Pub Date : 2024-10-04DOI: 10.1016/j.ijhcs.2024.103381
Joanna Bergström, Kasper Hornbæk
The user interface is a central concept in human–computer interaction, but peculiarly fuzzy. This affects discussions of the fundamentals of our discipline and the positioning of our work. We propose a model of the user interface that consists of four elements: Devices, Interaction Techniques, Representations, and Assemblies (DIRA). We explain their roles in the user interface and discuss some associated concerns about evaluation and design. We then show how to use the model to describe the elements of user interfaces (with examples that include a menu, a fisheye interface, and notifications) and to analyze the central characteristics of user interface paradigms (including tangible user interfaces and mixed reality). Finally, we discuss how describing user interfaces with the model can drive their design and evaluation.
{"title":"DIRA: A model of the user interface","authors":"Joanna Bergström, Kasper Hornbæk","doi":"10.1016/j.ijhcs.2024.103381","DOIUrl":"10.1016/j.ijhcs.2024.103381","url":null,"abstract":"<div><div>The user interface is a central concept in human–computer interaction, but peculiarly fuzzy. This affects discussions of the fundamentals of our discipline and the positioning of our work. We propose a model of the user interface that consists of four elements: Devices, Interaction Techniques, Representations, and Assemblies (DIRA). We explain their roles in the user interface and discuss some associated concerns about evaluation and design. We then show how to use the model to describe the elements of user interfaces (with examples that include a menu, a fisheye interface, and notifications) and to analyze the central characteristics of user interface paradigms (including tangible user interfaces and mixed reality). Finally, we discuss how describing user interfaces with the model can drive their design and evaluation.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103381"},"PeriodicalIF":5.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ijhcs.2024.103379
Olga Iarygina , Kasper Hornbæk , Aske Mottelson
Demand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance.
In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.
{"title":"Demand characteristics in human–computer experiments","authors":"Olga Iarygina , Kasper Hornbæk , Aske Mottelson","doi":"10.1016/j.ijhcs.2024.103379","DOIUrl":"10.1016/j.ijhcs.2024.103379","url":null,"abstract":"<div><div>Demand characteristics refer to cues that can inform participants in experiments about the hypothesis and influence their behavior. They lead researchers to erroneously infer non-existing effects, undermining the experimental integrity of empirical studies. Despite a widespread acknowledgment of their confounding influence in experimental psychology, experiments involving humans and computers to a lesser extent consider effects of demand characteristics, as computerized protocols are thought to be immune to some experimenter biases. Furthermore, demand characteristics are considered to mainly effect subjective measures. As a result, demand characteristics often remain uncontrolled in studies involving computers, and in particular for objective measures such as performance.</div><div>In this paper, we present two experiments that underline the importance of demand characteristics in human–computer interaction experiments. In a text-entry study, we made participants believe they were evaluating a research-based keyboard. This belief led to increased performance and self-reported user experience. In a second study, we conducted a thought experiment on the illusion of body ownership in virtual reality, where the experimental design indicated the study hypothesis. We found hypothesis-compliant responses from participants, even when they did not experience the illusion. We conclude that demand characteristics pose a significant challenge to the interpretation and validity of human–computer experiments, even when they are fully automated. We discuss the implications and offer guidelines to mitigate effects of demand characteristics.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103379"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the integration of location-based services into social media platforms has seen a significant surge, coinciding with the growing challenges posed by the proliferation of fake news online. However, the influence of location data on readers’ perceptions of online news credibility, particularly in relation to the reporters’ whereabouts, remains unclear. To investigate this relationship, we conducted a 3 (Topics: crime, science, health) 2 (Location anchor: event-anchored or participant-anchored) 4 (Proximity to location anchor - no, same, close-by or faraway location) mixed-method online study (N 288) on Prolific. Our data collection involved presenting participants with news articles and assessing their credibility assessments and sharing intentions based on the proximity of those disseminating the news to both the subject matter of the news and the audience consuming it. Our findings reveal that the proximity of the reporter’s location to the readers’ location had a noticeable adverse impact on perceptions of news credibility and the likelihood of sharing it. Furthermore, we also identified a weak positive correlation between sharing intentions and trust in social media platforms. In addition, we observed that crime news were generally perceived as less credible compared to health and science news. Our research contributes significantly to a nuanced understanding of how location-based cues impact user behaviour when interacting with online news articles. Furthermore, it provides design insights for social media platforms aiming to enhance user trust and promote pro-social behaviours.
