Game Interaction Research Articles

Bibliometric Analysis of the Research Landscape in Human-Computer Interaction in Ibero-America

Human-Computer Interaction (HCI) is a research discipline that optimizes human-machine interfaces. Ibero-America has become a leading region in HCI advances adapted to its unique socioeconomic environment. However, a global characterization of the research landscape for this discipline in the region still needs to be improved. Therefore, this study aimed to analyze the scientific literature on HCI in Ibero-America using bibliometric techniques, identifying key authors, institutions, conceptual structure, and research trends. The methodology employed consisted of a bibliometric analysis of 9812 documents from 1990-2023 from Scopus and Web of Science. The data were analyzed using Bibliometrix and VOSviewer for statistical and network analysis. The results revealed that Spain, Brazil, Portugal, Mexico, and Colombia are at the forefront of HCI research in the region, with more than 90 % of total publications experiencing an annual growth rate of approximately 14.6 %, driven by strong international collaborations. Key topics are human-computer interaction, virtual reality, augmented reality, and video games, while education is one of the main application areas. The main conclusion is that the HCI research landscape in Ibero-America is characterized by rapid evolution, international integration, and conceptual diversity, indicative of a mature and interdisciplinary field that responds to regional demands. The advancement of the field could be driven by increased private-sector funding. Future system designs in the region should prioritize cultural sensitivity and inclusion.

Player Experience of Minimalist Video Game Design: Case Study of Indie Horror Iron Lung

Player experiences of minimalist video games are not well documented. This article addresses this gap by exploring players’ experiences of the minimalist horror video game Iron Lung. This game is identified as employing unique methods of gameplay toward affecting players. A case study design was framed using Caroux et al.’s player–video game interactions and Nealen et al.’s minimalist video game design characteristics and observed through affect theory to consider how bodies (video games) affect other bodies (players). A selection of player reviews was thematically analyzed, and three video streams were observed and analyzed. Results demonstrate that most players were affected as the developer intended using obfuscation, deliberately clumsy mechanics, and unique gameplay pacing. This article contributes to the field of minimalist design by filling the gap of player experiences of minimalist game design. This is useful to video game designers, minimalist designers, interactive experience designers, scenographers, or environmental design specialists.

Replicator-mutator dynamics of the rock-paper-scissors game: Learning through mistakes.

We generalize the Bush-Mosteller learning, the Roth-Erev learning, and the social learning to include mistakes, such that the nonlinear replicator-mutator equationwith either additive or multiplicative mutation is generated in an asymptotic limit. Subsequently, we exhaustively investigate the ubiquitous rock-paper-scissors game for some analytically tractable motifs of mutation pattern for which the replicator-mutator flow is seen to exhibit rich dynamics that include limit cycles and chaotic orbits. The main result of this paper is that in both symmetric and asymmetric game interactions, mistakes can sometimes help the players learn; in fact, mistakes can even control chaos to lead to rational Nash-equilibrium outcomes. Furthermore, we report a hitherto-unknown Hamiltonian structure of the replicator-mutator equation.

Historical Analysis of Interactive Gamification Research: A Literature Review with Kuhn and Lakatos’ Approaches

This literature review provides an analysis of the historical development and theoretical frameworks put forth by notable philosophers such as Thomas Kuhn and Imre Lakatos in the field of interactive gamification research. By examining the contributions and perspectives of these influential figures, the authors aimed to enhance our understanding of the evolution of gamification as a research area. This review serves as a valuable resource for individuals interested in delving deeper into the concept of gamification in interactive research. Furthermore, it offers a collection of references that can be explored in the initial stages of gamification research. By studying the works of influential philosophers such as Thomas Kuhn and Imre Lakatos, researchers can gain valuable insights into the evolution of gamification research and its theoretical underpinnings.. Kuhn's concept of paradigm shifts and Lakatos's ideas on research programs provide researchers with a framework to understand the progression of ideas and theories within the field.

