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1. A splash of color: a dual dive into the effects of EVO on decision-making with goal models

   https://doi.org/10.1007/s00766-024-00422-5  

   Baatartogtokh, Yesugen; Foster, Irene; Grubb, Alicia M (September 2024, Requirements Engineering)

   Recent approaches have investigated assisting users in making early trade-off decisions when the future evolution of project elements is uncertain. These approaches have demonstrated promise in their analytical capabilities; yet, stakeholders have expressed concerns about the readability of the models and resulting analysis, which builds upon Tropos. Tropos is based on formal semantics enabling automated analysis; however, this creates a problem of interpreting evidence pairs. The aim of our broader research project is to improve the process of model comprehension and decision-making by improving how analysts interpret and make decisions. We extend and evaluate a prior approach, called EVO, which uses color to visualize evidence pairs. In this article, we explore the effectiveness of EVO with and without the impacts of tooling through a two-phased empirical study. All subjects in both phases were untrained modelers, given training at study time. First, we conduct an experiment to measure any effect of using colors to represent evidence pairs. Second, we explore how subjects engage in decision-making activities (with or without color) through a user study. We find that the EVO color visualization significantly improves the speed of model comprehension and is perceived as helpful by study subjects. 

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2. Leveraging Machine Learning to Analyze Semantic User Interactions in Visual Analytics

   https://doi.org/10.3390/info15060351  

   Jeong, Dong Hyun; Jeong, Bong Keun; Ji, Soo Yeon (June 2024, Information)

   In the field of visualization, understanding users’ analytical reasoning is important for evaluating the effectiveness of visualization applications. Several studies have been conducted to capture and analyze user interactions to comprehend this reasoning process. However, few have successfully linked these interactions to users’ reasoning processes. This paper introduces an approach that addresses the limitation by correlating semantic user interactions with analysis decisions using an interactive wire transaction analysis system and a visual state transition matrix, both designed as visual analytics applications. The system enables interactive analysis for evaluating financial fraud in wire transactions. It also allows mapping captured user interactions and analytical decisions back onto the visualization to reveal their decision differences. The visual state transition matrix further aids in understanding users’ analytical flows, revealing their decision-making processes. Classification machine learning algorithms are applied to evaluate the effectiveness of our approach in understanding users’ analytical reasoning process by connecting the captured semantic user interactions to their decisions (i.e., suspicious, not suspicious, and inconclusive) on wire transactions. With the algorithms, an average of 72% accuracy is determined to classify the semantic user interactions. For classifying individual decisions, the average accuracy is 70%. Notably, the accuracy for classifying ‘inconclusive’ decisions is 83%. Overall, the proposed approach improves the understanding of users’ analytical decisions and provides a robust method for evaluating user interactions in visualization tools. 

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3. Modeling End-User Affective Discomfort With Mobile App Permissions Across Physical Contexts

   https://doi.org/10.14722/usec.2025.23023  

   Wu, Yuxi; Logas, Jacob; Ponda, Devansh Jatin; Haines, Julia; Li, Jiaming; Nichols, Jeffrey; Edwards, W Keith; Das, Sauvik (February 2025, Internet Society)

   Users make hundreds of transactional permission decisions for smartphone applications, but these decisions persist beyond the context in which they were made. We hypothesized that user concern over permissions varies by context, e.g., that users might be more concerned about location permissions at home than work. To test our hypothesis, we ran a 44-participant, 4-week experience sampling study, asking users about their concern over specific application-permission pairs, plus their physical environment and context. We found distinguishable differences in participants’ concern about permissions across locations and activities, suggesting that users might benefit from more dynamic and contextually-aware approaches to permission decision-making. However, attempts to assist users in configuring these more complex permissions should be made with the aim to reduce concern and affective discomfort—not to normalize and perpetuate this discomfort by replicating prior decisions alone. 

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4. Development of food bank data visualizations using eye tracking

   https://doi.org/10.54941/ahfe1005087  

   Jiang, Steven; Ivuawuogu, Henry; Milad_Javadi, Amir; Hamilton, Mikaya; Davis, Lauren (January 2024, AHFE International, USA)

   Leitner, Christine; Nägele, Rainer; Bassano, Clara; Satterfield, Debra (Ed.)

   Food banks are key players in the fight against hunger. The complexity of the food bank operations data makes decision-making very challenging. Data visualization can allow food bank operations managers to quickly and easily understand the data and make evidence-based decisions. However, poorly designed visualizations could be confusing and/or misleading. This study uses eye-tracking technology to understand how users interact with various food bank data visualizations and use eye-tracking data to better design those visualizations. The findings of this study will have an impact on improving the effectiveness and efficiency of the food bank operations. 

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5. A parameter recovery assessment of a wide class of evidence accumulation models of decision-making.

   https://doi.org/10.31234/osf.io/3ca6y_v1  

   Murrow, Matthew; Holmes, William (May 2024, OSF)

   Computational modeling has become indispensable in investigating the dynamics of decision making processes. A prominent category of models in this domain are Evidence Accumulation Models (EAMs), which model both the decisions people make and the time they take. Many variations have been proposed which modify the drift rate, diffusion rate, and decision thresholds, encoding increasingly complex dynamics into the EAM framework. However, adding model features complicates parameter recovery, making model interpretation more difficult. In this work, we perform a parameter recovery study to a variety of common binary choice EAMs, identify the specific challenges for each, and explore how to improve their parameter recoverability. Though previous studies have addressed this question, they have been piecemeal in nature, with different groups applying different computational methods to study different models. We aim to unify this body of literature using the best currently available computational methods. Further, we present the first, to our knowledge, Bayesian analysis of diffusion conflict models. Our purpose here is to be thorough, not exhaustive or comprehensive. With this in mind, this article catalogues a number of results, some previously shown and some new. Further, it illustrates different approaches to model analysis. This article is intended to be a resource for researchers interested in utilizing EAMs for studying decision-making processes, providing insights into the challenges associated these models, how to analyze them in light of those challenges, and examples of how to address those challenges. 

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