{"title":"Exploring the effects of location information on perceptions of news credibility and sharing intention","authors":"Ying Ma , Zhanna Sarsenbayeva , Jarrod Knibbe , Jorge Goncalves","doi":"10.1016/j.ijhcs.2024.103378","DOIUrl":"10.1016/j.ijhcs.2024.103378","url":null,"abstract":"<div><div>In recent years, the integration of location-based services into social media platforms has seen a significant surge, coinciding with the growing challenges posed by the proliferation of fake news online. However, the influence of location data on readers’ perceptions of online news credibility, particularly in relation to the reporters’ whereabouts, remains unclear. To investigate this relationship, we conducted a 3 (Topics: crime, science, health) <span><math><mo>×</mo></math></span> 2 (Location anchor: event-anchored or participant-anchored) <span><math><mo>×</mo></math></span> 4 (Proximity to location anchor - no, same, close-by or faraway location) mixed-method online study (N <span><math><mo>=</mo></math></span> 288) on Prolific. Our data collection involved presenting participants with news articles and assessing their credibility assessments and sharing intentions based on the proximity of those disseminating the news to both the subject matter of the news and the audience consuming it. Our findings reveal that the proximity of the reporter’s location to the readers’ location had a noticeable adverse impact on perceptions of news credibility and the likelihood of sharing it. Furthermore, we also identified a weak positive correlation between sharing intentions and trust in social media platforms. In addition, we observed that crime news were generally perceived as less credible compared to health and science news. Our research contributes significantly to a nuanced understanding of how location-based cues impact user behaviour when interacting with online news articles. Furthermore, it provides design insights for social media platforms aiming to enhance user trust and promote pro-social behaviours.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103378"},"PeriodicalIF":5.3,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.ijhcs.2024.103380
Timothée Schmude , Laura Koesten , Torsten Möller , Sebastian Tschiatschek
Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the “XAI Novice Question Bank”, an extension of the XAI Question Bank (Liao et al., 2020) containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences.
{"title":"Information that matters: Exploring information needs of people affected by algorithmic decisions","authors":"Timothée Schmude , Laura Koesten , Torsten Möller , Sebastian Tschiatschek","doi":"10.1016/j.ijhcs.2024.103380","DOIUrl":"10.1016/j.ijhcs.2024.103380","url":null,"abstract":"<div><div>Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the “XAI Novice Question Bank”, an extension of the XAI Question Bank (Liao et al., 2020) containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103380"},"PeriodicalIF":5.3,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1016/j.ijhcs.2024.103376
Yueqing Xuan , Edward Small , Kacper Sokol , Danula Hettiachchi , Mark Sanderson
Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, more importantly, what information it lacks. To answer this question we conducted an online study with 200 participants, which allowed us to assess explainees’ ability to realise explicated information – i.e., factual insights conveyed by an explanation – and unspecified information – i.e, insights that are not communicated by an explanation – across four representative explanation types: model architecture, decision surface visualisation, counterfactual explainability and feature importance. Our findings uncover that highly comprehensible explanations, e.g., feature importance and decision surface visualisation, are exceptionally susceptible to misinterpretation since users tend to infer spurious information that is outside of the scope of these explanations. Additionally, while the users gauge their confidence accurately with respect to the information explicated by these explanations, they tend to be overconfident when misinterpreting the explanations. Our work demonstrates that human comprehension can be a double-edged sword since highly accessible explanations may convince users of their truthfulness while possibly leading to various misinterpretations at the same time. Machine learning explanations should therefore carefully navigate the complex relation between their full scope and limitations to maximise understanding and curb misinterpretation.