Plastic borders: on the photographic frame and its virtual experience

During the last decade, a number of media theorists have defended the idea that photography, through its new computational and networked existence, is progressively losing its representational identity. While it is evident that the computational materiality of networked photographs has turned most distributed images into data generating assets, the way most photographs continue to operate in our society suggests that the specificity of the medium remains practically unaltered since modernist times. In an age where immersive virtual worlds will soon dominate our online interactions, this paper discusses the current forms and uses of photography within the emerging virtual spaces. Through practice-led, experimental research on the use of virtual cameras to record immersive, lived experiences, and the analysis of recent works produced by the so-called virtual photographers documenting their gaming interactions, this study investigates the value of the photographic frame as a still, two-dimensional representation, while questioning its function within extended reality environments.

Assessing the Importance of Content Versus Design for Successful Crowdfunding of Health Education Games: Online Survey Study.

Health education games make health-related tasks enjoyable and interactive, thereby encouraging user participation. Entrepreneurs and health educators can leverage online crowdfunding platforms, such as Kickstarter, to transform their innovative ideas into funded projects. This research focuses on health education game initiatives on Kickstarter. Through an online user survey, it aims to understand user perceptions and evaluate the significance of 8 distinct components that may influence the success of such crowdfunding initiatives. A total of 75 participants evaluated games using 8 dimensions: game rules, learning objectives, narrative, content organization, motivation, interactivity, skill building, and assessment and feedback. The survey data were analyzed using descriptive statistical analysis, exploratory factor analysis, the Wilcoxon-Mann-Whitney test, and multivariate analysis. Exploratory data analysis showed that, among the 8 dimensions, skill building, content organization, and interactivity were the top-ranking dimensions most closely associated with crowdfunding health education game. The 8 dimensions can be grouped into 3 categories from the exploratory factor analysis: game content-, instruction-, and game design-related components. Further statistical analysis confirmed the correlation between these dimensions with the successful crowdfunding of health education games. This empirical analysis identified critical factors for game proposal design that can increase the likelihood of securing crowdfunding support.

Gameful interaction: How principles of game design can be applied to enhance user experiences in non-game applications

In the era of digital transformation, the integration of game design and interaction design has become an important trend, and its scope of application has gone beyond purely entertaining applications. This thesis investigates the principles of gamified interaction with a focus on how game design elements can be utilized to enhance the user experience in non-game applications. The research objective of this thesis is to investigate the effectiveness and limitations of gamification design in a variety of contexts when incorporating it into educational, business, health, and other types of applications. The study employs a mixed research methodology, which includes analysis and comparison of the literature, as well as a specific case study of the Chinese fitness app "Keep" to demonstrate real-world applications. The data for this study came from existing academic papers, app analysis, user feedback and design evaluation. This thesis finds that gamified interactions have a significant impact on user engagement and motivation, providing valuable insights for designers to create more engaging digital environments.

Developing a Framework for Interactions in CBT-Based Serious Games on Smartphones

While there are numerous serious games that explore cognitive behavioral therapy (CBT) techniques through gamification on smartphones, the framework for developing interactions is not often thoroughly discussed. The objective of this study is to outline the process of combining CBT techniques, narrative setup, and game mechanics to create two types of interactions (verbal- and physical-based) using the player interaction framework (PIF). The PIF consists of three key sections: setup, aim, and execution. In the setup section, it utilizes the ABCDE (activating events, beliefs, consequences, disputation of beliefs, and effective new approaches) model to combine narrative and CBT techniques such as gratitude and self-monitoring. The aim section is used to break down the intended experience of both the player and their main character, at the same time clarifying the CBT goal of the interaction. Finally, the execution section includes the representation of how players will interact in the game through input and feedback. The efficacy of the framework in visual narrative serious games remains to be investigated through a randomized controlled trial after the completion of our serious game, BlueLine.

Application of machine learning algorithms to character interaction in single-player role-playing games

Character interaction in single-player role-playing games refers to the role interactions between the player character and the non-player characters (NPCs) in the game including dialogue, social interaction, cooperation, and competition, which are designed and implemented to achieve certain game goals and effects. This paper summarizes and compares machine learning algorithms applied to game development using a paper review and theoretical analysis, aiming to provide new solutions for optimizing and improving algorithms commonly used in game design. The ADEM algorithm can automate the evaluation of semantic relevance and logical coherence of character dialogues, but it is more costly in the pre-training phase and more difficult to perform domain adaptation for specific game content. The BERT algorithm can improve the speed of character dialogue generation through the training of unlabeled text, and then improve the flexibility of the dialogue through a small amount of labeled text for domain adaptation and fine-tuning, but it lacks in the generation of smooth and complex text and other aspects. The GAN algorithm can learn from a single or a small number of action sequences to achieve the transformation and mixing of character actions, thus generating multiple action gestures. However, its training process may suffer from problems such as pattern collapse leading to degradation in the quality of the generation, and it is also difficult to set a uniform objective criterion to determine the loss function.

Use of human–computer interactive games for the dynamic assessment of language skills of children with autism spectrum disorder

AbstractThis study explored the influence of the dynamic assessment of human–computer interactive games on children's language potential. Thirty‐seven special children aged 3–7 years were selected to participate in the study. They were divided into three groups according to their scores on the Autism Behaviour Checklist: (1) a non‐autism group, (2) a suspected autism group and (3) an autism group. In two stages of static and dynamic assessment, all three groups of children completed language tests under the conditions of no mediated prompt and mediated prompts, and the data generated in the game interactions were processed and analysed. The findings suggest the following: (1) static assessment may ignore children's language potential, while dynamic assessment can measure and differentiate children's potential language development ability; (2) children with ASD need more mediation prompts and are more likely to complete tasks with explicit mediation prompts; (3) children with ASD have weaker language potential, and children with suspected ASD or non‐ASD children have homogeneous language potential; and (4) assessors should pay timely attention to and maintain children's learning status and psychological changes. This study demonstrates the importance of human–computer interactive games as a dynamic assessment tool to tap into children's language potential and to determine their proximal developmental zone, which has important implications for teachers in understanding groups with different language abilities. Practitioner notesWhat is already known about the topic? The traditional language assessment paradigm (static assessment) can only evaluate the learners' current language development ability, while the short‐term development potential may not be observed. Different prompts in the mediation strategy can be used as a quantitative assessment paradigm in dynamic assessment. Much of the dynamic assessment in children with ASD focuses on language vocabulary learning outcomes and lacks measurement or quantification of language potential. What this paper adds? Presents a method for the dynamic assessment of a human–computer interactive language game suitable for children aged 3–7 years with ASD. Quantifies the potential level of language skills in children with varying degrees of ASD. Objectively and dynamically compares differences in language development potential between different groups. In dynamic assessment, children's learning status and psychological changes should be paid attention to and maintained in time. Implications for practice, research or policy This paper provides a concrete example of how human–computer interactive games can be used to dynamically assess the language potential of children with ASD. These findings highlight the importance of progressive scaffolds (mediated prompts) in children's language learning, which can help children identify and solve problems in a timely manner. For educational developers, language intervention materials should be prepared in a way that encompasses the different levels of language skills of children with different disabilities, that is, within the individual's zone of proximal development.

Testing a Behavioral Activation Gaming App for Depression During Pregnancy: Multimethod Pilot Study.

Depression during pregnancy is increasingly recognized as a worldwide public health problem. If untreated, there can be detrimental outcomes for the mother and child. Anxiety is also often comorbid with depression. Although effective treatments exist, most women do not receive treatment. Technology is a mechanism to increase access to and engagement in mental health services. The Guardians is a mobile app, grounded in behavioral activation principles, which seeks to leverage mobile game mechanics and in-game rewards to encourage user engagement. This study seeks to assess app satisfaction and engagement and to explore changes in clinical symptoms of depression and anxiety among a sample of pregnant women with elevated depressive symptoms. This multimethod pilot test consisted of a single-arm, proof-of-concept trial to examine the feasibility and acceptability of The Guardians among a pregnant sample with depression (N=18). Participation included two web-based study visits: (1) a baseline assessment to collect demographic and obstetric information and to assess clinical symptoms and (2) an exit interview to administer follow-up measures and explore user experience. Participants completed biweekly questionnaires (ie, Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7) during the trial to assess depression and anxiety symptom severity. App satisfaction was measured using 2 self-report scales (ie, Mobile Application Rating Scale and Player Experience of Needs Satisfaction scale). Engagement with The Guardians was captured using game interaction metric data. We used backward-eliminated mixed effects longitudinal models to examine the effects of app engagement and satisfaction and length of time in the study on symptoms of depression and anxiety. Content analysis was conducted on qualitative data from exit interviews. The 15-day and 30-day overall app retention rates were 26.6% and 15.1%, respectively. Mixed effects models found significant negative main effects of week in study (β=-.35; t61=-3.05; P=.003), number of activities completed (β=-.12; t61=-2.05; P=.04), days played (β=-.12; t58=-2.9; P=.005), and satisfaction, according to the Mobile Application Rating Scale (β=-3.05; t45=-2.19; P=.03) on depressive symptoms. We have reported about similar analyses for anxiety. There is preliminary evidence suggesting harder activities are associated with greater mood improvement than easier activities. Qualitative content analysis resulted in feedback falling under the following themes: activities, app design, engagement, fit of the app with lifestyle, perceived impact of the app on mood, and suggestions for app modifications. Preliminary results from this multimethod study of The Guardians indicate feasibility and acceptability among pregnant women with depression. Retention and engagement levels were more than double those of previous public mental health apps, and use of the app was associated with significant decrease in depressive symptom scores over the 10-week trial. The Guardians shows promise as an effective and scalable digital intervention to support women experiencing depression.

