D. LeCompte 9780759122116

Perception as Bayesian Inference
Perception as Bayesian Inference

Author: David C. Knill

Publisher: Cambridge University Press

Published: 1996-09-13

Total Pages: 534

ISBN-13: 9780521461092

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

Social Connectionism
Social Connectionism

Author: Frank Van Overwalle

Publisher: Psychology Press

Published: 2013-03-07

Total Pages: 536

ISBN-13: 1134956134

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Many of our thoughts and decisions occur without us being conscious of them taking place; connectionism attempts to reveal the internal hidden dynamics that drive the thoughts and actions of both individuals and groups. Connectionist modeling is a radically innovative approach to theorising in psychology, and more recently in the field of social psychology. The connectionist perspective interprets human cognition as a dynamic and adaptive system that learns from its own direct experiences or through indirect communication from others. Social Connectionism offers an overview of the most recent theoretical developments of connectionist models in social psychology. The volume is divided into four sections, beginning with an introduction and overview of social connectionism. This is followed by chapters on causal attribution, person and group impression formation, and attitudes. Each chapter is followed by simulation exercises that can be carried out using the FIT simulation program; these guided exercises allow the reader to reproduce published results. Social Connectionism will be invaluable to graduate students and researchers primarily in the field of social psychology, but also in cognitive psychology and connectionist modeling.