{"title":"Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanations","authors":"Yueqing Xuan , Edward Small , Kacper Sokol , Danula Hettiachchi , Mark Sanderson","doi":"10.1016/j.ijhcs.2024.103376","DOIUrl":"10.1016/j.ijhcs.2024.103376","url":null,"abstract":"<div><div>Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, more importantly, what information it lacks. To answer this question we conducted an online study with 200 participants, which allowed us to assess explainees’ ability to realise <em>explicated information</em> – i.e., factual insights conveyed by an explanation – and <em>unspecified information</em> – i.e, insights that are not communicated by an explanation – across four representative explanation types: model architecture, decision surface visualisation, counterfactual explainability and feature importance. Our findings uncover that highly comprehensible explanations, e.g., feature importance and decision surface visualisation, are exceptionally susceptible to misinterpretation since users tend to infer spurious information that is outside of the scope of these explanations. Additionally, while the users gauge their confidence accurately with respect to the information explicated by these explanations, they tend to be overconfident when misinterpreting the explanations. Our work demonstrates that human comprehension can be a double-edged sword since highly accessible explanations may convince users of their truthfulness while possibly leading to various misinterpretations at the same time. Machine learning explanations should therefore carefully navigate the complex relation between their full scope and limitations to maximise understanding and curb misinterpretation.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103376"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1016/j.ijhcs.2024.103377
Yunxian Pan , Jie Xu
With rapid technological development, humans are more likely to cooperatively work with intelligence systems in everyday life and work. Similar to interpersonal teamwork, the effectiveness of human-machine teams is affected by conflicts. Some human-machine conflict scenarios occur when neither the human nor the system was at fault, for example, when the human and the system formulated different but equally effective plans to achieve the same goal. In this study, we conducted two experiments to explore the effects of human-machine plan conflict and the different conflict resolution approaches (human adapting to the system, system adapting to the human, and transparency design) in a computer-aided visual search task. The results of the first experiment showed that when conflicts occurred, the participants reported higher mental load during the task, performed worse, and provided lower subjective evaluations towards the aid. The second experiment showed that all three conflict resolution approaches were effective in maintaining task performance, however, only the transparency design and the human adapting to the system approaches were effective in reducing mental load and improving subjective evaluations. The results highlighted the need to design appropriate human-machine conflict resolution strategies to optimize system performance and user experience.
{"title":"Human-machine plan conflict and conflict resolution in a visual search task","authors":"Yunxian Pan , Jie Xu","doi":"10.1016/j.ijhcs.2024.103377","DOIUrl":"10.1016/j.ijhcs.2024.103377","url":null,"abstract":"<div><div>With rapid technological development, humans are more likely to cooperatively work with intelligence systems in everyday life and work. Similar to interpersonal teamwork, the effectiveness of human-machine teams is affected by conflicts. Some human-machine conflict scenarios occur when neither the human nor the system was at fault, for example, when the human and the system formulated different but equally effective plans to achieve the same goal. In this study, we conducted two experiments to explore the effects of human-machine plan conflict and the different conflict resolution approaches (human adapting to the system, system adapting to the human, and transparency design) in a computer-aided visual search task. The results of the first experiment showed that when conflicts occurred, the participants reported higher mental load during the task, performed worse, and provided lower subjective evaluations towards the aid. The second experiment showed that all three conflict resolution approaches were effective in maintaining task performance, however, only the transparency design and the human adapting to the system approaches were effective in reducing mental load and improving subjective evaluations. The results highlighted the need to design appropriate human-machine conflict resolution strategies to optimize system performance and user experience.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103377"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1016/j.ijhcs.2024.103375
Amon Rapp , Chiara Di Lodovico , Federico Torrielli , Luigi Di Caro
Generative Artificial Intelligence (Gen-AI) has rapidly advanced in recent years, potentially producing enormous impacts on industries, societies, and individuals in the near future. In particular, Gen-AI text-to-image models allow people to easily create high-quality images possibly revolutionizing human creative practices. Despite their increasing use, however, the broader population's perceptions and understandings of Gen-AI-generated images remain understudied in the Human-Computer Interaction (HCI) community. This study investigates how individuals, including those unfamiliar with Gen-AI, perceive Gen-AI text-to-image (Stable Diffusion) outputs. Study findings reveal that participants appraise Gen-AI images based on their technical quality and fidelity in representing a subject, often experiencing them as either prototypical or strange: these experiences may raise awareness of societal biases and evoke unsettling feelings that extend to the Gen-AI itself. The study also uncovers several “relational” strategies that participants employ to cope with concerns related to Gen-AI, contributing to the understanding of reactions to uncanny technology and the (de)humanization of intelligent agents. Moreover, the study offers design suggestions on how to use the anthropomorphizing of the text-to-image model as design material, and the Gen-AI images as support for critical design sessions.
{"title":"How do people experience the images created by generative artificial intelligence? An exploration of people's perceptions, appraisals, and emotions related to a Gen-AI text-to-image model and its creations","authors":"Amon Rapp , Chiara Di Lodovico , Federico Torrielli , Luigi Di Caro","doi":"10.1016/j.ijhcs.2024.103375","DOIUrl":"10.1016/j.ijhcs.2024.103375","url":null,"abstract":"<div><p>Generative Artificial Intelligence (Gen-AI) has rapidly advanced in recent years, potentially producing enormous impacts on industries, societies, and individuals in the near future. In particular, Gen-AI text-to-image models allow people to easily create high-quality images possibly revolutionizing human creative practices. Despite their increasing use, however, the broader population's perceptions and understandings of Gen-AI-generated images remain understudied in the Human-Computer Interaction (HCI) community. This study investigates how individuals, including those unfamiliar with Gen-AI, perceive Gen-AI text-to-image (Stable Diffusion) outputs. Study findings reveal that participants appraise Gen-AI images based on their technical quality and fidelity in representing a subject, often experiencing them as either prototypical or strange: these experiences may raise awareness of societal biases and evoke unsettling feelings that extend to the Gen-AI itself. The study also uncovers several “relational” strategies that participants employ to cope with concerns related to Gen-AI, contributing to the understanding of reactions to uncanny technology and the (de)humanization of intelligent agents. Moreover, the study offers design suggestions on how to use the anthropomorphizing of the text-to-image model as design material, and the Gen-AI images as support for critical design sessions.</p></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103375"},"PeriodicalIF":5.3,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1071581924001587/pdfft?md5=359a59059de59a754e630f5dbeeba9c9&pid=1-s2.0-S1071581924001587-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study, introduces and evaluates different countermeasures using real-time eye-tracking data. The countermeasures detect when driver gaze deviates from the road for longer than a predetermined threshold and then redirect the driver's attention back to the road. The countermeasures include bimodal and trimodal alerts using combinations of auditory, tactile, and visual modalities. These countermeasures showcase the utility of adopting eye-tracking technologies in the context of driver monitoring and advanced driver's assistance systems. They enhance safety as a safeguard for the increased use of devices such as in-vehicle infotainment systems. Results show that countermeasures effectively redirect drivers’ attention to the road, with higher on-road gaze time. Additionally, bimodal alerts that include the visual modality are less effective at redirecting participants’ gaze on-road and result in poorer driving performance.
{"title":"A gaze-based driver distraction countermeasure: Comparing effects of multimodal alerts on driver's behavior and visual attention","authors":"Jérémy Lachance-Tremblay , Zoubeir Tkiouat , Pierre-Majorique Léger , Ann-Frances Cameron , Ryad Titah , Constantinos K. Coursaris , Sylvain Sénécal","doi":"10.1016/j.ijhcs.2024.103366","DOIUrl":"10.1016/j.ijhcs.2024.103366","url":null,"abstract":"<div><p>This study, introduces and evaluates different countermeasures using real-time eye-tracking data. The countermeasures detect when driver gaze deviates from the road for longer than a predetermined threshold and then redirect the driver's attention back to the road. The countermeasures include bimodal and trimodal alerts using combinations of auditory, tactile, and visual modalities. These countermeasures showcase the utility of adopting eye-tracking technologies in the context of driver monitoring and advanced driver's assistance systems. They enhance safety as a safeguard for the increased use of devices such as in-vehicle infotainment systems. Results show that countermeasures effectively redirect drivers’ attention to the road, with higher on-road gaze time. Additionally, bimodal alerts that include the visual modality are less effective at redirecting participants’ gaze on-road and result in poorer driving performance.</p></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"193 ","pages":"Article 103366"},"PeriodicalIF":5.3,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1071581924001496/pdfft?md5=1ec1ba108f246c0b63054749117a8534&pid=1-s2.0-S1071581924001496-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1016/j.ijhcs.2024.103359
Niels van Berkel , Benjamin Tag , Rune Møberg Jacobsen , Daniel Russo , Helen C. Purchase , Daniel Buschek
This paper presents an analysis of different interaction techniques used in interactive data visualisations to support end-users in visual analytics tasks. Our selection of interaction techniques is based on prior work and consists of the interaction techniques Select, Explore, Reconfigure, Encode, Filter, Abstract/Elaborate, and Connect. Through a within-subject study, we assessed participants’ abilities to utilise these techniques when faced with three distinct types of data-driven tasks; lookup, comparison, and Relation-seeking. Our research investigates the impact of these interaction techniques on the correctness, confidence, perceived difficulty, and cognitive load of N = 80 self-identified data scientists and N = 80 non-experts. We find that interaction technique significantly impacts answer correctness and participant confidence. Participants performed best across those interaction techniques that allow for information that is deemed least relevant to be concealed, which is reflected in lower intrinsic and extraneous cognitive load. Interestingly, participants’ expertise affected their confidence but not their accuracy. Our results provide insights useful for a more targeted and informed design and usage of interactive data visualisations.
本文分析了交互式数据可视化中使用的不同交互技术，以支持终端用户完成可视化分析任务。我们对交互技术的选择是基于先前的工作，包括选择、探索、重新配置、编码、过滤、抽象/协作和连接等交互技术。通过主体内研究，我们评估了参与者在面对三种不同类型的数据驱动任务（查找、比较和关系搜索）时使用这些技术的能力。我们的研究调查了这些交互技术对 N = 80 名自我认同的数据科学家和 N = 80 名非专家的正确性、自信心、感知难度和认知负荷的影响。我们发现，交互技术对答案的正确性和参与者的信心有很大影响。在那些允许隐藏被认为最不相关的信息的交互技术中，参与者的表现最好，这反映在较低的内在和外在认知负荷上。有趣的是，参与者的专业知识会影响他们的信心，但不会影响他们的准确性。我们的研究结果为更有针对性地设计和使用交互式数据可视化提供了有益的启示。
{"title":"Impact of interaction technique in interactive data visualisations: A study on lookup, comparison, and relation-seeking tasks","authors":"Niels van Berkel , Benjamin Tag , Rune Møberg Jacobsen , Daniel Russo , Helen C. Purchase , Daniel Buschek","doi":"10.1016/j.ijhcs.2024.103359","DOIUrl":"10.1016/j.ijhcs.2024.103359","url":null,"abstract":"<div><p>This paper presents an analysis of different interaction techniques used in interactive data visualisations to support end-users in visual analytics tasks. Our selection of interaction techniques is based on prior work and consists of the interaction techniques <span>Select</span>, <span>Explore</span>, <span>Reconfigure</span>, <span>Encode</span>, <span>Filter</span>, <span>Abstract/Elaborate</span>, and <span>Connect</span>. Through a within-subject study, we assessed participants’ abilities to utilise these techniques when faced with three distinct types of data-driven tasks; lookup, comparison, and Relation-seeking. Our research investigates the impact of these interaction techniques on the correctness, confidence, perceived difficulty, and cognitive load of N = 80 self-identified data scientists and N = 80 non-experts. We find that interaction technique significantly impacts answer correctness and participant confidence. Participants performed best across those interaction techniques that allow for information that is deemed least relevant to be concealed, which is reflected in lower intrinsic and extraneous cognitive load. Interestingly, participants’ expertise affected their confidence but not their accuracy. Our results provide insights useful for a more targeted and informed design and usage of interactive data visualisations.</p></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"192 ","pages":"Article 103359"},"PeriodicalIF":5.3,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1071581924001423/pdfft?md5=02cc8d46762448607a2127d822911854&pid=1-s2.0-S1071581924001423-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1016/j.ijhcs.2024.103361
Doireann Peelo Dennehy , Stephanie Murphy , Sarah Foley , John McCarthy , Kellie Morrissey
Social media has transformed how users create, share, and consume health and fitness content. Research to date demonstrates that despite positive sharing opportunities, women are subject to misinformation, gendered harassment, and economic surveillance. To clarify the benefits and challenges facing women who interact with fitness content on social media, we conducted a qualitative systematic synthesis of 21 research papers. Thematic synthesis of the included papers describes how social media is used as a site to share information and experiences, how women engage with fitness content and how platforms are used in this engagement. We constructed four themes describing women's actions in engaging with fitness content online: producing, observing, interacting, and managing. In one of the main contributions of this paper, these themes are worked into a modes of engagement framework, for categorising and understanding the ways women use social media for fitness. This framework may be useful in further analysis of women's use of social media.
{"title":"Keeping Fit & Staying Safe: A Systematic Review of Women's Use of Social Media for Fitness","authors":"Doireann Peelo Dennehy , Stephanie Murphy , Sarah Foley , John McCarthy , Kellie Morrissey","doi":"10.1016/j.ijhcs.2024.103361","DOIUrl":"10.1016/j.ijhcs.2024.103361","url":null,"abstract":"<div><p>Social media has transformed how users create, share, and consume health and fitness content. Research to date demonstrates that despite positive sharing opportunities, women are subject to misinformation, gendered harassment, and economic surveillance. To clarify the benefits and challenges facing women who interact with fitness content on social media, we conducted a qualitative systematic synthesis of 21 research papers. Thematic synthesis of the included papers describes how social media is used as a site to share information and experiences, how women engage with fitness content and how platforms are used in this engagement. We constructed four themes describing women's actions in engaging with fitness content online: producing, observing, interacting, and managing. In one of the main contributions of this paper, these themes are worked into a modes of engagement framework, for categorising and understanding the ways women use social media for fitness. This framework may be useful in further analysis of women's use of social media.</p></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"192 ","pages":"Article 103361"},"PeriodicalIF":5.3,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1071581924001447/pdfft?md5=625ee3e890becbb0f69316f39a17d3be&pid=1-s2.0-S1071581924001447-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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