Discovering Cognitive Architecture By Selectively Influencing Mental Processes

One of the most successful methods for discovering the way mental processes are organized is to observe the effects in experiments of selectively influencing the processes. Selective influence is crucial in techniques such as Sternberg's additive factor method for reaction times and Jacoby's process dissociation procedure for accuracy. The successful uses of selective influence have encouraged application extensions to complex architectures, to dependent variables such as evoked potentials, and to complex interpretations. But the common themes have become lost in the details of separate uses and specialized terminology. The book gives an introductory and unified account of the many uses of the technique in cognitive psychology. Related models from operations research and human factors are covered. The applications include dual tasks, visual and memory search, timing, categorization, and recall. The book takes a self-contained approach starting with clear explanations of the elementary notions and a building to advanced techniques. The book is written with graduate students in mind, but has content of interest to all researchers in cognitive science and cognitive engineering.

One of the most successful methods for discovering the way mental processes are organized is to observe the effects in experiments of selectively influencing the processes. Selective influence is crucial in techniques such as Sternberg's additive factor method for reaction times and Jacoby's process dissociation procedure for accuracy. The successful uses of selective influence have encouraged application extensions to complex architectures, to dependent variables such as evoked potentials, and to complex interpretations. But the common themes have become lost in the details of separate uses and specialized terminology. The book gives an introductory and unified account of the many uses of the technique in cognitive psychology. Related models from operations research and human factors are covered. The applications include dual tasks, visual and memory search, timing, categorization, and recall. The book takes a self-contained approach starting with clear explanations of the elementary notions and a building to advanced techniques. The book is written with graduate students in mind, but has content of interest to all researchers in cognitive science and cognitive engineering.

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.

The field of mathematical psychology began in the 1950s and includes both psychological theorizing, in which mathematics plays a key role, and applied mathematics motivated by substantive problems in psychology. Central to its success was the publication of the first Handbook of Mathematical Psychology in the 1960s. The psychological sciences have since expanded to include new areas of research, and significant advances have been made in both traditional psychological domains and in the applications of the computational sciences to psychology. Upholding the rigor of the original Handbook, the New Handbook of Mathematical Psychology reflects the current state of the field by exploring the mathematical and computational foundations of new developments over the last half-century. The second volume focuses on areas of mathematics that are used in constructing models of cognitive phenomena and decision making, and on the role of measurement in psychology.

Invariances in Human Information Processing examines and identifies processing universals and how they are implemented in elementary judgemental processes. This edited collection offers evidence that these universals can be extracted and identified from observing law-like principles in perception, cognition, and action. Addressing memory operations, development, and conceptual learning, this book considers basic and complex meso- and makro-stages of information processing. Chapter authors provide theoretical accounts of cognitive processing that may offer tools for identification of functional components in brain activity in cognitive neuroscience

Traditional approaches to cognitive psychology correspond with a classical view of logic and probability theory. More specifically, one typically assumes that cognitive processes of human thought are founded on the Boolean structures of classical logic, while the probabilistic aspects of these processes are based on the Kolmogorovian structures of classical probability theory. However, growing experimental evidence indicates that the models founded on classical structures systematically fail when human decisions are at stake. These experimental deviations from classical behavior have been called `paradoxes’, `fallacies’, `effects’ or `contradictions’, depending on the specific situation where they appear. But, they involve a broad spectrum of cognitive and social science domains, ranging from conceptual combination to decision making under uncertainty, behavioral economics, and linguistics. This situation has constituted a serious drawback to the development of various disciplines, like cognitive science, linguistics, artificial intelligence, economic modeling and behavioral finance. A different approach to cognitive psychology, initiated two decades ago, has meanwhile matured into a new domain of research, called ‘quantum cognition’. Its main feature is the use of the mathematical formalism of quantum theory as modeling tool for these cognitive situations where traditional classically based approaches fail. Quantum cognition has recently attracted the interest of important journals and editing houses, academic and funding institutions, popular science and media. Specifically, within a quantum cognition approach, one assumes that human decisions do not necessarily obey the rules of Boolean logic and Kolmogorovian probability, and can on the contrary be modeled by the quantum-mechanical formalism. Different concrete quantum-theoretic models have meanwhile been developed that successfully represent the cognitive situations that are classically problematical, by explaining observed deviations from classicality in terms of genuine quantum effects, such as `contextuality’, `emergence’, `interference’, `superposition’, `entanglement’ and `indistinguishability’. In addition, the validity of these quantum models is convincingly confirmed by new experimental tests. We also stress that, since the use of a quantum-theoretic framework is mainly for modeling purposes, the identification of quantum structures in cognitive processes does not presuppose (without being incompatible with it) the existence of microscopic quantum processes in the human brain. In this Research Topic, we review the major achievements that have been obtained in quantum cognition, by providing an accurate picture of the state-of-the-art of this emerging discipline. Our overview does not pretend to be either complete or exhaustive. But, we aim to introduce psychologists and social scientists to this challenging new research area, encouraging them, at the same time, to consider its promising results. It is our opinion that, if continuous progress in this domain can be realized, quantum cognition can constitute an important breakthrough in cognitive psychology, and potentially open the way towards a new scientific paradigm in social science.

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.

This book aims to understand human cognition and psychology through a comprehensive computational theory of the mind, namely, a "cognitive architecture." The goal is to develop a unified framework for understanding the human mind, and within the unified framework, to develop process-based, mechanistic explanations of a variety of psychological phenomena.

There are many books on lattice theory in the field, but none interfaces with the foundations of probability. This book does. It also develops new probability theories with rigorous foundations for decision theory and applies them to specific well-known problematic examples. There is only one other book that attempts this. It uses quantum probability theory from physics. The new probability theories developed in this book are different; they are not borrowed from physics but are explicitly designed for decision theory.

Team Mental Models (TMM) are one of the strongest predictors of team behavior and performance. TMM direct team behaviors through the series of tasks they perform over time. Research in the area, although crucial in demonstrating the effect of TMM, has been largely static, failing to articulate specifically how TMM emerge or function in teams over time. This dissertation develops a computational model to explicate the process of TMM emergence and demonstrate necessary factors. First, I explain the core concepts of TMM emergence, including team composition, dyadic interactions, and contextual variables. Second, I develop a process-oriented theory of TMM development in narrative format. Third, I translate the narrative theory into a computational model proposed to explore how the core processes interact to influence TMM emergence. Results of the model suggest that teams may simultaneously increase TMM similarity and decrease overall accuracy as a team. Additionally, team intelligence may be viewed as a liability in some respects. While intelligence in team members could facilitate more efficient, faster sharing of information, incorrect information spreads more quickly. As team members are more agreeable starting from the beginning of the simulation, members could be more susceptible to believing more incorrect information.