Examining the Validity of the Exemplar-Based Classifier in Identifying Decision Strategy with Eye-Movement Data

In this study, an exemplar-based classifier was developed to predict which decision strategy may underlie an empirical ocular search behavior. Our rationale was mainly inspired by the exemplar-based models of categorization; that is, different decision strategies are conceived as different concepts, with the exemplar referring to the sequence of empirical fixations on decision information during a decision process. In order to ascertain the best exemplar of each strategy for our classifier, the Tabu search algorithm was applied. An eye-tracking based experiment was conducted to collect fixation data for training and validation. Our result showed that the classifier has significant accuracy in identifying underlying strategies, achieving an average hit-ratio of 76%. This indicated to us that the integration of the exemplar classifier with fixation data has certain applicable value for leveraging the adaptability of DSSs. Our result also has some important implications for the direction and methodology of behavioral decision researc

Using eye-tracking into decision makers evaluation in evolutionary interactive UA-FLP algorithms

Unequal area facility layout problem is an important issue in the design of industrial plants, as well as other fields such as hospitals or schools, among others. While participating in an interactive designing process, the human user is required to evaluate a high number of proposed solutions, which produces them fatigue both mental and physical. In this paper, the use of eye-tracking to estimate user’s evaluations from gaze behavior is investigated. The results show that, after a process of training and data taking, it is possible to obtain a good enough estimation of the user’s evaluations which is independent of the problem and of the users as well. These promising results advice to use eye-tracking as a substitute for the mouse during users’ evaluations

Inhibitory control in mind and brain 2.0: blocked-input models of saccadic countermanding.

The interactive race model of saccadic countermanding assumes that response inhibition results from an interaction between a go unit, identified with gaze-shifting neurons, and a stop unit, identified with gaze-holding neurons, in which activation of the stop unit inhibits the growth of activation in the go unit to prevent it from reaching threshold. The interactive race model accounts for behavioral data and predicts physiological data in monkeys performing the stop-signal task. We propose an alternative model that assumes that response inhibition results from blocking the input to the go unit. We show that the blocked-input model accounts for behavioral data as accurately as the original interactive race model and predicts aspects of the physiological data more accurately. We extend the models to address the steady-state fixation period before the go stimulus is presented and find that the blocked-input model fits better than the interactive race model. We consider a model in which fixation activity is boosted when a stop signal occurs and find that it fits as well as the blocked input model but predicts very high steady-state fixation activity after the response is inhibited. We discuss the alternative linking propositions that connect computational models to neural mechanisms, the lessons to be learned from model mimicry, and generalization from countermanding saccades to countermanding other kinds of responses

Human performance prediction in man-machine systems. Volume 1 - A technical review

Tests and test techniques for human performance prediction in man-machine systems task

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference

Dissemination of aerospace science and technology to highway safety and highway transportation programs Final report, 1 Jan. - 1 Apr. 1968

Feasibility of NASA literature application to highway safety and transportation engineerin

Engineering Adaptive Interfaces – Enhancement of Comprehension and Decision-Making

The role of information systems is growing steadily and permeating more and more all levels of our society. Meanwhile, information systems have to support different user groups in various decision situations simultaneously. Hence, the existing design approach to creat- ing a unified user interface is reaching its limits. This work examines adaptive information system design by investigating user-adaptive information visualization and situation-aware nudging. An exploratory eye-tracking study investigates participants’ perception and comprehension of different financial visualizations and shows that none of them can be preferred across the board. Moreover, it reveals expertise knowledge as the research direction for visualization recommendations. Afterward, two empirical studies are conducted to relate different visualizations to participants’ domain-specific knowledge. The first study, conducted with a broad sample of the population, shows that financial and graphical literacy increases participants’ financial decision-making competency with certain visualizations. The second study, conducted with a more specific sample and an additional visualization, underlines a large part of the first study’s results. Additionally, it identifies statistical literacy as an increasing factor in financial decision-making. Both studies are demonstrating that different visualizations cause different cognitive loads despite the same amount of information. After all, the results are used to derive visualization recommendations based on domain-specific knowledge and cognitive load. This work also investigates the situation-aware effectiveness of nudging with the example of decision inertia. In a preliminary study, an experimental task is systematically transferred to different situational contexts by observing situational user characteristics. The identified contexts are examined in a subsequent large-scale empirical study with different nudges to reduce decision inertia. The results show gender-specific differences in decision inertia across the context. Hence, information system design has to adapt to gender and situational user characteristics to support users in their decision-making. Moreover, the study delivers empirical evidence for the contextual effectiveness of nudg- ing. Future nudging research has to incorporate situational user characteristics to provide effective nudges in different situational contexts. Especially, further fundamental research is needed to understand the situational effectiveness of nudging. The study identifies in- dividual situational preferences as one promising research stream

Human Problem Solving in 2012

This paper presents a bibliography of 263 references related to human problem solving, arranged by subject matter. The references were taken from PsycInfo and Academic Premier database. Journal papers, book chapters, and dissertations are included. The topics include human development, education, neuroscience, and research in applied settings. It is argued that researchers are more and more engaged with problem solving research because of its centrality in human actions and because society needs advice from science in understanding and solving complex problems

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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