DISTINGUISHING VISUAL IMAGERY AND SPATIAL REASONING

The imagery debate in its most recent revival inquires into the “psychological reality” of pictorial representations, asking whether images serve any cognitive function in humans, whether knowledge representation is analog or digital, whether images serve their functions in a stand-alone manner or require tacit knowledge, and so forth. However, these questions have not moved far enough away from the original phenomenological puzzle (how and why do I “see” things that are not there) because they are ill-specified and lack an encompassing theoretical context. For these reasons the debate has not been resolved by psychological experiment and never will be without more precise theories to test. Janice Glasgow joins those who have proposed that we address instead questions of how images can be represented and used by computers. The issue then becomes whether specific depictive representations, whatever form they might take, have computational advantages over alternatives such as predicate logic with deduction. The judgments must be based on comparisons of total systems, including the access processes, not merely the notation used. Later the psychologists can make of these results what they will. Of course, there has already been interest in artificial intelligence (AI) on several problems of spatial reasoning $such as navigation. Nonetheless, Glasgow’s emphasis on the computational questions and imagery is a minority one. Classical AI, particularly knowledge representation and problem solving, has been almost exclusively wedded to search-based forms of deduction and descriptive representations. Since I have also been a member of the minority, I applaud Glasgow’s efforts, both her proselytizing and her research, and on the general issues she and I appear to be in substantial agreement. Here I will contrast my own research with hers to provide some perspective on our differences. Psychology and philosophy have placed emphasis on the percept-like nature of imagery, particularly visual imagery. However, the cognitive roles of most sensory features such as color, pitch, and saltiness are relatively limited in inferential richness and structure and are probably amenable to conventional methods of analysis. However, a consortium of sensory-perceptual abilities evolved to provide efficient ways to move about in space and deal with physical objects in real-time, and these abilities have come to include in man the means to make inferences, especially predictions, about non-current events because such abilities have clear adaptive advantages that are central to our success. My approach adopts the position that the cognitive role of imagery is primarily a result of the fact that visual imagery and, in less obvious ways, tactile, proprioceptive, and auditory imagery, involve the representation and use of the structure of space, time and mechanics. Thus, I have chosen to avoid problems of nonspatial sensory features, and instead study computational methods that use spatial information in reasoning about well-defined tasks, e.g., to understand the role of diagrammatic reasoning in addressing mathematical problems (Lindsay 1988; 1989; 1992). Similar problems have long been addressed in A1 by others, from Gelernter (1959) 10 Larkin and Simon (1987) and Novak and Bulko (1990), but without explicitly attempting to capture the structure of space in a uniform representational scheme. We-have clear mathematical descriptions both of space and of the mechanics of rigid bodies acted upon by forces. The most straightforward ways to represent space are based on these descriptions. One way of doing this is illustrated by the work of Kaufman (1991),