Bayesian Cognitive Modeling

Download or read book Bayesian Cognitive Modeling written by Michael D. Lee and published by Cambridge University Press. This book was released on 2014-04-03 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R), with additional support available online. No advance knowledge of statistics is required and, from the very start, readers are encouraged to apply and adjust Bayesian analyses by themselves. The book contains a series of chapters on parameter estimation and model selection, followed by detailed case studies from cognitive science. After working through this book, readers should be able to build their own Bayesian models, apply the models to their own data, and draw their own conclusions.

Download or read book Bayesian Cognitive Modeling written by Michael D. Lee and published by Cambridge University Press. This book was released on 2013 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using a practical, hands-on approach, this book will teach anyone how to carry out Bayesian analyses and interpret the results.

Download or read book Introduction to Modeling Cognitive Processes written by Tom Verguts and published by MIT Press. This book was released on 2022-02-08 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and Bayesian models; and models of social interaction. All mathematical concepts are introduced gradually, with no background in advanced topics required. Hints and solutions for exercises and a glossary follow the main text. All code in the book is Python, with the Spyder editor in the Anaconda environment. A GitHub repository with Python files enables readers to access the computer code used and start programming themselves. The book is suitable as an introduction to modeling cognitive processes for students across a range of disciplines and as a reference for researchers interested in a broad overview.

Download or read book Computational Modeling of Cognition and Behavior written by Simon Farrell and published by Cambridge University Press. This book was released on 2018-02-22 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an integrated framework for developing and testing computational models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models.

Download or read book Computational Cognitive Modeling and Linguistic Theory written by Adrian Brasoveanu and published by Springer Nature. This book was released on 2020-01-01 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit them to real-time experimental data. This computational cognitive modeling approach opens up exciting new directions for research in formal semantics, and linguistics more generally, and offers new ways of (re)connecting semantics and the broader field of cognitive science. The approach of this book is novel in more ways than one. Assuming the mental architecture and procedural modalities of Anderson's ACT-R framework, it presents fine-grained computational models of human language processing tasks which make detailed quantitative predictions that can be checked against the results of self-paced reading and other psycho-linguistic experiments. All models are presented as computer programs that readers can run on their own computer and on inputs of their choice, thereby learning to design, program and run their own models. But even for readers who won't do all that, the book will show how such detailed, quantitatively predicting modeling of linguistic processes is possible. A methodological breakthrough and a must for anyone concerned about the future of linguistics! (Hans Kamp) This book constitutes a major step forward in linguistics and psycholinguistics. It constitutes a unique synthesis of several different research traditions: computational models of psycholinguistic processes, and formal models of semantics and discourse processing. The work also introduces a sophisticated python-based software environment for modeling linguistic processes. This book has the potential to revolutionize not only formal models of linguistics, but also models of language processing more generally. (Shravan Vasishth) .