Effects of individual and collective decision rule on cooperation in public goods game

Research on public goods game has traditionally focused on studying the effects of punishment and reward in experimental settings, commonly known as standard games. However, these standard games often examine isolated interactions among individuals, leading to varying results based on factors such as the population studied, number of participants, and geographical context. Consequently, the understanding of cooperation evolution remains imprecise.To address this limitation, we are trying to generalize these experimental studies, we employ an agent-based model in this study to simulate interactions in public goods game and explore the influence of punishment and reward on the evolution of cooperation. Unlike standard games, evolutionary games offer a more comprehensive framework to capture and analyze a broader range of strategic interactions and behaviors.In our approach, we introduce two methods for applying punishment or reward: Individual Punishment or Reward and Collective Punishment or Reward. Under the first method, actors can individually reward or punish each other based on their contributions or free-riding behavior towards the collective good. In the second method, decisions for punishment or reward are made collectively after achieving a consensus or agreement among the members of the group. We provide a model for comparing Individual Decision Rule and Collective Decision Rule under punishment and reward, to study their effects on the evolution of contributions.Consistent with recent experimental findings, our results indicate that these mechanisms lead to a significant increase in contributions compared to the outcomes observed in standard public goods game. Additionally, we demonstrate the significant role of rewards in promoting contribution within public goods game.

Evolutionary game of inland waterways LNG construction under government subsidy and carbon tax policy under fuzzy environment

Abstract Since the pollution from inland waterways account for a great portion of urban pollutions, to use clean energy in inland waters has become an efficient means in combatting inland waterway pollution. With the increasing global demand for clean energy, liquefied natural gas (LNG) is increasingly favored by various countries, and its application in inland waterways transportation is becoming increasingly widespread. This paper focuses on the evolutionary game interaction mechanism between the government, energy company and shipping company as the main participants in inland waterways LNG construction, considering the costs as fuzzy variables. The impact of participating in LNG construction implementation on the evolutionary stable strategy (ESS) was discussed. The research result indicates the following: (1) If the government, energy company and shipping company all participate in inland waterways LNG construction, the conditions are that the profits of the government, energy company and shipping company that choose to participate in LNG construction exceed the costs of not participating. (2) The convergence rates of ESS of the government, energy company and shipping company are positively correlated with the initial values of each other’s choice strategies, negatively correlated with carbon tax, subsidy and the cost of implementing positive strategy. (3) The cooperation between the energy company and shipping company has no impact on the selection of government evolutionary game strategy. The premise for the energy company and shipping company to participate in LNG construction together is that both parties have excess returns. Therefore, to ensure the implement of LNG application in inland waterways, the government should carry out carbon tax and at the same time perform subsidy policy, with the common participation of the energy company and shipping company.

