The elements of graphing data
Automating the design of graphical presentations of relational information
ACM Transactions on Graphics (TOG)
The visual display of quantitative information
The visual display of quantitative information
Graphics and managerial decision making: research-based guidelines
Communications of the ACM
A cognitive model for the perception and understanding of graphs
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing interaction
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
Semiology of graphics
Toward a Model of Knowledge-Based Graph Comprehension
DIAGRAMS '02 Proceedings of the Second International Conference on Diagrammatic Representation and Inference
Extracting Explicit and Implict Information from Complex Visualizations
DIAGRAMS '02 Proceedings of the Second International Conference on Diagrammatic Representation and Inference
Task-dependent processing of tables and graphs
Behaviour & Information Technology
A probabilistic framework for recognizing intention in information graphics
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Extending plan inference techniques to recognize intentions in information graphics
UM'03 Proceedings of the 9th international conference on User modeling
Measuring effective data visualization
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Modeling and querying graphical representations of statistical data
Web Semantics: Science, Services and Agents on the World Wide Web
The automated understanding of simple bar charts
Artificial Intelligence
Geovisualisation: sense-making and knowledge discovery with location-based data
Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction
Syntax highlighting in business process models
Decision Support Systems
Analysing the cognitive effectiveness of the UCM visual notation
SAM'10 Proceedings of the 6th international conference on System analysis and modeling: about models
Communicative signals as the key to automated understanding of simple bar charts
Diagrams'06 Proceedings of the 4th international conference on Diagrammatic Representation and Inference
Using channel theory to account for graphical meaning generations
Diagrams'06 Proceedings of the 4th international conference on Diagrammatic Representation and Inference
Toward a comprehensive model of graph comprehension: making the case for spatial cognition
Diagrams'06 Proceedings of the 4th international conference on Diagrammatic Representation and Inference
An integrated model of eye movements and visual encoding
Cognitive Systems Research
Visual scanning as a reference framework for interactive representation design
Information Visualization - Special issue on Evaluation for Information Visualization
Access to multimodal articles for individuals with sight impairments
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Rules from cognition for conceptual modelling
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
A component-based approach for specifying DSML's concrete syntax
Proceedings of the Second Workshop on Graphical Modeling Language Development
Towards a systematic understanding of graphical cues in communication through statistical graphs
Journal of Visual Languages and Computing
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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.