Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms explores the theoretical and methodological tools used to investigate fundamental questions central to basic psychological and perceptual processes. Such processes include detection, identification, classification, recognition, and decision-making. This book collects the tools that allow researchers to deal with the pervasive model mimicry problems which exist in standard experimental and theoretical paradigms and includes novel applications to not only basic psychological questions, but also clinical diagnosis and links to neuroscience. Researchers can use this book to begin using the methodology behind SFT and to get an overview of current uses and future directions. The collected developments and applications of SFT allow us to peer inside the human mind and provide strong constraints on psychological theory. - Provides a thorough introduction to the diagnostic tools offered by SFT - Includes a tutorial on applying the method to reaction time data from a variety of different situations - Introduces novel advances for testing the significance of SFT results - Incorporates new measures that allow for the relaxation of the high accuracy criterion - Examines tools to expand the scope of SFT analyses - Applies SFT to a spectrum of different cognitive domains across different sensory modalities

A Handbook of Process Tracing Methods demonstrates how to better understand decision outcomes by studying decision processes, through the introduction of a number of exciting techniques. Decades of research have identified numerous idiosyncrasies in human decision behavior, but some of the most recent advances in the scientific study of decision making involve the development of sophisticated methods for understanding decision process—known as process tracing. In this volume, leading experts discuss the application of these methods and focus on the best practices for using some of the more popular techniques, discussing how to incorporate them into formal decision models. This edition has been expanded and thoroughly updated throughout, and now includes new chapters on mouse tracking, protocol analysis, neurocognitive methods, the measurement of valuation, as well as an overview of important software packages. The volume not only surveys cutting-edge research to illustrate the great variety in process tracing techniques, but also serves as a tutorial for how the novice researcher might implement these methods. A Handbook of Process Tracing Methods will be an essential read for all students and researchers of decision making.

V. Methodology: E. J. Wagenmakers (Volume Editor) Topics covered include methods and models in categorization; cultural consensus theory; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning; convergent methods of memory research; theories for discriminating signal from noise; bayesian cognitive modeling; mathematical modeling in cognition and cognitive neuroscience; the stop-signal paradigm; hypothesis testing and statistical inference; model comparison in psychology; fmri; neural recordings; open science; neural networks and neurocomputational modeling; serial versus parallel processing; methods in psychophysics.

The aim of the book is to present side-by-side representative and cutting-edge samples of work in mathematical psychology and the analytic philosophy with prominent use of mathematical formalisms.

In this two volume festschrift, contributors explore the theoretical developments (Volume I) and applications (Volume II) in traditional cognitive psychology domains, and model other areas of human performance that benefit from rigorous mathematical approaches. It brings together former classmates, students and colleagues of Dr. James T. Townsend, a pioneering researcher in the field since the early 1960s, to provide a current overview of mathematical modeling in psychology. Townsend’s research critically emphasized a need for rigor in the practice of cognitive modeling, and for providing mathematical definition and structure to ill-defined psychological topics. The research captured demonstrates how the interplay of theory and application, bridged by rigorous mathematics, can move cognitive modeling forward.

To deal with the abundant amount of information in the environment in order to achieve our goals, human beings adopt a strategy to accumulate some information and filter out other information to ultimately make decisions. Since the development of cognitive science in the 1960s, researchers have been interested in understanding how human beings process and accumulate information for decision-making. Researchers have conducted extensive behavioral studies and applied a wide range of modeling tools to study human behavior in simple-detection tasks and two-choice decision tasks (e.g., discrimination, classification). In general, researchers often assume that the manner in which information is processed for decision-making is invariant across individuals given a particular experimental context. Independent variables, including speed-accuracy instructions, stimulus properties (i.e., intensity), and characteristics of the participants (i.e., aging, cognitive ability) are assumed to affect the parameters in a model (i.e., speed of information accumulation, response bias) but not the way that participants process information (e.g., the order of information processing). Given these assumptions, much modeling has been accomplished based on the grouped data, rather than the individual data. However, a growing number of studies have demonstrated that there were individual differences in the perceptual decision process. In the same task context, different groups of the participants may process information in different manners. The capacity and architecture of the decision mechanism were found to vary across individuals, implying that humans’ decision strategies can vary depending on the context to maximize their performance. In this special issue, we focused on a particular subset of cognitive models, particularly accumulator models, multinomial processing trees and systems factorial technology (SFT) as applied to perceptual decision making. The motivation for the focus on perceptual decision-making is threefold. Empirical studies of perception have grown out of a history of making a large number of observations for each individual so as to achieve precise estimates of each individual’s performance. This type of data, rather than a small number of observations per individual, is most amenable to achieving precision in individual-level and group-level cognitive modeling. Second, the interaction between the acquisition of perceptual information and the decisions based on that information (to the extent that those processes are distinguishable) offers rich data for scientific exploration. Finally, there is an increasing interest in the practical application of individual variation in perceptual ability, whether to inform perceptual training and expertise, or to guide personnel decisions. Although these practical applications are beyond the scope of this issue, we hope that the research presented herein may serve as the foundation for future endeavors in that domain.

Issues in Psychology and Psychiatry Research and Practice: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Psychology and Psychiatry Research and Practice. The editors have built Issues in Psychology and Psychiatry Research and Practice: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Psychology and Psychiatry Research and Practice in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Psychology and Psychiatry Research and Practice: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

This Oxford Handbook offers a comprehensive and authoritative review of important developments in computational and mathematical psychology. With chapters written by leading scientists across a variety of subdisciplines, it examines the field's influence on related research areas such as cognitive psychology, developmental psychology, clinical psychology, and neuroscience. The Handbook emphasizes examples and applications of the latest research, and will appeal to readers possessing various levels of modeling experience. The Oxford Handbook of Computational and mathematical Psychology covers the key developments in elementary cognitive mechanisms (signal detection, information processing, reinforcement learning), basic cognitive skills (perceptual judgment, categorization, episodic memory), higher-level cognition (Bayesian cognition, decision making, semantic memory, shape perception), modeling tools (Bayesian estimation and other new model comparison methods), and emerging new directions in computation and mathematical psychology (neurocognitive modeling, applications to clinical psychology, quantum cognition). The Handbook would make an ideal graduate-level textbook for courses in computational and mathematical psychology. Readers ranging from advanced undergraduates to experienced faculty members and researchers in virtually any area of psychology--including cognitive science and related social and behavioral sciences such as consumer behavior and communication--will find the text useful.

In this two volume festschrift, contributors explore the theoretical developments (Volume I) and applications (Volume II) in traditional cognitive psychology domains, and model other areas of human performance that benefit from rigorous mathematical approaches. It brings together former classmates, students and colleagues of Dr. James T. Townsend, a pioneering researcher in the field since the early 1960s, to provide a current overview of mathematical modeling in psychology. Townsend’s research critically emphasized a need for rigor in the practice of cognitive modeling, and for providing mathematical definition and structure to ill-defined psychological topics. The research captured demonstrates how the interplay of theory and application, bridged by rigorous mathematics, can move cognitive modeling forward.

Progress in Brain Research serial highlights new advances in the field with this new volume presenting interesting chapters. Each chapter is written by an international board of authors. - An authoritative opinion piece on the evolving landscape of research into physical exercise and cognitive function. - Systematical review articles offering a synthesis of existing research and key conclusions on the field of exercise and cognition. - Original research articles presenting new findings on the enhancement of neurocognitive performance through physical exercise.