What mechanisms are involved in enabling us to generate predictions of what will happen in the near future? Although we use associative mechanisms as the basis to predict future events, such as using cues from our surrounding environment, timing, attentional, and configural mechanisms are also needed to improve this function. Timing mechanisms allow us to determine when those events will take place. Attentional mechanisms ensure that we keep track to cues that are present when unexpected events occur and disregard cues present when everything happens according to our expectation. Configural mechanisms make it possible to combine separate cues into one signal that predicts an event different from that predicted individually by separate cues. Writing for graduates and researchers in neuroscience, computer science, biomedical engineering, and psychology, the author presents neural network models that incorporate these mechanisms and shows, through computer simulations, how they explain the multiple properties of associative learning.
Since first described, multiple properties of classical conditioning have been discovered, establishing the need for mathematical models to help explain the defining features. The mathematical complexity of the models puts our understanding of their workings beyond the ability of our intuitive thinking and makes computer simulations irreplaceable. The complexity of the models frequently results in function redundancy, a natural property of biologically evolved systems that is much desired in technologically designed products. Experts provide the latest advancements in the field and present detailed descriptions of how the models simulate conditioned behaviour and its physiological bases. It offers advanced students and researchers examples of how the models are used to analyse existing experimental results and design future experiments. This volume is of great interest to psychologists and neuroscientists, as well as computer scientists and engineers searching for ideas applicable to the design of robots that mimic animal behaviour.
Originally published in 1980, this volume explores some of the dramatic and exciting changes that had taken place in the field of conditioning in the 15 years prior to publication. The usefulness of a particular learning procedure, second-order conditioning, is explored in three aspects of the learning process: (1) the measurement of learning; (2) the circumstances that produce associative learning; and (3) the content of that learning.
The usefulness of this new paradigm is documented with the results of experiments that had grown out of the author's programmatic work at the time. Completely new results were published for the first time, in an attempt to demonstrate the power of this particular learning procedure in elucidating fundamental questions about the nature of learning.
This book presents a new basis for the empirical analysis of film. Starting from an established body of work in film theory, the authors show how a close incorporation of the current state of the art in multimodal theory, including accounts of the syntagmatic and paradigmatic axes of organisation, discourse semantics and advanced #xE2;#xAC;#xDC;layout structure#xE2;#xAC;", provides a methodology by which concrete details of film sequences drive mechanisms for constructing filmic discourse structures. The book introduces the necessary background, the open questions raised, and the method by which analysis can proceed step-by-step with extensive examples drawn from a broad range of films. The book aims to provide an analytic tool set that will enable the reader to approach the study of film organisation with new levels of detail, probing deeply into the fundamental question of film as to just how it is that films reliably communicate meaning.
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