Download or read book Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Set written by John T. Wixted and published by Wiley. This book was released on 2018-04-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the first edition was published in 1951, The Stevens' Handbook of Experimental Psychology has been recognized as the standard reference in the field. The most recent (3rd) edition of the handbook was published in 2004, and it was a success by any measure. But the field of experimental psychology has changed in dramatic ways since then. Throughout the first 3 editions of the handbook, the changes in the field were mainly quantitative in nature. That is, the size and scope of the field grew steadily from 1951 to 2004, a trend that was reflected in the growing size of the handbook itself: the 1-volume first edition (1951) was succeeded by a 2-volume second edition (1988) and then by a 4-volume third edition (2004). Since 2004, however, this still-growing field has also changed qualitatively in the sense that, in virtually every subdomain of experimental psychology, theories of the mind have evolved into theories of the brain. Research methods in experimental psychology have changed accordingly and now include not only venerable EEG recordings (long a staple of research in psycholinguistics) but also MEG, fMRI, TMS, and single-unit recording. The trend towards neuroscience is an absolutely dramatic, worldwide phenomenon that is unlikely to ever be reversed. Thus, the era of purely behavioral experimental psychology is already long gone, even though not everyone has noticed. Experimental psychology and "cognitive neuroscience" (an umbrella term that includes behavioral neuroscience, social neuroscience and developmental neuroscience) are now inextricably intertwined. Nearly every major psychology department in the country has added cognitive neuroscientists to its ranks in recent years, and that trend is still growing. A viable handbook of experimental psychology should reflect the new reality on the ground. There is no handbook in existence today that combines basic experimental psychology and cognitive neuroscience, this despite the fact that the two fields are interrelated – and even interdependent – because they are concerned with the same issues (e.g., memory, perception, language, development, etc.). Almost all neuroscience-oriented research takes as its starting point what has been learned using behavioral methods in experimental psychology. In addition, nowadays, psychological theories increasingly take into account what has been learned about the brain (e.g., psychological models increasingly need to be neurologically plausible). These considerations explain why this edition of: The Stevens' Handbook of Experimental Psychology is now called The Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience. The title serves as a reminder that the two fields go together and as an announcement that the Stevens' Handbook covers it all. The 4th edition of the Stevens’ Handbook is a 5-volume set structured as follows: I. Learning & Memory: Elizabeth Phelps & Lila Davachi (Volume Editors) Topics include fear learning; time perception; working memory; visual object recognition; memory and future imagining; sleep and memory; emotion and memory; attention and memory; motivation and memory; inhibition in memory; education and memory; aging and memory; autobiographical memory; eyewitness memory; and category learning. II. Sensation, Perception & Attention: John Serences (Volume Editor) Topics include attention; vision; color vision; visual search; depth perception; taste; touch; olfaction; motor control; perceptual learning; audition; music perception; multisensory integration; vestibular, proprioceptive, and haptic contributions to spatial orientation; motion perception; perceptual rhythms; the interface theory of perception; perceptual organization; perception and interactive technology; perception for action. III. Language & Thought: Sharon Thompson-Schill (Volume Editor) Topics include reading; discourse and dialogue; speech production; sentence processing; bilingualism; concepts and categorization; culture and cognition; embodied cognition; creativity; reasoning; speech perception; spatial cognition; word processing; semantic memory; moral reasoning. IV. Developmental & Social Psychology: Simona Ghetti (Volume Editor) Topics include development of visual attention; self-evaluation; moral development; emotion-cognition interactions; person perception; memory; implicit social cognition; motivation group processes; development of scientific thinking; language acquisition; category and conceptual development; development of mathematical reasoning; emotion regulation; emotional development; development of theory of mind; attitudes; executive function. V. Methodology: E. J. Wagenmakers (Volume Editor) Topics include hypothesis testing and statistical inference; model comparison in psychology; mathematical modeling in cognition and cognitive neuroscience; methods and models in categorization; serial versus parallel processing; theories for discriminating signal from noise; Bayesian cognitive modeling; response time modeling; neural networks and neurocomputational modeling; methods in psychophysics analyzing neural time series data; convergent methods of memory research; models and methods for reinforcement learning; cultural consensus theory; network models for clinical psychology; the stop-signal paradigm; fmri; neural recordings; open science.

Download or read book Bayesian Modeling and Computation in Python written by Osvaldo A. Martin and published by CRC Press. This book was released on 2021-12-28 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling and Computation in Python aims to help beginner Bayesian practitioners to become intermediate modelers. It uses a hands on approach with PyMC3, Tensorflow Probability, ArviZ and other libraries focusing on the practice of applied statistics with references to the underlying mathematical theory. The book starts with a refresher of the Bayesian Inference concepts. The second chapter introduces modern methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various models including linear regressions, splines, time series, Bayesian additive regression trees. The final chapters include Approximate Bayesian Computation, end to end case studies showing how to apply Bayesian modelling in different settings, and a chapter about the internals of probabilistic programming languages. Finally the last chapter serves as a reference for the rest of the book by getting closer into mathematical aspects or by extending the discussion of certain topics. This book is written by contributors of PyMC3, ArviZ, Bambi, and Tensorflow Probability among other libraries.