Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2

Euer Artikel:

Graphical Inference for Infovis Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja IEEE Transactions on Visualization and Computer Graphics (TVCG) November/December 2010 (vol. 16 no. 6):973-979, 2010.

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 (Zugriff im TU Netzwerk)

-- Theresia Gschwandtner 07:42, 08 November 2010 (CEST)

Änderungsvorschläge

Introduction

da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:

Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise. Statistics instead provides methods that can examine if an assumption can be deduced from given sample data, and is therefore used to expose invalid hypotheses. Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.

The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.

Rorschach

The Rorschach protocol was named after the Rorschach test, in which a proband has to interpret abstract ink blots. Similar to that, for the Rorschach protocol a series of null plots is generated, and presented to the proband, who is then asked to find patterns in the visualisations.

The goal of this operation is to train the senses to random deviations, and therefore reduce the effect of apophenia for the given type of visualisation.

Additional Resources

Fun and Trivia

• blog-o-sphere reactions 01
• sesame street's Line-up

InfoVis1011 9925916 12:28, 16 November 2010 (CET)