This book introduces a host of connectionist models of cognition and behavior. The major areas covered are high-level cognition, language, categorization and visual perception, and sensory and attentional processing. All of the articles cover unpublished research work. The key contribution of this book is that it focuses exclusively on the advances in connectionist modeling in psychology. The papers are relatively short, and were explicitly written to be accessible to both connectionist modelers and experimental psychologists.

This volume examines two main questions: What is linguistics about? And how do the results of linguistic theorizing bear on inquiry in related fields, particularly in psychology? The book develops views that depart from received wisdom in both philosophy and linguistics. With regard to questions concerning the subject matter, methodological goals, and ontological commitments of formal syntactic theorizing, it argues that the cognitive conception adopted by most linguists and philosophers is not the only acceptable view, and that the arguments in its favor collapse under scrutiny. Nevertheless, as the book shows, a detailed examination of the relevant psycholinguistic results and computational models does support the claim that the theoretical constructs of formal linguistics are operative in real-time language comprehension. These constructs fall into two categories: mental phrase markers and mental syntactic principles. Both are indeed psychologically real, but in importantly different ways. The book concludes by drawing attention to the importance of the often-elided distinction between personal and subpersonal psychological states and processes, as well as the logical character of dispositional and occurrent states. By clarifying these concepts, particularly by reference to up-and-running psychological and computational models, the book yields a richer and more satisfying perspective on the psychological reality of language.

Current robots and other artificial systems are typically able to accomplish only one single task. Overcoming this limitation requires the development of control architectures and learning algorithms that can support the acquisition and deployment of several different skills, which in turn seems to require a modular and hierarchical organization. In this way, different modules can acquire different skills without catastrophic interference, and higher-level components of the system can solve complex tasks by exploiting the skills encapsulated in the lower-level modules. While machine learning and robotics recognize the fundamental importance of the hierarchical organization of behavior for building robots that scale up to solve complex tasks, research in psychology and neuroscience shows increasing evidence that modularity and hierarchy are pivotal organization principles of behavior and of the brain. They might even lead to the cumulative acquisition of an ever-increasing number of skills, which seems to be a characteristic of mammals, and humans in particular. This book is a comprehensive overview of the state of the art on the modeling of the hierarchical organization of behavior in animals, and on its exploitation in robot controllers. The book perspective is highly interdisciplinary, featuring models belonging to all relevant areas, including machine learning, robotics, neural networks, and computational modeling in psychology and neuroscience. The book chapters review the authors' most recent contributions to the investigation of hierarchical behavior, and highlight the open questions and most promising research directions. As the contributing authors are among the pioneers carrying out fundamental work on this topic, the book covers the most important and topical issues in the field from a computationally informed, theoretically oriented perspective. The book will be of benefit to academic and industrial researchers and graduate students in related disciplines.