The book also introduces a host of new experimental techniques and major hypotheses to guide future research.

This long-awaited work by prominent Harvard psychologist Stephen Kosslyn integrates a twenty-year research program on the nature of high-level vision and mental ...

What might a theory of mental imagery look like, and how might one begin formulating such a theory? These are the central questions addressed in the present paper. The first section outlines the general research direction taken here and provides an overview of the empirical foundations of our theory of image representation and processing. Four issues are considered in succession, and the relevant results of experiments are presented and discussed. The second section begins with a discussion of the proper form (...) for a cognitive theory, and the distinction between a theory and a model is developed. Following this, the present theory and computer simulation model are introduced. This theory specifies the nature of the internal representations (data structures) and the processes that operate on them when one generates, inspects, or transforms mental images. In the third, concluding, section we consider three very different kinds of objections to the present research program, one hinging on the possibility of experimental artifacts in the data, and the others turning on metatheoretical commitments about the form of a cognitive theory. Finally, we discuss how one ought best to evaluate theories and models of the sort developed here. (shrink)

This paper describes an operational computer simulation of visual mental imagery in humans. The structure of the simulation was motivated by results of experiments on how people represent information in, and access information from, visual images. The simulation includes a “surface representation,” which is spatial and quasi‐pictorial, and an underlying “deep representation,” which contains “perceptual” information encoding appearance plus “propositional” information describing facts about an object. The simulation embodies a theory of how surface images are generated from deep representations, and (...) how surface images are processed when one accesses information embedded in them. The simulation also offers an account of various sorts of imagery transformations. (shrink)

Much indirect evidence supports the hypothesis that transformations of mental images are at least in part guided by motor processes, even in the case of images of abstract objects rather than of body parts. For example, rotation may be guided by processes that also prime one to see results of a specific motor action. We directly test the hypothesis by means of a dual-task paradigm in which subjects perform the Cooper-Shepard mental rotation task while executing an unseen motor rotation in (...) a given direction and at a previously learned speed. Four results support the inference that mental rotation relies on motor processes. First, motor rotation that is compatible with mental rotation results in faster times and fewer errors in the imagery task than when the two rotations are incompatible. Second, the angle through which subjects rotate their mental images, and the angle through which they rotate a joystick handle are correlated, but only if the directions of the two rotations are compatible. Third, motor rotation modifies the classical inverted V-shaped mental rotation response time function, favoring the direction of the motor rotation; indeed, in some cases motor rotation even shifts the location of the minimum of this curve in the direction of the motor rotation. Fourth, the preceding effect is sensitive not only to the direction of the motor rotation, but also to the motor speed. A change in the speed of motor rotation can correspondingly slow down or speed up the mental rotation. (shrink)

The concept of representation has become almost inextricably bound to the concept of symbol systems. the concepts is nowhere more prevalent than in descriptions of "internal representations." These representations are thought to occur in an internal symbol system that allows the brain to store and use information. In this paper we explore a different approach to understanding psychological processes, one that retains a commitment to representations and computations but that is not based on the idea that information must be stored (...) and manipulated in symbol systems. In particular, we suggest that the notion of a symbol system as currently understood construes psychological processes in terms of a specific type of computational system, in which a control function "reads," "interprets," and manipulates discrete entities called "symbols." We argue that other types of computational systems may provide a more appropriate characterization of psychological processes. One implication of our argument is the need to consider the constraints placed on computational theories in psychology by the nature of the computing device itself, the human brain. Perhaps surprisingly, this implication leads us to the conclusion that a "functionalist" conception of psychological processes (discussed below) does not entail that physiology is irrelevant to psychology, as has been maintained by prominent adherents of the symbol-systems approach. (shrink)

Two experiments were conducted to test a prediction of the Kosslyn & Shwartz computer simulation model of mental image processing. According to this model, more complex images require more time to form because parts are placed sequentially, and larger images require more time to form than smaller ones because more parts are placed. If these accounts are correct, then the advantage of forming a small image (i.e., one that seems to subtend a smaller visual angle) should be greater for more (...) complex objects because the difference in number of parts imaged at the two sizes will be greater than with simpler objects. This prediction was confirmed only when subjects were not motivated to form highly elaborated images at small sizes. When subject tried to include all details, it actually took longest to form images of complex objects at small sizes. Both of these results support the central assumption of the Kosslyn‐Shwartz model, namely the existence of a fixed resolution analog spatial medium. (shrink)

This commentary focuses on four major points: (1) “Tacit knowledge” is not a complete explanation for imagery phenomena, if it is an explanation at all. (2) Similarities and dissimilarities between imagery and perception are entirely consistent with the depictive view. (3) Knowledge about the brain is crucial for settling the debate. (4) It is not clear what sort of theory Pylyshyn advocates.

A particular research program on mental imagery is defended against certain sweeping methodological criticisms that have been advanced against it. The central claim is that the approach taken in the program is an appropriate response to the problem of doing empirical research in a theoretical vacuum, and that when it is viewed in this perspective, the criticisms are not merely unfounded, they are inappropriate. The argument for this claim is developed by first describing the program and then analyzing the methodological (...) rationale behind it. (shrink)

Cook (1995) criticized Kosslyn, Chabris, Marsolek & Koenig's (1992) network simulation models of spatial relations encoding in part because the absolute position of a stimulus in the input array was correlated with its spatial relation to a landmark; thus, on at least some trials, the networks did not need to compute spatial relations. The network models reported here include larger input arrays, which allow stimuli to appear in a large range of locations with an equal probability of being above or (...) below a “bar,” thus eliminating the confound present in earlier models. The results confirm the original hypothesis that as the size of the network's receptive fields increases, performance on a coordinate spatial relations task (which requires computing precise, metric distance) will be relatively better than on a categorical spatial relations task (which requires computing above/below relative to a landmark). (shrink)

We start with a simple question: If you could reinvent higher education for the 21st century, what should it look like? We began by taking a hard look at problems in traditional higher education, and innovated in many ways to address these problems head-on: We have created a new curriculum, focusing on what we call "practical knowledge"; we have developed new pedagogy, based on the science of learning; we have used technology in novel ways, to deliver small seminars in real (...) time; and we have developed an international hybrid residential model, where students take classes on the computer but live together, rotating through seven different cities around the world. The Minerva Schools at the Keck Graduate Institute (KGI) are the first university experience built for the twenty-first century. In setting up this program, we have had to confront the realities of all aspects of higher education--from admissions, through instruction, to career development, to establishing a reputation. The goal of this book is to provide an evidence-based model for a future of higher education. We have learned a lot about how to reshape all facets of higher education and this book summarizes what we have learned. We hope that our innovations can serve as models of "best practices"--and thereby have a major influence on higher education writ large. (shrink)

Cook (1995) criticizes the work of Jacobs and Kosslyn (1994) on spatial relations, shape representations, and receptive fields in neural network models on the grounds that first‐order correlations between input and output unit activities can explain the results. We reply briefly to Cook's arguments here (and in Kosslyn, Chabris, Marsolek, Jacobs & Koenig, 1995) and discuss how new simulations can confirm the importance of receptive field size as a crucial variable in the encoding of categorical and coordinate spatial relations and (...) the corresponding shape representations; such simulations would testify to the computational distinction between the different types of representations. (shrink)