Modeling Psychophysical Data In R
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Modeling Psychophysical Data in R
Author: Kenneth Knoblauch
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-09-02
Many of the commonly used methods for modeling and fitting psychophysical data are special cases of statistical procedures of great power and generality, notably the Generalized Linear Model (GLM). This book illustrates how to fit data from a variety of psychophysical paradigms using modern statistical methods and the statistical language R. The paradigms include signal detection theory, psychometric function fitting, classification images and more. In two chapters, recently developed methods for scaling appearance, maximum likelihood difference scaling and maximum likelihood conjoint measurement are examined. The authors also consider the application of mixed-effects models to psychophysical data. R is an open-source programming language that is widely used by statisticians and is seeing enormous growth in its application to data in all fields. It is interactive, containing many powerful facilities for optimization, model evaluation, model selection, and graphical display of data. The reader who fits data in R can readily make use of these methods. The researcher who uses R to fit and model his data has access to most recently developed statistical methods. This book does not assume that the reader is familiar with R, and a little experience with any programming language is all that is needed to appreciate this book. There are large numbers of examples of R in the text and the source code for all examples is available in an R package MPDiR available through R. Kenneth Knoblauch is a researcher in the Department of Integrative Neurosciences in Inserm Unit 846, The Stem Cell and Brain Research Institute and associated with the University Claude Bernard, Lyon 1, in France. Laurence T. Maloney is Professor of Psychology and Neural Science at New York University. His research focusses on applications of mathematical models to perception, motor control and decision making.
Novel Applications of Bayesian and Other Models in Translational Neuroscience
Author: Reza Rastmanesh
language: en
Publisher: Frontiers Media SA
Release Date: 2024-05-06
It has been proposed that the brain works in a Bayesian manner, and based on the free-energy principle, the brain's main function is to reduce environmental uncertainty; this is a proposed model as a universal principle governing adaptive brain function and structure. There are many pathophysiological, and clinical observations that can be easily explained by predictive Bayesian brain models. However, the novel applications of Bayesian models in translational neuroscience has been understudied and underreported. For example, variational Bayesian mixed-effects inference has been successfully tested for classification studies. A multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions has been recently publishe
Psychophysics
Author: Frederick A.A. Kingdom
language: en
Publisher: Academic Press
Release Date: 2016-01-04
Psychophysics: A Practical Introduction, Second Edition, is the primary scientific tool for understanding how the physical world of colors, sounds, odors, movements, and shapes translates into the sensory world of sight, hearing, touch, taste, and smell; in other words, how matter translates into mind. This timely revision provides a unique introduction to the techniques for researching and understanding how the brain translates the external physical world to the internal world of sensation. The revision expands and refines coverage of the basic tools of psychophysics research and better integrates the theory with the supporting software. The new edition continues to be the only book to combine, in a single volume, the principles underlying the science of psychophysical measurement and the practical tools necessary to analyze data from psychophysical experiments. The book, written in a tutorial style, will appeal to new researchers as well as to seasoned veterans. This introduction to psychophysics research methods will be of interest to students, scholars and researchers within sensory neuroscience, vision research, behavioral neuroscience, and the cognitive sciences. - Presents a large variety of analytical methods explained for the non-expert - Provides a novel classification scheme for psychophysics experiments - Disseminates the pros and cons of different psychophysical procedures - Contains practical tips for designing psychophysical experiments