This book collects some of the most exciting pioneering work in perceptual and cognitive psychology. The authors' quantitative approach to the study of mental images and their representation is clearly depicted in this invaluable volume of research which presents, interprets, evaluates, and extends their work. The selections are preceded by a thorough review of the history of their experiments, and all of the articles have been updated with reviews of the current literature. The book's first part focuses on mental rotation; (...) the second includes other, more complex transformations and sequences of transformations. A third part describes work on rotational transformations in the context of the perceptual illusion of &"apparent motion.&" Roger N. Shepard is Professor of Psychology, Stanford University. Lynn A. Cooper is Professor of Psychology, University of Arizona. A Bradford Book. (shrink)

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation (...) and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individual's existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction. Key Words: concept learning; conceptual development; features; perceptual learning; stimulus encoding. (shrink)