Software Simulation and Modeling in Psychology: MATLAB, SPSS, Excel and E-Prime describes all the stages of psychology experimentation, from the manipulation of factors, to statistical analysis, data modeling, and automated stimuli creation. The book shows how software can help automate various stages of the experiment for which operations may quickly become repetitive. For example, it shows how to compile data files (instead of opening files one by one to copy and paste), generate stimuli (instead of drawing one by one in a drawing software), and transform and recode tables of data. This type of modeling in psychology helps determine if a model fits the data, and also demonstrates that the algorithmic is not only useful, but essential for modeling data. - Covers the entire process of experimenting, from designing an experiment, to modeling the data - Shows how software can help automate various stages of the experiment for which operations may quickly become repetitive - Contains sections on how to compile data files (instead of opening files one by one to copy and paste) and generate stimuli (instead of drawing one by one in a drawing software)

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.

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.

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning.

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) .

An accessible introduction to the principles of computational and mathematical modeling in psychology and cognitive science This practical and readable work provides students and researchers, who are new to cognitive modeling, with the background and core knowledge they need to interpret published reports, and develop and apply models of their own. The book is structured to help readers understand the logic of individual component techniques and their relationships to each other.

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.

Modeling Human Behavior With Integrated Cognitive Architectures summarizes the results of four years of collaborative research within the Air Force Research Laboratory and the Office of Naval Research.

An inside view of the experimental practices of cognitive psychology—and their influence on the addictive nature of social media Experimental cognitive psychology research is a hidden force in our online lives. We engage with it, often unknowingly, whenever we download a health app, complete a Facebook quiz, or rate our latest purchase. How did experimental psychology come to play an outsized role in these developments? Experiments of the Mind considers this question through a look at cognitive psychology laboratories. Emily Martin traces how psychological research methods evolved, escaped the boundaries of the discipline, and infiltrated social media and our digital universe. Martin recounts her participation in psychology labs, and she conveys their activities through the voices of principal investigators, graduate students, and subjects. Despite claims of experimental psychology’s focus on isolated individuals, Martin finds that the history of the field—from early German labs to Gestalt psychology—has led to research methods that are, in fact, highly social. She shows how these methods are deployed online: amplified by troves of data and powerful machine learning, an unprecedented model of human psychology is now widespread—one in which statistical measures are paired with algorithms to predict and influence users’ behavior. Experiments of the Mind examines how psychology research has shaped us to be perfectly suited for our networked age.

Biomedicine has become one of the best-modeled domains from several perspectives - artificial intelligence, psychology, and the social sciences; yet few studies have combined these points of view. In this book, the interdisciplinary strengths of cognitive science offer fresh insights into biomedical problem solving. Cognitive Science in Medicine presents current research that focuses on issues and results in applying techniques from cognitive science to problems in biomedicine. It includes material by researchers who have worked in both areas and is unique in linking models of physician knowledge with models of physician behavior. David Evans discusses issues of cognitive science in medicine in his introduction; and in a chapter with Cindy Gadd and Harry Pople, deals with the problem of managing coherence and context in medical problem-solving discourse. Vimla Patel, Evans, and Guy Groen provide experimental data that illuminates the role of biomedical knowledge in clinical reasoning; and Patel, Evans, and David Kaufman offer a cognitive science framework for analysis of clinical interviews. Other contributors and subjects include Clark Glymour on the empirical and representational issues in cognitive and medical science; Alan Lesgold on multilevel models of expertise; Arthur Elstein, James Dodd, and Gerald B. Holzman on the analysis of estrogen replacement decisions among residents; Kenneth R. Hammond, Elizabeth Frederick, Nichole Robillard, and Doreen Victor on the features of the student-teacher dialog in medicine; Naomi Rodolitz and William J. Clancey on tutoring for strategic knowledge; Paul J. Feltovich, Rand J. Spiro, and Richard L. Coulson on the foundations of misunderstanding in established medical knowledge; John K. Vries, Evans, and Peretz Shoval on the development of semantic networks for medical information retrieval; and John Bruer, with a preface on the implications of cognitive-scientific studies for medical education. David A. Evans is Assistant Professor of Linguistics and Computer Science at Carnegie-Mellon University and Vimla L. Patel is Associate Professor of Medicine and Educational Psychology at McGill University. A Bradford Book.