The papers published in this Special Issue are based upon presentations at a workshop on "Associative Learning and Representation" which was sponsored by the Experimental Psychology Society at Emmanuel College, Cambridge.

Clark charts a fundamental shift from a static, inner-code-oriented conception of the subject matter of cognitive science to a more dynamic, developmentally rich, process-oriented view.

The editor and authors of this book present a synthesis of work on human associative learning, tracing some of its historical roots but concentrating mainly on recent developments. It is divided into three sections: an introduction to the recent data and controversies in the study of human associative learning; recent developments in the formal theories of how associative learning occurs; and applied work on human associative learning, particularly its application to depression and to the development of preferences. The book is designed to be accessible to undergraduates, providing a clear illustration of how principles most commonly introduced in animal cognition courses are relevant to the contemporary study of human cognition.

First published in 1982. During the past fifty years, dramatic changes have occurred in the use of laboratory animals to study learning and memory. Yet the basic reasons for this research, diverse as they are, have not changed. At one extreme is the need for relatively direct application of findings with animal models to medical or educational problems of humans; at the other extreme, the quest for understanding animal behavior for its own sake. It is probably fair to say that no chapters in this book represent either of these extremes, although in each case the author’s purposes can be said to be like those of some scientists working in this area fifty years ago. In contrast to this continuity of purpose, the approach that scientists now take in this area of study is really quite different from that of most or all scientists in the 1930s.

While philosophers have been interested in animals since ancient times, in the last few decades the subject of animal minds has emerged as a major topic in philosophy. The Routledge Handbook of Philosophy of Animal Minds is an outstanding reference source to the key topics, problems, and debates in this exciting subject and is the first collection of its kind. Comprising nearly fifty chapters by a team of international contributors, the Handbook is divided into eight parts: Mental representation Reasoning and metacognition Consciousness Mindreading Communication Social cognition and culture Association, simplicity, and modeling Ethics. Within these sections, central issues, debates, and problems are examined, including: whether and how animals represent and reason about the world; how animal cognition differs from human cognition; whether animals are conscious; whether animals represent their own mental states or those of others; how animals communicate; the extent to which animals have cultures; how to choose among competing models and explanations of animal behavior; and whether animals are moral agents and/or moral patients. The Routledge Handbook of Philosophy of Animal Minds is essential reading for students and researchers in philosophy of mind, philosophy of psychology, ethics, and related disciplines such as ethology, biology, psychology, linguistics, and anthropology.

This book brings together leading international learning and attention researchers to provide both a comprehensive and wide-ranging overview of the current state of knowledge of this area as well as new perspectives and directions for the future.

Associative Networks: Representation and Use of Knowledge by Computers is a collection of papers that deals with knowledge base of programs exhibiting some operational aspects of understanding. One paper reviews network formalism that utilizes unobstructed semantics, independent of the domain to which it is applied, that is also capable of handling significant epistemological relationships of concept structuring, attribute/value inheritance, multiple descriptions. Another paper explains network notations that encode taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, as well as the relationships between syntactic units and their interpretations. One paper shows that networks can be designed to be intuitively and formally interpretable. Network formalisms are computer-oriented logics which become distinctly significant when access paths from concepts to propositions are built into them. One feature of a topical network organization is its potential for learning. If one topic is too large, it could be broken down where groupings of propositions under the split topics are then based on "co-usage" statistics. As an example, one paper cites the University of Maryland artificial intelligence (AI) group which investigates the control and interaction of a meaning-based parser. The group also analyzes the inferences and predictions from a number of levels based on mundane inferences of actions and causes that can be used in AI. The collection can be useful for computer engineers, computer programmers, mathematicians, and researchers who are working on artificial intelligence.