14,314 research outputs found

    Visual analytics on eye movement data reveal search patterns on dynamic and interactive maps

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    In this paper the results of a visual analytics approach on eye movement data are described which allows detecting underlying patterns in the scanpaths of the user’s during a visual search on a map. These patterns give insights in the user his cognitive processes or his mental map while working with interactive maps

    Analyzing eye movement patterns to improve map design

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    Recently, the use of eye tracking systems has been introduced in the field of cartography and GIS to support the evaluation of the quality of maps towards the user. The quantitative eye movement metrics are related to for example the duration or the number of the fixations which are subsequently (statistically) compared to detect significant differences in map designs or between different user groups. Hence, besides these standard eye movement metrics, other - more spatial - measurements and visual interpretations of the data are more suitable to investigate how users process, store and retrieve information from a (dynamic and/or) interactive map. This information is crucial to get insights in how users construct their cognitive map: e.g. is there a general search pattern on a map and which elements influence this search pattern, how do users orient a map, what is the influence of for example a pan operation. These insights are in turn crucial to be able to construct more effective maps towards the user, since the visualisation of the information on the map can be keyed to the user his cognitive processes. The study focuses on a qualitative and visual approach of the eye movement data resulting from a user study in which 14 participants were tested while working on 20 different dynamic and interactive demo-maps. Since maps are essentially spatial objects, the analysis of these eye movement data is directed towards the locations of the fixations, the visual representation of the scanpaths, clustering and aggregation of the scanpaths. The results from this study show interesting patterns in the search strategies of users on dynamic and interactive maps

    Investigating the generalizability of EEG-based Cognitive Load Estimation Across Visualizations

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    We examine if EEG-based cognitive load (CL) estimation is generalizable across the character, spatial pattern, bar graph and pie chart-based visualizations for the nback~task. CL is estimated via two recent approaches: (a) Deep convolutional neural network, and (b) Proximal support vector machines. Experiments reveal that CL estimation suffers across visualizations motivating the need for effective machine learning techniques to benchmark visual interface usability for a given analytic task
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