Experience categories describe repeatedly occurring qualities of positive experiences that can be used for the analysis and generation of new/further/more positive experiences. This paper describes experience categories for the workplace. Based on 345 reports of positive user experiences in the workplace, we identified 17 experience categories through qualitative content analysis and describe their necessary and optional attributes. We believe that experience categories can support analysis and design activities for the work place in three ways: (a) using the questions derived from experience interviews to analyze existing positive experiences in work contexts, (b) explaining the potential of positive experiences in work contexts as a formal analysis tool, and (c) showing the ways in which experience categories can inform the design of software concepts to foster/generate positive user experience. The experience category approach is thus a more actionable addition to other, mainly theory-driven, approaches.

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In the present experiment we addressed the question of how the visual system determines surface lightness from luminances in the retinal image. We measured the perceived lightness of target surfaces that were embedded in custom-made checkerboards. The checkerboards consisted of 10 by 10 checks of 10 different reflectance values that were arranged randomly across the board. They were rendered under six viewing conditions including plain view, with a shadow-casting cylinder, or with one of four different transparent media covering part of the board. For each reflectance we measured its corresponding luminance in the different viewing conditions. We then assessed the lightness matches of four observers for each of the reflectances in the different viewing conditions. We derived predictions of perceived lightness based on local luminance, Michelson contrast, edge integration, anchoring theory, and a normalized Michelson contrast measure. The normalized contrast measure was the best predictor of surface lightness and was almost as good as the actual reflectance values. The normalized contrast measure combines a local computation of Michelson contrast with a region-based normalization of contrast ranges with respect to the contrast range in plain view. How the segregation of image regions is accomplished remains to be elucidated.

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