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The Probabilistic Mind by Nick Chater,Mike Oaksford Pdf
The Probabilistic Mind is a follow-up to the influential and highly cited Rational Models of Cognition (OUP, 1998). It brings together developmetns in understanding how, and how far, high-level cognitive processes can be understood in rational terms, and particularly using probabilistic Bayesian methods.
Bayesian Rationality by Mike Oaksford,Nick Chater Pdf
For almost 2,500 years, the Western concept of what is to be human has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. In this text a more radical suggestion for explaining these puzzling aspects of human reasoning is put forward.
Traditional philosophical discussions of knowledge have focused on the epistemic status of full beliefs. Sarah Moss argues that in addition to full beliefs, credences can constitute knowledge. For instance, your 0.4 credence that it is raining outside can constitute knowledge, in just the same way that your full beliefs can. In addition, you can know that it might be raining, and that if it is raining then it is probably cloudy, where this knowledge is not knowledge of propositions, but of probabilistic contents. The notion of probabilistic content introduced in this book plays a central role not only in epistemology, but in the philosophy of mind and language as well. Just as tradition holds that you believe and assert propositions, you can believe and assert probabilistic contents. Accepting that we can believe, assert, and know probabilistic contents has significant consequences for many philosophical debates, including debates about the relationship between full belief and credence, the semantics of epistemic modals and conditionals, the contents of perceptual experience, peer disagreement, pragmatic encroachment, perceptual dogmatism, and transformative experience. In addition, accepting probabilistic knowledge can help us discredit negative evaluations of female speech, explain why merely statistical evidence is insufficient for legal proof, and identify epistemic norms violated by acts of racial profiling. Hence the central theses of this book not only help us better understand the nature of our own mental states, but also help us better understand the nature of our responsibilities to each other.
Lack of ability to think probabilistically makes one prone to a variety of irrational fears and vulnerable to scams designed to exploit probabilistic naiveté, impairs decision making under uncertainty, facilitates the misinterpretation of statistical information, and precludes critical evaluation of likelihood claims. Cognition and Chance presents an overview of the information needed to avoid such pitfalls and to assess and respond to probabilistic situations in a rational way. Dr. Nickerson investigates such questions as how good individuals are at thinking probabilistically and how consistent their reasoning under uncertainty is with principles of mathematical statistics and probability theory. He reviews evidence that has been produced in researchers' attempts to investigate these and similar types of questions. Seven conceptual chapters address such topics as probability, chance, randomness, coincidences, inverse probability, paradoxes, dilemmas, and statistics. The remaining five chapters focus on empirical studies of individuals' abilities and limitations as probabilistic thinkers. Topics include estimation and prediction, perception of covariation, choice under uncertainty, and people as intuitive probabilists. Cognition and Chance is intended to appeal to researchers and students in the areas of probability, statistics, psychology, business, economics, decision theory, and social dilemmas.
Degrees of Freedom Uncertainty This is the degrees of freedom uncertainty rule [which actually allows us freedom]. We can never be sure which individual went this way and which went the other way [that is what entropy and Carnot’s ‘jinks’ on Maxwell’s demons is all about]. This is a statistical population; there are enough members to apply the statistical rule [the rule of large numbers]. That is the same rule [just inverted] as the degrees of freedom uncertainty principle [which says that you cannot specify Newtonian activity on populations that provide excellent statistical results because of the same theory of large numbers. - You can’t have your cake and eat it too [precisely what Carnoy meant]. Also, the difficulties with this rule could be resolved easily; by applying the viewpoint of harmonics. So, under the degrees of freedom uncertainty [when that applies {strongly enough}] you have harmonics. This is the fact that systems under the rule of degrees of freedom uncertainty and that are constrained [in certain natural or “harmonics” ways.] can form “natural” patterns. Harmonics [the name] refers to the patterns since they form in harmonic kine [a set of eigenfunctions]. The pattern does not specify where any part [molecule] is at or how fast it is going. The pattern is an envelope of probability distribution for the randomly distributed contents. This does not allow Maxwell's Demons to sneak some particles into a special place to violate equilibrium rules. Demythologizing Jung Demythologizing and deconstruction is the territory of the post-structuralist. But reconstruction should be the goal of such endeavors. Here the deconstruction of Jung's archetypes is reconstructed into a meaningful, workable, and useful concept of how the mind works. This effort is about the mind and the algorithms that the mind uses to process information. In the brain, pictures are a very important part of the information processing; but computer processing is approaching that state now as well. Here the mind is the program. That mind can use different algorithms in its programming to solve its “problems”. Recognizing these algorithms is our desire for this study. I start with Jung’s Archetype algorithms and proceed to expand that into a more complete recognition of mental algorithms. The process of understanding conversation is to compare the text of a sentence with contextual information we have. The question is: “How do we store and retrieve the context in our grammar?” It is not stored using relational algebra, which is the method we use to store computer database data for efficient computer store and retrieve mechanisms. Relational data storage is not fast enough and it is not broad enough in its combinatorial strength to explain the minds process. The mind has a way of producing mental objects out of the interpretation of external information. A fresh encounter with the outer world is analyzed by a neural network. The information is carried by nerves from the sensing point. These nerve signals are then filtered through neural networks. The archetype [Jung] for that area of mental processing is the link with the conscious. From this link, a memory object can be extended from the archetype (as base class). Then the extended archetype layer becomes the output layer of the neural network. Note the archetype layer serves both as the interpretation function determining layer (how the input is interpreted) and, in the instantiation of the object from the base class extended to a memory object from (based on the neural interpretation). This is a probabilistic process that is under constraints. The process is probabilistic but the constraints provide limitations so the result that is controlled by these limitations produces a meaningful pattern. Thus the constraints prevent dissipation, and encourage meaningful results. The constraints in the young child are the archetypes. As we grow older our minds develop aggregate (abstract) classes that are useful as though they were archetypes. These archetypes and aggregates constrain the mental process so that meaningful patterns result from the interpretation process. The features of the archetypal classes, relating to the attributes and methods of a class, are then the similar to the neural network activation functions. With input (our nerves send these signals about our present context) these features are used to interpret the signals (our internal program adapts them to interpretation of the input signals). When applied to a memory object in our conscious mind, the features (activation functions) are used in a way that they make the memory object useful and meaningful in our thought process. Remember the class here is a (hidden) layer of the neural network not a single node. Also an abstract class can be extended into a memory object (as a real [visible] class). (Also see books by Dr. Jerome Heath: https://sites.google.com/site/jbhcontextcalculus/)
The Probabilistic Foundations of Rational Learning by Simon M. Huttegger Pdf
According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences.
