Visual Differentiation vs. Visual Grouping: Task Types and Visual Perception Performance

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I whakaputaina i:International Journal of Business Intelligence Research vol. 16, no. 1 (2025), p. 1-25
Kaituhi matua: Chen, Fang
Ētahi atu kaituhi: Zhang, Limin
I whakaputaina:
IGI Global
Ngā marau:
Urunga tuihono:Citation/Abstract
Full Text - PDF
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022 |a 1947-3591 
022 |a 1947-3605 
024 7 |a 10.4018/IJBIR.380953  |2 doi 
035 |a 3222668867 
045 2 |b d20250101  |b d20251231 
100 1 |a Chen, Fang  |u University of Montana, USA 
245 1 |a Visual Differentiation vs. Visual Grouping: Task Types and Visual Perception Performance 
260 |b IGI Global  |c 2025 
513 |a Journal Article 
520 3 |a For business data visualization, there are two fundamental types of visual tasks: (a) differentiation tasks (identifying a data point or comparing individual data points) and (b) integration tasks (comparing sums of multiple data points and recognizing patterns). In this study, the authors propose a model of visual grouping that uses color to customize graphs for integration tasks. They conducted two experiments to investigate the effects of visual grouping on user performance across different task types. The results indicate that color grouping can enhance the outcomes of decision-making tasks, a specific type of integration task. More specifically, they found that when using graphs with visual grouping, participants spent significantly less time and achieved higher comprehension accuracy compared to those using graphs without visual grouping. 
653 |a Differentiation 
653 |a Visual tasks 
653 |a Software 
653 |a Accuracy 
653 |a Scientific visualization 
653 |a Data visualization 
653 |a Graphs 
653 |a Visual perception 
653 |a Trends 
653 |a Business intelligence 
653 |a Decision making 
653 |a Taxonomy 
653 |a Design 
653 |a Computer graphics 
653 |a Literature reviews 
653 |a Color 
653 |a Visual effects 
653 |a Pattern recognition 
653 |a Data points 
700 1 |a Zhang, Limin  |u North Dakota State University, USA 
773 0 |t International Journal of Business Intelligence Research  |g vol. 16, no. 1 (2025), p. 1-25 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3222668867/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3222668867/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch