2021-02-09, Neth, Hansjörg, Gradwohl, Nico, Streeb, Dirk, Keim, Daniel A., Gaissmaier, Wolfgang

Cognition is both empowered and limited by representations. The matrix lens model explicates tasks that are based on frequency counts, conditional probabilities, and binary contingencies in a general fashion. Based on a structural analysis of such tasks, the model links several problems and semantic domains and provides a new perspective on representational accounts of cognition that recognizes representational isomorphs as opportunities, rather than as problems. The shared structural construct of a 2×2 matrix supports a set of generic tasks and semantic mappings that provide a unifying framework for understanding problems and defining scientific measures. Our model's key explanatory mechanism is the adoption of particular perspectives on a 2×2 matrix that categorizes the frequency counts of cases by some condition, treatment, risk, or outcome factor. By the selective steps of filtering, framing, and focusing on specific aspects, the measures used in various semantic domains negotiate distinct trade-offs between abstraction and specialization. As a consequence, the transparent communication of such measures must explicate the perspectives encapsulated in their derivation. To demonstrate the explanatory scope of our model, we use it to clarify theoretical debates on biases and facilitation effects in Bayesian reasoning and to integrate the scientific measures from various semantic domains within a unifying framework. A better understanding of problem structures, representational transparency, and the role of perspectives in the scientific process yields both theoretical insights and practical applications.

2021, Neth, Hansjörg

This book provides an introduction to data science that is tailored to the needs of students in psychology, but is also suitable for students of the humanities and other biological or social sciences. This audience typically has a basic familiarity with statistics, but rarely an idea how data is prepared to allow for statistical testing. By working with a variety of data types and many examples, this text teaches strategies and tools for transforming, summarizing, and visualizing data. By keeping our eyes open for the perils of misleading representations, the book fosters fundamental skills of data literacy and cultivates reproducible research practices that enable and precede any practical use of statistics.

2021-01-13, Streeb, Dirk, Metz, Yannick, Schlegel, Udo, Schneider, Bruno, El-Assady, Mennatallah, Neth, Hansjörg, Chen, Min, Keim, Daniel A.

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.

2020-02, Giese, Helge, Neth, Hansjörg, Moussaïd, Mehdi, Betsch, Cornelia, Gaissmaier, Wolfgang

Background
Online discussions may impact the willingness to get vaccinated. This experiment tests how groups of individuals with consistent and inconsistent attitudes towards flu vaccination attend to and convey information online, and how they alter their corresponding risk perceptions.

Methods
Out of 1859 MTurkers, we pre-selected 208 people with negative and 221 people with positive attitudes towards flu vaccinations into homogeneous or heterogeneous 3-link experimental diffusion chains. We assessed (i) which information about flu vaccinations participants conveyed to the subsequent link, (ii) how flu-vaccination related perceptions were altered by incoming messages, and (iii) how participants perceived incoming information.

Results
Participants (i) selectively conveyed attitude-consistent information, but exhibited no overall anti-vaccination bias, (ii) were reluctant to alter their flu-vaccination related perceptions in response to messages, and (iii) evaluated incoming information consistent with their prior attitudes as more convincing.

Discussion
Flu-vaccination related perceptions are resilient against contradictions and bias online communication. Contrary to expectations, there was no sign of amplification of anti-vaccine attitudes by online communication.

2021, Giese, Helge, Neth, Hansjörg, Gaissmaier, Wolfgang

Objective
Social media are an increasingly important source of information on the benefits and risks of vaccinations, but the high prevalence of misinformation provides challenges for informed vaccination decisions. It is therefore important to understand which messages are likely to diffuse online and why, and how relevant aspects—such as scientific facts on vaccination effectiveness—can be made more comprehensible and more likely to be shared. In two studies, we (i) explore which characteristics of messages on flu vaccination facilitate their diffusion in online communication, and (ii) whether visual displays (i.e., icon arrays) facilitate the comprehension and diffusion of scientific effectiveness information.

Methods
In Study 1, 208 participants each rated a random sample of 15 out of 63 messages on comprehensibility, trustworthiness, persuasiveness, familiarity, informativeness, valence, and arousal, and then reported which information they would share with subsequent study participants. In Study 2 (N = 758), we employed the same rating procedure for a selected set of 9 messages and experimentally manipulated how scientific effectiveness information was displayed.

Results
Study 1 illustrated that scientific effectiveness information was difficult to understand and thus did not diffuse well. Study 2 demonstrated that visual displays improved the understanding of this information, which could, in turn, increase its social impact.

Conclusions
The comprehensibility of scientific information is an important prerequisite for its diffusion. Visual displays can facilitate informed vaccination decisions by rendering important information on vaccination effectiveness more transparent and increasing the willingness to share this information.