Towards a 3-dimensional model of individual cognitive differences

Peck, Evan M.; Yuksel, Beste F.; Harrison, Lane; Ottley, Alvitta; Chang, Remco

doi:10.1145/2442576.2442582

Cited by 23 publications

(15 citation statements)

References 50 publications

(75 reference statements)

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“…That said, LOC is a multidimensional construct and there are also many other cognitive traits and states that may impact performance on visualization systems [11]. Thus, it is important for us to better understand how these factors, individually and collectively, impact the usability of our designs.…”

Section: Chi 2015 Crossings Seoul Koreamentioning

confidence: 99%

Personality as a Predictor of User Strategy

Ottley

Yang

Chang

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Individual differences matter. While this has been the theme for many recent works in the Visualization and HCI communities, the mystery of how to develop personalized visualizations remains. This is largely because very little is known about how users actually use visualizations to solve problems and even less is known about how individual differences affect these problem-solving strategies. In this paper, we provide evidence that strategies are indeed influenced by individual differences. We demonstrate how the personality trait locus of control impacts strategies on hierarchical visualizations, and we introduce design recommendations for personalized visualizations.

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Section: Chi 2015 Crossings Seoul Koreamentioning

confidence: 99%

Personality as a Predictor of User Strategy

Ottley

Yang

Chang

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…To evaluate data type the accompanied dimensions need to be considered (Kuang et al, 2012) and for data density the amount of data compressed in a visualization (Gelman and Unwin, 2013). • Individual Complexity: Individual complexity can be clustered into three fundamental dimensions: Cognitive traits representing a persons' working memory ability, cognitive states representing situational and emotional influences and experience and biases (Peck et al, 2012). Situational and emotional states revere to a persons' origin and current state.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Understanding how visualizations affect a user's perception is highly complex as it is influenced by the task and the data at hand as well as by individual factors such as experience, knowledge or culture Parush et al, 2007;Peck et al, 2012;Yigitbasioglu and Valcu, 2012). So far, research in the area of information visualization lacks a means and method of determining the quality of a graphical representation for such purposes a-priori (Peck et al, 2012;Ziemkiewicz and Kosara, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…So far, research in the area of information visualization lacks a means and method of determining the quality of a graphical representation for such purposes a-priori (Peck et al, 2012;Ziemkiewicz and Kosara, 2010). To advance theory, the authors introduce a new triangulated and validated holistic model based on structural equation modeling.…”

Section: Introductionmentioning

confidence: 99%

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Deriving a holistic cognitive fit model for an optimal visualization of data for management decisions

Falschlunger

Lehner

Grabmann

et al. 2015

Proceedings of the 2nd International Symposium on Partial Least Squares Path Modeling: The Conference for PLS Users

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Research shows that managerial decision making is directly correlated to both, the swift availability, and subsequently the ease of interpretation of the relevant information. Visualizations are already widely used to transform raw data into a more understandable format and to compress the constantly growing amount of information produced. However, research in this area is highly fragmented and results are contradicting. This paper proposes a preliminary model based on an extensive literature review including top current research on cognition theory. Furthermore an early stage validation of this model by experimental research using structural equation modeling is presented. The authors are able to identify task complexity as one of the most important predicting variables for information perception of visual data, however, other influences are significant as well (data density, domain expertise, working memory capacity and subjective visual complexity).

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“…Only some of those papers address cognitive processes while evaluating interactive visualization systems [8,34,40,43]. Other papers focus on cognitive strategies and evaluate those [2, 10,26,39,49] mostly leaving out an evaluation of interaction. We believe that specifically for an evaluation of interaction as an integral part of visualization, cognitive strategies must be included.…”

Section: Introductionmentioning

confidence: 99%

Towards analyzing eye tracking data for evaluating interactive visualization systems

Blascheck¹,

Ertl²

2014

Proceedings of the Fifth Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization

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Eye tracking can be a suitable evaluation method for determining which regions and objects of a stimulus a human viewer perceived. Analysts can use eye tracking as a complement to other evaluation methods for a more holistic assessment of novel visualization techniques beyond time and error measures. Up to now, most stimuli in eye tracking are either static stimuli or videos. Since interaction is an integral part of visualization, an evaluation should include interaction. In this paper, we present an extensive literature review on evaluation methods for interactive visualizations. Based on the literature review we propose ideas for analyzing eye movement data from interactive stimuli. This requires looking critically at challenges induced by interactive stimuli. The first step is to collect data using different study methods. In our case, we look at using eye tracking, interaction logs, and thinking-aloud protocols. In addition, this requires a thorough synchronization of the mentioned study methods. To analyze the collected data new analysis techniques have to be developed. We investigate existing approaches and how we can adapt them to new data types as well as sketch ideas how new approaches can look like.

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Towards a 3-dimensional model of individual cognitive differences

Cited by 23 publications

References 50 publications

Personality as a Predictor of User Strategy

Personality as a Predictor of User Strategy

Deriving a holistic cognitive fit model for an optimal visualization of data for management decisions

Towards analyzing eye tracking data for evaluating interactive visualization systems

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