Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data

@article{Kim2017ExplainingTG,
  title={Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data},
  author={Yea-Seul Kim and Katharina Reinecke and Jessica R. Hullman},
  journal={Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems},
  year={2017}
}
Information visualizations use interactivity to enable user-driven querying of visualized data. However, users' interactions with their internal representations, including their expectations about data, are also critical for a visualization to support learning. We present multiple graphically-based techniques for eliciting and incorporating a user's prior knowledge about data into visualization interaction. We use controlled experiments to evaluate how graphically eliciting forms of prior… 

Figures and Tables from this paper

Data Through Others' Eyes: The Impact of Visualizing Others' Expectations on Visualization Interpretation
TLDR
It was found that social information that exhibits a high degree of consensus lead participants to recall the data more accurately relative to participants who were exposed to the data alone and trusted the accuracy of the data less.
Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics
TLDR
PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, is introduced, as an alternative to data-driven exploration and suggests that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis.
Pushing the (Visual) Narrative: The Effects of Prior Knowledge Elicitation in Provocative Topics
TLDR
It is found that incorporating priors does not significantly affect attitudinal change, but participants who externalized their beliefs expressed greater surprise at the data, and visualizations are more persuasive than equivalent textual data representations for exposing contentious issues.
Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
TLDR
An exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data.
A Bayesian Cognition Approach to Improve Data Visualization
TLDR
A Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs is demonstrated and it is shown how this model provides a guide for improving visualization evaluation.
Does Interacting Help Users Better Understand the Structure of Probabilistic Models?
TLDR
The results suggest that improvements in the understanding of the interaction group are most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations in comparison to the static group, and interaction improves users’ confidence.
Towards Concept-Driven Visual Analytics
TLDR
This work proposes a new paradigm for ‘concept-driven’ visual analysis, in which analysts share their conceptual models and hypotheses with the system, and the system then uses those inputs to drive the generation of visualizations, while providing plots and interactions to explore places where models and data disagree.
Modeling the Interpretation of Visualized Statistics as Bayesian Cognition
TLDR
A Bayesian cognitive model is proposed for understanding how people interpret visualizations in light of prior beliefs, and evaluating visualization designs using rational belief updating as a target, and how normative Bayesian inference can be used to evaluate visualizations, including of uncertainty.
Visualizations as Data Input?
TLDR
This paper argues for the deeper examination of input visualizations, highlighting a set of recent examples and introducing vocabulary for characterizing them, and presents a series of provocations which examine some of the challenges posed byinput visualizations and suggest opportunities for better understanding this type of visual representations and their potential.
reVISit: Looking Under the Hood of Interactive Visualization Studies
TLDR
The findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 65 REFERENCES
Mental Models, Visual Reasoning and Interaction in Information Visualization: A Top-down Perspective
TLDR
A top-down perspective of reasoning as model construction and simulation is presented, and the role of visualization in model based reasoning is discussed, and interaction can be understood as active modeling for three primary purposes: external anchoring, information foraging, and cognitive offloading.
Graphs as aids to knowledge construction: Signaling techniques for guiding the process of graph comprehension.
TLDR
It is established that graphs can be redesigned to improve viewers' interpretations by minimizing the inferential processes and maximizing the pattern association processes required to interpret relevant information.
Effects of knowledge and display design on comprehension of complex graphics
The effects of self-explaining when learning with text or diagrams
Learning from Worked-Out Examples: The Effects of Example Variability and Elicited Self-Explanations
TLDR
It was found that the acquisition of transferable knowledge can be supported by eliciting self-explanations, and especially learners with low levels of prior topic knowledge profited from the elicitation procedure.
A Model of the Perceptual and Conceptual Processes in Graph Comprehension
TLDR
A model of the comprehension of line graphs that emphasizes the close interaction between conceptual processes, such as interpreting labels and scales, and perceptual processes,such as encoding and interpreting the slopes and patterns of the lines themselves is proposed.
Eliciting Self-Explanations Improves Understanding
TLDR
It is shown that self-explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text, and three processing characteristics of self-explaining are considered as reasons for the gains in deeper understanding.
The impact of social information on visual judgments
TLDR
This work addresses how social information signals (social proof) affect quantitative judgments in the context of graphical perception, and describes how these findings can be applied to collaborative visualization systems to produce more accurate individual interpretations in social contexts.
Towards Tutorial Dialog to Support Self- Explanation: Adding Natural Language Understanding to a Cognitive Tutor *
TLDR
A prototype of a cognitive tutor that understands students' explanations and provides feedback is implemented, which uses a knowledge-based approach to natural language understanding and is entering a phase of pilot testing.
Diagrams in the Mind and in the World: Relations between Internal and External Visualizations
TLDR
This paper examines three possible relations that might exist between internal and external visualization and argues that the design of external visualizations should be informed by research on internal visualization skills, and that the development of technologies for external visualization calls for more research on the nature of internal visualization abilities.
...
1
2
3
4
5
...