Inferences about spatial, temporal, and other relations are ubiquitous. This article presents a novel model-based theory of such reasoning. The theory depends on 5 principles. (a) The structure of mental models is iconic as far as possible. (b) The logical consequences of relations emerge from models constructed from the meanings of the relations and from knowledge. (c) Individuals tend to construct only a single, typical model. (d) They spontaneously develop their own strategies for relational reasoning. (e) Regardless of strategy, the difficulty of an inference depends on the process of integration of the information from separate premises, the number of entities that have to be integrated to form a model, and the depth of the relation. The article describes computer implementations of the theory and presents experimental results corroborating its main principles.

Abstract: Humans use visual imagery for a variety of purposes including reasoning about visual properties and spatial relationships, anticipating future events, recalling past experiences, and memorizing concepts and facts. Cognitive architectures have traditionally ignored visual imagery, but we have started to explore this phenomenon in the context of Soar. Soar provides the underlying control, memory, and learning mechanisms while visual imagery provides efficient representations and processes for finding an object’s visual features and spatial relationships not explicitly encoded with symbols. This multi-modal approach enables the architecture to use the most efficient representation for the appropriate computation and requires less domain knowledge for visual-spatial type environments and tasks. This paper outlines our theory and the corresponding architecture, design, and implementation. We present initial results in two small problem domains.

Abstract. Research spanning decades has generated a long list of phenomena associated with human spatial information processing. Additionally, a number of theories have been proposed about the representation, organization and processing of spatial information by humans. This paper presents a broad account of human spatial competence, integrated with the ACT-R cognitive architecture. Using a cognitive architecture grounds the research in a validated theory of human cognition, enhancing the plausibility of the overall account. This work posits a close link of aspects of spatial information processing to vision and motor planning, and integrates theoretical perspectives that have been proposed over the history of research in this area. In addition, the account is supported by evidence from neuropsychological investigations of human spatial ability. The mechanisms provide a means of accounting for a broad range of phenomena described in the experimental literature.

A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation processes; and (c) orientation and size congruency effects indicate that recognition involves the adjustment of a reference frame. All 3 classes of findings can be explained by a transformational framework of recognition: Recognition is achieved by an analogue transformation of a perceptual coordinate system that aligns memory and input representations. Coordinate transformations can be implemented neurocomputationally by gain (amplitude) modulation and may be regarded as a general processing principle of the visual cortex.

Eight experiments investigated the effects of visual, spatial, auditory, and executive interference on the symbolic comparison of animal size and ferocity, semantic goodness of words, and numbers. Dynamic visual noise (DVN) and the reading of visually presented stimulus items were shown to selectively interfere with response times on the animal size comparison task, though the slope of the symbolic distance function remained unchanged. Increased change of DVN significantly increased interference, but interference was reduced by equiluminant DVN. Spatial tracking reduced the slope of the symbolic distance function in contrast to an executive task that only increased mean latency and errors for all comparisons. Results suggest that the generation of an image is necessary for size comparison, but neither imagery nor executive function is responsible for the frequently observed distance–time function.

2. The propositional view of concepts and meaning: some reasons of its success

this article (although this distinction is the subject of extensive discussion in Pylyshyn, 1984a, Chapter 7). This informal characterization and the following example will have to do for present purposes. To make this point in a more concrete way, I invented a somewhat frivolous but revealing example, involving a certain mystery box of unknown construction whose pattern of behavior has been assiduously recorded (Pylyshyn, 1984a). This box is known to emit long and short pulses with a reliable recurring pattern. The pattern (illustrated in Figure 6-1) can be described as follows: pairs of short pulses usually precede single short pulses, except when a pair of long-short pulses occurs first. In this example it turns out that the observed regularity, though completely regular when the box is in its "ecological niche," is not due to the nature of the box (to how it is constructed) but to an entirely extrinsic reason. These two sorts of "reasons" for the observed pattern (intrinsic or extrinsic) are analogous to the architecture versus tacit knowledge distinction and is crucial to understanding why the box works the way it does, as well as to why certain patterns of cognition occur. 6-9 Figure 6-1. Pattern of blips observed from a box in its typical mode of operation. The question is: Why does it exhibit this pattern of behavior? What does this behavior tell us about how it works? The reason why this particular pattern of behavior occurs in this case can only be appreciated if we know that the pulses are codes, and the pattern is due to a pattern in what they represent, in particular that the pulses represent English words spelled out in International Morse Code. The observed pattern does not reflect how the box is wired or its functional architecture -- it is due entirel...

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2010 For my parents. ii Acknowledgements First, I would like to thank my advisor, John Laird. I came to graduate school as a Master's student with no AI background, and right from the beginning John provided me with his advice and knowledge, and, most importantly, a foothold to get started in research. Since then, John's ability to help me when necessary and to let me go my own way at other times has made this experience much better than it could have been. The other members of my committee, Satinder Singh, Ben Kuipers, and Rick Lewis, have also all been very responsive throughout this process, and have all provided me with excellent feedback relevant to their areas of expertise, from which the work here has greatly benefited. I would also like to thank the other current and former members of the Soar group at U of M, who have provided me with constant valuable feedback on my work, or at least have put up with it when I needed to talk something over out loud: Nate Derbinsky, Nick Gorski, Scott Lathrop, Bob Marinier, Andrew Nuxoll, Yongjia Wang, Joseph Xu, and

by scanning processes