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.

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.

An argument that there are perceptual mechanisms that retrieve information in cognitively and conceptually unmediated ways and that this sheds light on various philosophical issues. In Cognition and Perception, Athanassios Raftopoulos discusses the cognitive penetrability of perception and claims that there is a part of visual processes (which he calls “perception”) that results in representational states with nonconceptual content; that is, a part that retrieves information from visual scenes in conceptually unmediated, “bottom-up,” theory-neutral ways. Raftopoulos applies this insight to problems in philosophy of science, philosophy of mind, and epistemology, and examines how we access the external world through our perception as well as what we can know of that world. To show that there is a theory-neutral part of existence, Raftopoulos turns to cognitive science and argues that there is substantial scientific evidence. He then claims that perception induces representational states with nonconceptual content and examines the nature of the nonconceptual content. The nonconceptual information retrieved, he argues, does not allow the identification or recognition of an object but only its individuation as a discrete persistent object with certain spatiotemporal properties and other features. Object individuation, however, suffices to determine the referents of perceptual demonstratives. Raftopoulos defends his account in the context of current discussions on the issue of the theory-ladenness of perception (namely the Fodor-Churchland debate), and then discusses the repercussions of his thesis for problems in the philosophy of science. Finally, Raftopoulos claims that there is a minimal form of realism that is defensible. This minimal realism holds that objects, their spatiotemporal properties, and such features as shape, orientation, and motion are real, mind-independent properties in the world.

The mental representations of perceptual and cognitive stimuli vary on many dimensions. In addition, because of quantal fluctuations in the stimulus, spontaneous neural activity, and fluctuations in arousal and attentiveness, mental events are characterized by an inherent variability. During the last several years, a number of models and theories have been developed that explicitly assume the appropriate mental representation is both multidimensional and probabilistic. This new approach has the potential to revolutionize the study of perception and cognition in the same way that signal detection theory revolutionized the study of psychophysics. This unique volume is the first to critically survey this important new area of research.

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.

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 Psychophysics of Learning presents a learning system design approach that is formulated by the strategies and techniques the brain uses to process external information and make sense of that information to the learning ecology of all learners.