Philosophy of Mind: A Contemporary Introduction by John Heil Pdf
This comprehensive textbook, written by a leading author in the field, provides a survey of mainstream conceptions of the nature of mind accessible to readers with little or no background in philosophy. Included are the dualist, behaviourist, and functionalist accounts of the nature of mind, along with a critical assessment of recent trends in the subject. The problem of consciousness, widely thought to be the chief roadblock to our understanding of the mind, is addressed throughout the book and there is also material to interest those with a professional interest in the topic - philosophers, psychologists and neuroscientists - as well as the general reader. Unique features of Philosophy of Mind: * provides a comprehensive survey of basic concepts and major theories * contains many lucid examples to support ideas * cites key literature in annotated suggested reading and a full bibliography * contains a full index including the location of key terms and concepts.
The Great Mental Models, Volume 1 by Shane Parrish,Rhiannon Beaubien Pdf
Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.
This book provides a comprehensive examination of how theory of mind develops. Building on previous work, this book pulls together' all that we have learned in the past 25 years to make sense of this powerful everyday theory. This book includes chapters on evolution and the brain bases of theory of mind; updated treatments and explanations of theory; infant theory of mind as the platform for developments in later childhood; and later developments from middle childhood to adulthood, including how we understand extraordinary minds such as those that belong to gods, superheroes, or supernatural beings.
Bayesian Brain by Kenji Doya,Shin Ishii,Alexandre Pouget Pdf
Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.
The Intuitive Sources of Probabilistic Thinking in Children by H. Fischbein Pdf
About a year ago I promised my friend Fischbein a preface to his book of which I knew the French manuscript. Now with the printer's proofs under my eyes I like the book even better than I did then, because of, and influenced by, new experiences in the meantime, and fresh thoughts that crossed my mind. Have I been influenced by what I remembered from the manuscript? If so, it must have happened unconsciously. But of course, what struck me in this work a year ago, struck a responsive chord in my own mind. In the past, mathematics teaching theory has strongly been influenced by a view on mathematics as a heap of concepts, and on learning mathematics as concepts attainment. Mathematics teaching practice has been jeopardised by this theoretical approach, which in its most dangerous form expresses itself as a radical atomism. To concepts attainment Fischbein opposes acquisition of intuitions. In my own publications I avoided the word "intuition" because of the variety of its meanings across languages. For some time I have used the term "constitution of mathematical objects", which I think means the same as Fischbein's "acquisition of intuitions" - indeed as I view it, constituting a mental object precedes its conceptualising, and under this viewpoint I tried to observe mathematical activities of young children.
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics by Christine Sinoquet,Raphaël Mourad Pdf
Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Bayesian Methods for Hackers by Cameron Davidson-Pilon Pdf
Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes • Learning the Bayesian “state of mind” and its practical implications • Understanding how computers perform Bayesian inference • Using the PyMC Python library to program Bayesian analyses • Building and debugging models with PyMC • Testing your model’s “goodness of fit” • Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works • Leveraging the power of the “Law of Large Numbers” • Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning • Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes • Selecting appropriate priors and understanding how their influence changes with dataset size • Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough • Using Bayesian inference to improve A/B testing • Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
It's hard to conceive of a topic of more broad and personal interest than the study of the mind. In addition to its traditional investigation by the disciplines of psychology, psychiatry, and neuroscience, the mind has also been a focus of study in the fields of philosophy, economics, anthropology, linguistics, computer science, molecular biology, education, and literature. In all these approaches, there is an almost universal fascination with how the mind works and how it affects our lives and our behavior. Studies of the mind and brain have crossed many exciting thresholds in recent years, and the study of mind now represents a thoroughly cross-disciplinary effort. Researchers from a wide range of disciplines seek answers to such questions as: What is mind? How does it operate? What is consciousness? This encyclopedia brings together scholars from the entire range of mind-related academic disciplines from across the arts and humanities, social sciences, life sciences, and computer science and engineering to explore the multidimensional nature of the human mind.