Two experiments comparing user performance on ClearType and Regular displays are reported. In the first, 26 participants scanned a series of spreadsheets for target information. Speed of performance was significantly faster with ClearType. In the second experiment, 25 users read two articles for meaning. Reading speed was significantly faster for ClearType. In both experiments no differences in accuracy of performance or visual fatigue scores were observed. The data also reveal substantial individual differences in performance suggesting ClearType may not be universally beneficial to information workers.

This item is not the definitive copy. Please use the following citation when referencing this material: Dillon, A. (1992) Reading from paper versus screens: a critical review of the empirical literature. Ergonomics, 35(10), 1297-1326. Abstract: The advent of widespread computer use in general and increasing developments in the domain of hypertext in particular have increased awareness of the issue of reading electronic text. To date the literature has been dominated by reference to work on overcoming speed deficits resulting from poor image quality but an emerging literature reveals a more complex set of variables at work. The present review considers the differences between the media in terms of outcomes and processes of reading and concludes that single variable explanations are insufficient to capture the range of issues involved in reading from screens.

This is a preprint of a paper published (with a slightly different title: Spatial semantics and individual differences in the perception of shape in information space) in the Journal of the American Society for Information Science, 51(6), 521-528. Abstract: User problems with large information spaces multiply in complexity when we enter the digital domain. Virtual information environments can offer 3-D representations, reconfigurations and access to large databases that can overwhelm many usersâ abilities to filter and represent. As a result, users frequently experience disorientation in navigating large digital spaces to locate and use information. To date, the research response has been predominantly based on the analysis of visual navigational aids that might support users' bottom-up processing of the spatial display. In the present paper an emerging alternative is considered that places greater emphasis on the top-down application of semantic knowledge by the user gleaned from their experiences within the socio-cognitive context of information production and consumption. A distinction between spatial and semantic cues is introduced and existing empirical data are reviewed that highlight the differential reliance on spatial or semantic information as domain expertise of the user increases. The conclusion is reached that interfaces for shaping information should be built on an increasing analysis of users' semantic processing.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Can on-line information retrieval systems negotiate the diverse vocabularies of different users? This article suggests a robust algorithmic solution to the vocabulary problem in collaborative systems.

The present paper reviews the general usability framework that has dominated discussion in the field of human-computer interaction (HCI) and finds it wanting. An alternative view of the important determinants of user experience of interactive devices is presented with examples.

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Microtask marketplaces provide shortcuts for automating tasks that are otherwise intractable for computers. Creative tasks fall squarely within this definition, and microtask marketplaces have been heavily leveraged to this end. Brainstorming is often an implicit component of these solutions. This thesis provides the first foundational study of brainstorming in microtask marketplaces, aimed at solving the open problems in brainstorming task design to make this process more accessible and effective. This is achieved by establishing techniques for coding brainstorming data at scale, models for quantifying desirable outcomes of brainstorming, and a qualitative deconstruction of brainstorming strategies employed in this environment. Idea forests are introduced as a data structure to enable the disambiguation of ideas in large corpuses, providing natural measures of two metrics of primary interest in brainstorming research: quantity and novelty. They are constructed via a tree-traversal algorithm, restricting the subset of the corpus which the coder must be aware of when making decisions. A simulation approach is introduced to assess the validity of hypothesis outcomes derived from idea forest metrics. The introduction of idea forests enables the core contribution of this thesis, a set of quantitative models for brainstorming outcomes. This thesis extracts several actionable conclusions from the parameters of these models: the rate of unique idea generation is subject to decay over time; individuals have a significant effect on the rate of idea generation, with productive workers generating dozens more unique ideas; and individuals generate their most novel ideas late in a brainstorming session, after the first 18 responses. Furthermore, a replication of findings by Nijstad and Stroebe is conducted, finding that workers take more time to generate ideas when changing semantic categories and are more likely to remain within a category than expected by chance. Finally, a taxonomy of strategies employed by brainstormers is presented. In particular, this thesis discusses the phenomena of scoping brainstorming problems, providing partial solutions, and riffing on previous solutions.

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