Visual Interaction: A LinkLink Between Perception and Problem Solving Erika Rogers and Ronald C. ArkinArkin Abdmcl-TheAbstract-The approach taken in this research is to de­de- is to consider how to use automation to enhance the ef­ef- velop a cognitive model of how a human observer extracts fectiveness of the human performing a complex task, to information from a visual display and then u~eeuses this per-per­ build "tools“tools instead of prostheses”prostheses" [3,5]. HoltzmanHoltsman [6] ceptual information in a decision-making task. Knowledge states that "effective“effective decision systems must concentrate about this relationship provides information about the oc­oc- on assisting the decision-maker to gain insight into the currence of perceptual events in the course of problem-problem­ decision problem at hand rather than on merely supply­supply- solving activities, and suggests that perceptudperceptual assistance ing a somehow 'right'‘right’ answer".answer”. It is also stressed that the in the form of image enhancements is a useful supplement way in which this is to be achieved must involve an un-un­ to the user'suser’s own abilities. This knowledge is then to be derstanding of the cognitive abilities of the human user in embedded in an intelligent computerized assistant which order to implement a "cognitive“cognitivecooperation” cooperation" [3] between isie designed to facilitate and stimulateI/timulate the human problem-problem­ the human and the computer system. With respect to solving process. these stated philosophies, the research presented in this paper is in complete agreement. I. INTRODUCTION However, in practice, the implementations of these ideas have concentrated primarily on the development of With the advent of powerful new technologies for dis­dis- expert-like systems, both in medicine and industry. Such playing multi-dimensional scientific data, the develop­develop- systems usually achieve their cognitive plausibility by ex­ex- ment of new strategies for efficient use of these capabili­capabili- tracting domain-specific knowledge from one or more ex­ex- ties is offoremost concern. It is becoming more and more perts in an area, and then reformulating this knowledge evident that in order to perform complex tasks, man and in a rule-based format. There are three main drawbacks machine can no longer be treated as separate entities, but of such systems: 1) the knowledge extracted tends to be must be considered together as a unified decision-making shallow; 2) experts can also be fallible, and therefore a system. The powerful computational resources of com­com- range of experience would be more desirable for our mod-mod­ puter technology must be coupled with powerful human elling effort; and 3) expert systems tend to rely on verbal perceptual and problem-solving capabilities in order to representations of knowledge, and few are designed to ac­ac- achieve this goal. commodate "image"“image” or "spatial“spatial reasoning"reasoning” that is needed Efforts to apply artificial intelligence techniques in pur-pur­ for task domains where the decision-making process must suit of this goal have resulted in a number of different rely on interpretation of a visual image. Therefore, our approaches. One direction has been the development of approach is to first develop a detailed cognitive model automated reasoning systems, where conceptually, the of the human capabilities in our area of interest. This "faulty"“faulty” aspects of human decision-making are replaced model is then used to predict the type of assistance that with more mathematically precise components. At one will be most useful to the user at various stages of the time, it was thought that such systems might one day re-re­ problem solving or decision-making process. place humans altogether. However, more recently, there Systems which do address image reasoning issues may has been a shift in emphasis, especially in light of the be found primarily in domains that contain sensor­sensor- fact that these autonomous intelligent systems have not derived data. Examples of such systems include AXON - a really been as successful as hoped in complex task do­ AXON really been as successful as hoped in complex task do- computer-based intelligent assistant for retrieval ofradio­of radio- mains. Rubin et ala1 [17] note that "knowledge-based“knowledge-based com­com- graphic studies [2], Intelligent Atlas - an expert system puter control systems are unable to effectively handle - puter control systems are unable to effectively handle for neuroanatomy diagnosis, which presents the inference degraded information or novel situations with ambigu­ambigu- process with both verbal expressions and image presen­presen- ous conditions in which previous guidance of an explicit tations from a pictorial database [12], and research by nature is not available".available”. The more contemporary trend Kraiss on an intelligent dual-screen workstation for fea­fea- ture extraction and interpretation of sonar data [7]. It is interesting to note that although these systems present perceptual material in the form of images, and perhaps Task even allow perceptual enhancements in the form of im­im- Analysis age processing menu choices, the primary "intelligence"“intelligence” still focuses on reasoning capabilities. The perceptual capabilities of the user have not been addressed, particu­particu- larly with respect to effects on performance. The choices of image enhancements are left up to the user, and yet there is no guarantee that improvement in appearance will lead to improvement in perfomance.performance. Our own apap­ //======'0;:;: proach to this problem is best expressed in the following (JENCODING SCHEME)) working hypotheses: 1. The application of cognitive science methods leads to anan understanding of the close interaction be-be­ Full Protocol tween the user’suser's perceptual processes and his prob-prob­ Analysis lem solving capabilities in domains where computer-computer­ displayed images form an integral part of the prob-prob­ lem solving process. Cognitive Descriptive \\ 2. The resultant cognitive models can be embedded Elements Elements Elements ) in interactive, cooperative computer systems, which are designed to provide intelligent assistance to the human agent. Such intelligence consists of knowing Contextual what type of assistance is needed, and when it may Analysis be cognitivelycognitivelyeffective effective to afford it. 3. By providing appropriate image enhancements at key nodes in the problem-solving process, the hu-hu­ Patterns Patterns Patterns man user’suser's own perceptual abilities are enhanced, and overall performance in the task is improved. 4 The emphasis of this paper is on the exploration of the Cognitive Model first of these hypotheses and presents the components of of the cognitive model which has been developed through LVisual Interaction experiment and analysis. 11.II. PROJECT BACKGROUND Figure 1: Data Analysis Methodology Our preliminary work [15] has introduced the concept of Visual Interaction as the process which links percep-percep­ The collection of both observational data and concur-concur­ tion and problem solving such that problem solving is af-af­ rent protocol data was organized in order to develop a fected by what is seen, and conversely, what is seen and thorough understanding of the task environment, and the perceived is affected by the current state of the prob-prob­ task requirements. Details of the experimental data col-col­ lem solving process. Furthermore, the early phases of lection and preliminary results are reported in [15,16]. development of a cognitively-based model of the visual An overview of the Data Analysis Methodology devel-devel­ interaction process have been described for the domain oped to extract perceptual and problem solving concepts of diagnostic radiology. This development domain was from the verbal protocols is presented in Figure 1. The chosen because the specialists are trained to extract in-in­ details of the three main stages, namely, Task Analysis, formation from data that already has a known visual Full Protocol Analysis, and Contextual Analysis are pre-pre­ representation. That is, the numerical values produced sented in [15]. In this paper, we present more concrete by the various imaging technologies are presented in the results from the Contextual Analysis stage, which have form of images of human anatomy which are particularly had a direct impact on our modelling process. meaningful to medically-trained personnel. Investigation of visual interaction in the context of such “visual"visual famil-famil­ 111.III. RESULTS OF ANALYSIS iarity”iarity" facilitates our task considerably. Furthermore, by studying an environment in which the natural layout en-en­ By partitioning the protocol statements according to tails a static stimulus (i.e.,(Le., the image) and a relatively task-related categories, and by maintaining the tempo-tempo­ stationary observer, we constrain our problem, while still ral order of these statements, we see clusters of activity obtaining realistic data. That is, we circumvent the ac-ac­ which are common to the majority of subjects for each cusation, endemic to much AI research, of creating com-com­ case. Many of these clusters can be classified as percep-percep­ pletely artificial laboratory conditions that have no con-con­ tual or problem solving events, and they provide clues to nection with the real world. thethe understanding of the visual interaction process. The firstfirst typetype of