A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Karduni, Alireza; Markant, Douglas; Wesslen, Ryan; Dou, Wenwen

doi:10.1109/tvcg.2020.3029412

Cited by 26 publications

(33 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To elicit users' beliefs and uncertainty about the perceived bias, credibility, and political orientation variables as a continuous value rather than a binary choice. We adopted a modified version of the Line + Cone technique introduced by Karduni et al [12] The Line + Cone technique was specifically designed to elicit users' beliefs about correlations between two variables. Within that study, users were instructed to first select a line that best represents their belief and then draw a range that encodes other plausible alternatives to their beliefs.…”

Section: Dependent Variables and Elicitation Methodsmentioning

confidence: 99%

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Karduni¹,

Wesslen²,

Markant³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Images are an indispensable part of the news content we consume. Highly emotional images from sources of misinformation can greatly influence our judgements. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of happy and angry facial images on users' decisions. In study 2, we focus on sources' systematic emotional treatment of specific politicians. Our results show that depending on the political orientation of the source, the cumulative effect of angry facial emotions impacts users' perceived content bias and source credibility. When sources systematically portray specific politicians as angry, users are more likely to find those sources as less credible and their content as more biased. These results highlight how implicit visual propositions manifested by emotions in facial expressions might have a substantial effect on our trust of news content and sources.

show abstract

Section: Dependent Variables and Elicitation Methodsmentioning

confidence: 99%

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Karduni¹,

Wesslen²,

Markant³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additional research has also considered the intersection of visualization and financial decisions in relation to cognitive biases [57] and risk premium [56]. But one gap of this research on financial decision-making in visualization research has been the connection with research on visualizing uncertainty in which different techniques for encoding uncertainty such as Hypothetical Outcome Plots [16] or Quantile Dot Plots [8] which have different effects on users' belief-updating and decision outcomes [8,[20][21][22]24].…”

Section: Visualization In Financial Decisions and Uncertainty Visuali...mentioning

confidence: 99%

“…HOP: While static visualizations like error bars are the most common uncertainty visualization, hypothetical outcome plots (HOPs) are designed to focus on the user experiencing uncertainty information through animated draws. HOPs have been studied in a variety of applications including identifying trends [21], effect size judgments [20], and correlation judgment [22].…”

Section: Dot Plotmentioning

confidence: 99%

Effect of uncertainty visualizations on myopic loss aversion and equity premium puzzle in retirement investment decisions

Wesslen¹,

Karduni²,

Markant³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

“…These models assume individual cognition relies on Bayesian inference: an individual's implicit beliefs about the world are captured by a prior; when exposed to new information they update their prior according to Bayes' rule, arriving at posterior beliefs. Recent work applies Bayesian models of cognition to how people draw inferences when shown visualized data, either eliciting their prior beliefs about a parameter (e.g., Karduni et al (2020); Kim et al ( , 2021) or endowing priors, showing them new data, and then eliciting their posterior beliefs to compare to normative Bayesian posterior beliefs from one or more models reflecting different ways that Bayesian updating could occur.…”

Section: Graphical Inference As Bayesian Cognitionmentioning

confidence: 99%

“…Bayesian cognition has been applied to visualization-based inference in a normative sense, where a Bayesian model is used to define 'good belief updating' as a standard for comparing to or guiding people's belief updates from data (e.g., to evaluate different representations of uncertainty in data or guide belief updates (Kim et al, 2021)). It has also been used in a more descriptive sense, in which observations of people's belief updates are analyzed to gain insight into how human inference deviates (Karduni et al, 2020;, ideally approached using principled tools for model evaluation and model selection (Tauber et al, 2017). Toward both normative and descriptive applications, the mathematical basis of Bayesian inference has been used to calculate measures of graphical inference like perceived sample size, the size of the equivalent random sample that a Bayesian would have needed to see to arrive at the posterior beliefs expressed by a user .…”

Section: Graphical Inference As Bayesian Cognitionmentioning

confidence: 99%

To design interfaces for exploratory data analysis, we need theories of graphical inference

Hullman¹,

Gelman²

2021

Preprint

View full text Add to dashboard Cite

Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design philosophies that emphasize exploration over other phases of analysis risk confusing a need for flexibility with a conclusion that exploratory visual analysis is inherently "model free" and cannot be formalized. We describe how without a grounding in theories of human statistical inference, research in exploratory visual analysis can lead to contradictory interface objectives and representations of uncertainty that can discourage users from drawing valid inferences. We discuss how the concept of a model check in a Bayesian statistical framework unites exploratory and confirmatory analysis, and how this understanding relates to other proposed theories of graphical inference. Viewing interactive analysis as driven by model checks suggests new directions for software and empirical research around exploratory and visual analysis. For example, systems should enable specifying and explicitly comparing data to null and other reference distributions and better representations of uncertainty. Implications of Bayesian and other theories of graphical inference should be tested against outcomes of interactive analysis by people to drive theory development.Media Summary: Novel interactive graphical user interface tools for exploratory visual data analysis provide analysts with impressive flexibility in how to look at and interact with data. Often these systems are designed to make patterns in data as easy to see as possible. However, there are risks to designing systems for easy pattern finding alone. One risk is that the techniques used to emphasize patterns, like aggregating data by default, lead analysts to overlook variation and uncertainty in their data, leading them to draw conclusions that aren't well supported by the data. Another is that some analysts may fail to recognize the importance of confirming any insights they arrive at through visual search using confirmatory statistical modeling to make sure they are valid. One reason that graphical user interface systems for interactive analysis may not be designed to enforce strong connections between exploratory and confirmatory analysis is because there aren't well-established theories of how these two types of activities are related. We propose a perspective that unites exploratory and confirmatory analysis through the idea of graphs as model checks in a Bayesian statistical framework, and describe how in light of this view, it becomes clear that systems for exploratory visual analysis should better support model-driven inference and representation of uncertainty.

show abstract

A Bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations

Cited by 26 publications

References 47 publications

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media

Effect of uncertainty visualizations on myopic loss aversion and equity premium puzzle in retirement investment decisions

To design interfaces for exploratory data analysis, we need theories of graphical inference

Contact Info

Product

Resources

About