Given the resources needed to launch a retail store on the Internet or change an existing online storefront design, it is important to allocate product development resources to interface features that actually improve store traffic and sales.Using a regression model, we predict store traffic and dollar sales as a function of interface design features such as number of links into the store, image sizes, number of products, and store navigation features.By quantifying the benefits of user interface features, we hope to facilitate the process of designing and evaluating alternative storefronts by identifying those features with the greatest impact on tra.fEc and sales.

This research extends previous graphics research by examining how individual differences in working memory (WM) capacity and changes in graphic design influence graphical information processing. An experiment compared decision accuracy of two graphic decision aids and an unaided group for a task at two levels of complexity. There were no accuracy differences for the low complexity task. At high levels of task complexity, accuracy depended upon WM capacity and how the graphic aid influenced perception. Eye movement data show information processing differences also are contingent upon graphic design features and WM capacity. We postulate that graphs reduce cognitive overhead by shifting some of the cognitive burden to our visual perception system. More efficient graphical perceptual will improve decision performance only if our cognitive resources are capacity constrained and those cognitive resources are used elsewhere in the problem solving process.

The primary concern in designing an interface for an electronic trading system is the impact on market liquidity [9].Current systems make use of efficient order-execution algorithms but fail to capture elements of the trading floor that contribute to an efficient market [9].We briefly describe tasks conducted in futures pit trading and current off-hours electronic trading systems.Understanding the tasks helps define key components to an interface for electronic trading.These include visualization of the market and its participants, a trading process which allows active participation and price discovery as well as concurrent interaction among each of the participants.

We evaluate diagramming techniques for systems analysts and programmers from a cognitive perspective. This focuses on how people process information from system diagrams and how diagrams support the cognitive limitations of systems analysts and programmers. The paper increases awareness of the analyst's need for different information views during the systems development process and provides steps for improving the comprehension and communication of diagrammatic information. The examples provide ways to develop better diagrams given current tools for their development. We hope that future diagramming tools will reflect the cognitive limitations of the analyst by actively highlighting and dynamically governing the flow of graphic information across multiple information processing views.

Recently, researchers have started using texture for data visualization. The rationale behind this is to exploit the sensitivity of the human visual system to texture in order to overcome the limitations inherent in the display of multidimensional data. A fundamental issue that must be addressed is what textural features are important in texture perception, and how they are used. We designed an experiment to help identify the relevant higher order features of texture perceived by humans. We used twenty subjects, who were asked to rate 56 pictures from Brodatz's album on 12 nine-point Likert scales. We applied the techniques of hierarchical cluster analysis, non-parametric multidimensional scaling (MDS), classification and regression tree analysis (CART), discriminant analysis, and principal component analysis to data gathered from the subjects. Based on these techniques, we identified three orthogonal dimensions for texture to be repetitive vs. non-repetitive; high-contrast and non-directional vs. low-contrast and directional; granular, coarse and low-complexity vs. non-granular, fine and high-complexity.>

A fundamental issue in texture analysis is that of deciding what textural features are important in texture perception, and how they are used. Experiments on human preattentive vision have identified several low-level features (such as orientation of blobs and size of line segments), which are used in texture perception. However, the question of what higher level features of texture are used has not been adequately addressed. We designed an experiment to help identify the relevant higher order features of texture perceived by humans. We used 20 subjects, who were asked to perform an unsupervised classification of 30 pictures from Brodatz′s album on texture. Each subject was asked to group these pictures into as many classes as desired. Both hierarchical cluster analysis and nonparametric multidimensional scaling (MDS) were applied to the pooled similarity matrix generated from the subjects′ groupings. A surprising outcome is that the MDS solutions fit the data very well. The stress in the two-dimensional case is 0.10, and the stress in the three-dimensional case is 0.045. We rendered the original textures in these coordinate systems, and interpreted the (rotated) axes. It appears that the axes in the 2D case correspond to periodicity versus irregularity, and directionality versus nondirectionality. In the 3D case, the third dimension represents the structural complexity of the texture. Furthermore, the clusters identified by the hierarchical cluster analysis remain virtually intact in the MDS solution. The results of our experiment indicate that people use three high-level features for texture perception. Future studies are needed to determine the appropriateness of these high-level features for computational texture analysis and classification.

This article describes a computer program, UCIE (Understanding Cognitive Information Engineering) that simulates graphical perception. UCIE predicts response time to answer a question posed to a graphic display from assumptions about the sequence of eye fixations, short-term memory capacity and duration limits, and the degree of difficulty to acquire information in each glance. An empirical study compared actual performance to UCIE predictions over a range of display types and question types. The results yielded some support for the cognitive model. A zero-parameter model explains 37% of the variance in average reaction times (N = 1,128). However, the zero-parameter model only explains about 10% of the individual variation in reaction times across 28 subjects (N = 15,200). Although this is an important start to understand how we interpret visual displays for meaning, additional research is needed to explain individual differences in performance.

An exploratory effort to classify visual representations into homogeneous clusters is discussed. The authors collected hierarchical sorting data from twelve subjects. Five principal groups of visual representations emerged from a cluster analysis of sorting data: graphs and tables, maps, diagrams, networks, and icons. Two dimensions appear to distinguish these clusters: the amount of spatial information and cognitive processing effort. The authors discuss visual information processing issues relevant to the research, methodology and data analyses used to develop the classification system, results of the empirical study, and possible directions for future research.>