Z: Why vis?

Node: Visualizations enable expertise building / implicit learning

Descriptor ImplicitLearning
Argumentative standpoint Interplay
Description

Viewers build expertise on how to decode visualizations if they are constructed using specific conventions. While they do not need to be able to express their know-how, their expertise enables them to use similar visualizations effectively.

Last updated 3 years, 3 months ago (June 22, 2018) by Streeb, Dirk

Tags


Outgoing Links (1)

Incoming Links (1)

Publications (9)

D. C. Gooding 2010 Visualizing Scientific Inference 0 0 1 21 0
R. E. Patterson 2012 Cognitive Engineering, Cognitive Augmentation, and Information Display 0 0 1 9 2
W. Gaissmaier et al. 2012 Numbers Can Be Worth a Thousand Pictures: Individual Differences in Understanding Graphical and Numerical Representations of Health-Related Information 0 1 1 8 0
W. Dou et al. 2012 Toward a Deeper Understanding of the Relationship between Interaction Constraints and Visual Isomorphs 0 0 2 18 0
A. Gelman and A. Unwin 2013 Infovis and Statistical Graphics: Different Goals, Different Looks 0 0 1 21 2
R. E. Patterson et al. 2014 A Human Cognition Framework for Information Visualization 0 0 1 18 0
L. Byrne, D. Angus and J. Wiles 2016 Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives 0 0 1 8 0
A. Dasgupta et al. 2017 Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments 0 0 1 9 0
N. Kijmongkolchai, A. Abdul-Rahman and M. Chen 2017 Empirically Measuring Soft Knowledge in Visualization 0 0 1 9 0
Authors Year Title Codings Coded entities
Open Started Completed Nodes Links

Codings (9)

Streeb, Dirk: R. E. Patterson [2012]: Cognitive Engineering, Cognitive Augmentation, and Information Display doi:10.1889/JSID20.4.208 - 28.02.18 12:20 (+) Positive One or more paragraphs p. 211 Displays that present over time multiple exposures with statistical regularities may promote implicit learning and pattern-recognition-based decision making, and possibly contribute to the development of expertise in certain domains.
Streeb, Dirk: A. Gelman and A. Unwin [2013]: Infovis and Statistical Graphics: Different Goals, Different Looks doi:10.1080/10618600.2012.761137 - 27.02.18 16:15 (*) Mentioned without valuation One or more sentences p. 8 You need experience with a graphic form to use it well.
Streeb, Dirk: L. Byrne, D. Angus and J. Wiles [2016]: Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives doi:10.1109/TVCG.2015.2467321 - 25.01.18 16:19 (++) Central positive
Streeb, Dirk: W. Dou et al. [2012]: Toward a Deeper Understanding of the Relationship between Interaction Constraints and Visual Isomorphs doi:10.1177/1473871611433712 - 25.01.18 15:15 (+) Positive
Streeb, Dirk: A. Dasgupta et al. [2017]: Empirical Analysis of the Subjective Impressions and Objective Measures of Domain Scientists’ Visual Analytic Judgments doi:10.1145/3025453.3025882 - 25.01.18 15:09 (+) Positive
Streeb, Dirk: W. Gaissmaier et al. [2012]: Numbers Can Be Worth a Thousand Pictures: Individual Differences in Understanding Graphical and Numerical Representations of Health-Related Information doi:10.1037/a0024850 - 25.01.18 15:06 (+-) Ambivalent
Streeb, Dirk: N. Kijmongkolchai, A. Abdul-Rahman and M. Chen [2017]: Empirically Measuring Soft Knowledge in Visualization doi:10.1111/cgf.13169 - 25.01.18 15:04 (+) Positive
Streeb, Dirk: R. E. Patterson et al. [2014]: A Human Cognition Framework for Information Visualization doi:10.1016/j.cag.2014.03.002 - 25.01.18 14:01 (+) Positive
Streeb, Dirk: D. C. Gooding [2010]: Visualizing Scientific Inference doi:10.1111/j.1756-8765.2009.01048.x - 25.01.18 09:22 (+) Positive
Coding Affirmation Extent Reference Quote

Comments (0)