Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP,which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects.Processing of spatial information is an important cognitive ability. It is involved in such cognitive functions as perception, memory, attention, navigation, and problem solving. The multitude and diversity of these functions have often led students of cognition to assume the operation of several different and independent mechanisms (modules) for spatial information processing, each mechanism subserving its corresponding cognitive function. However, this account, recently referred to by the picturesque name "bag of tricks" (Ramachandran, 1990), should not be accepted until more parsimonious explanations are rejected. In fact, the results of prior research seem to suggest that visual perception, memory, and attention do involve similar mechanisms. Specifically, all these mechanisms are based on a hierarchical architecture This study was reported at annual meetings

How does an animal conceal itself from visual detection by other animals? This review paper seeks to identify general principles that may apply in this broad area. It considers mechanisms of visual encoding, of grouping and object encoding, and of search. In most cases, the evidence base comes from studies of humans or species whose vision approximates to that of humans. The effort is hampered by a relatively sparse literature on visual function in natural environments and with complex foraging tasks. However, some general constraints emerge as being potentially powerful principles in understanding concealment-a 'constraint' here means a set of simplifying assumptions. Strategies that disrupt the unambiguous encoding of discontinuities of intensity (edges), and of other key visual attributes, such as motion, are key here. Similar strategies may also defeat grouping and object-encoding mechanisms. Finally, the paper considers how we may understand the processes of search for complex targets in complex scenes. The aim is to provide a number of pointers towards issues, which may be of assistance in understanding camouflage and concealment, particularly with reference to how visual systems can detect the shape of complex, concealed objects.

eye movements made when only looking are different from those made when tapping. Visual search functions as a separate process, incorporated into both tasks: it can be used to improve performance when memory load is heavy.

The modern study of perception began when Fechner published his 'Elements of Psychophysics' in 1860. This book has guided most perception research ever since. It has become increasingly clear that there are problems with Fechner's approach, which assumes that the percept is completely determined by the sensory input. Fechner's approach cannot explain the processes that allow our percepts to be veridical. Post-Fechnerian schools (Helmholtzian, Structural, Gestalt and Gibsonian) have tried to deal with this problem, but have not been successful. An alternative to the Fechnerian approach is required. This paper describes an alternative that has been developing over the last 20 years within the computer vision community. It treats perceptual interpretation as a solution of an inverse problem that depends critically on the operation of a priori constraints. Contemporary research, which adopted this approach, has concentrated on verifying the usefulness of Bayesian and standard regularization methods. This paper takes the next step; it discusses theoretical and empirical aspects of studying human perception as an inverse problem. It reviews the literature that illustrates the power of the inverse problem approach. This review leads to the suggestion that progress in the study of perception will benefit if the inverse approach were to be adopted by experimentalists, as well as by the computational modelers, who have been actively exploring its potential to date.

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Gaze-shift dynamics of unrestrained seated subjects were examined. The subjects participated in two tasks. In the first task, they tapped sequences of 3-D targets located on a table in front of them. In the second task, they only looked at similar sequences of targets. The purpose of the task (tapping vs only looking) affected the dynamics of gaze-shifts. Gaze and eye-in-head peak velocities were higher and gaze-shift durations were shorter during tapping than during looking-only. We conclude that task variables affect gaze-shift dynamics, altering characteristics of the so-called saccadic "main sequence".

Human beings perceive 3D shapes veridically, but the underlying mechanisms remain unknown. The problem of producing veridical shape percepts is computationally difficult because the 3D shapes have to be recovered from 2D retinal images. This paper describes a new model, based on a regularization approach, that does this very well. It uses a new simplicity principle composed of four shape constraints: viz., symmetry, planarity, maximum compactness and minimum surface. Maximum compactness and minimum surface have never been used before. The model was tested with random symmetrical polyhedra. It recovered their 3D shapes from a single randomly-chosen 2D image. Neither learning, nor depth perception, was required. The effectiveness of the maximum compactness and the minimum surface constraints were measured by how well the aspect ratio of the 3D shapes was recovered. These constraints were effective; they recovered the aspect ratio of the 3D shapes very well. Aspect ratios recovered by the model were compared to aspect ratios adjusted by four human observers. They also adjusted aspect ratios very well. In those rare cases, in which the human observers showed large errors in adjusted aspect ratios, their errors were very similar to the errors made by the model.

This paper reviews recent progress towards understanding 3D shape perception made possible by appreciating the significant role that veridicality and complexity play in the natural visual environment. The ability to see objects as they really are "out there" is derived from the complexity inherent in the 3D object's shape. The importance of both veridicality and complexity was ignored in most prior research. Appreciating their importance made it possible to devise a computational model that recovers the 3D shape of an object from only one of its 2D images. This model uses a simplicity principle consisting of only four a priori constraints representing properties of 3D shapes, primarily their symmetry and volume. The model recovers 3D shapes from a single 2D image as well, and sometimes even better, than a human being. In the rare recoveries in which errors are observed, the errors made by the model and human subjects are very similar. The model makes no use of depth, surfaces or learning. Recent elaborations of this model include: (i) the recovery of the shapes of natural objects, including human and animal bodies with limbs in varying positions (ii) providing the model with two input images that allowed it to achieve virtually perfect shape constancy from almost all viewing directions. The review concludes with a comparison of some of the highlights of our novel, successful approach to the recovery of 3D shape from a 2D image with prior, less successful approaches.