Forecasting Future Behavior: Agents in Board Game Strategy

This paper presents findings on machine learning agent behavior prediction in a board game application developed by a group of students. The goal of this research is to create a model facilitating collaboration between a user and an AI to play together in the board game using a Human-in-the-Loop architecture. By injecting explainability, the aim is to enhance communication and understanding between the user and the AI agent. Featuring a competitive Artificial Intelligence (AI) based on the Proximal Policy Optimization model, this research explores methods to make AI decisions transparent for enhanced player understanding. Two predictive models, a Decision Tree (DT) and a Deep Learning (DL) classifier, were developed and compared. The results show that the DT model is effective for short-term predictions but limited in broader applications, while the DL classifier shows potential for long-term prediction without requiring direct access to the game's AI. This study contributes to understanding human-AI interaction in gaming and offers insights into AI decision-making processes.

GT-CAM: Game Theory based Class Activation Map for GCN.

Graph Convolutional Networks (GCN) have shown outstanding performance in skeleton-based behavior recognition. However, their opacity hampers further development. Researches on the explainability of deep learning have provided solutions to this issue, with Class Activation Map (CAM) algorithms being a class of explainable methods. However, existing CAM algorithms applies to GCN often independently compute the contribution of individual nodes, overlooking the interactions between nodes in the skeleton. Therefore, we propose a game theory based class activation map for GCN (GT-CAM). Firstly, GT-CAM integrates Shapley values with gradient weights to calculate node importance, producing an activation map that highlights the critical role of nodes in decision-making. It also reveals the cooperative dynamics between nodes or local subgraphs for a more comprehensive explanation. Secondly, to reduce the computational burden of Shapley values, we propose a method for calculating Shapley values of node coalitions. Lastly, to evaluate the rationality of coalition partitioning, we propose a rationality evaluation method based on bipartite game interaction and cooperative game theory. Additionally, we introduce an efficient calculation method for the coalition rationality coefficient based on the Monte Carlo method. Experimental results demonstrate that GT-CAM outperforms other competitive interpretation methods in visualization and quantitative analysis.

An examination of mothers’ self-efficacy and their interaction with their children aged 60-72 months

The present study has the aim of examining the parental self-efficacy of mothers in terms different variables, establishing the correlation between parenting and the general self-efficacy levels of mothers and to identify the characteristics of their interactions with their children in the context of their self-efficacy. Explanatory sequential design, a mixed method, was used in this study. For the quantitative part, 251 mothers with children who are 60-72 months was involved in. The sample for the qualitative part was formed of volunteers out of the said sample and consists of 20 mothers with 20 children in total; in this group of mothers, the self-efficacy levels were determined as low (n=10) or high (n=10). The data were collected through a set of forms, namely the “Demographic Information Form”, “General Self-Efficacy Scale”, “Berkeley Parenting Self-Efficacy Scale Revised”, and “Mother-Child Interaction Games Video Records”. Statistical analyzes revealed that the total scores of mothers' self-efficacy did not differ according to their educational status and social support from their spouses, but showed a significant difference according to the gender of their children, in favor of mothers with girls. Additionally, it was found that mothers' general self-efficacy levels and parenting self-efficacy were positively related. Mothers having low parenting self-efficacy levels exhibit more behaviors gravitating towards a “focusing on success” first and foremost, followed by “sensitivity” and “play interaction”. Mothers with high parenting self-efficacy, on the other hand, exhibited behaviors towards the theme of "sensitivity" the most, unlike mothers with low self-efficacy in games. However, the frequency of behavior for the themes of "achievement orientation" and "game interaction" was also found to be high.

Are transformer-based models more robust than CNN-based models?

As the deployment of artificial intelligence (AI) models in real-world settings grows, their open-environment robustness becomes increasingly critical. This study aims to dissect the robustness of deep learning models, particularly comparing transformer-based models against CNN-based models. We focus on unraveling the sources of robustness from two key perspectives: structural and process robustness. Our findings suggest that transformer-based models generally outperform convolution-based models in robustness across multiple metrics. However, we contend that these metrics may not wholly represent true model robustness, such as the mean of corruption error. To better understand the underpinnings of this robustness advantage, we analyze models through the lens of Fourier transform and game interaction. From our insights, we propose a calibrated evaluation metric for robustness against real-world data, and a blur-based method to enhance robustness performance. Our approach achieves state-of-the-art results, with mCE scores of 2.1% on CIFAR-10-C, 12.4% on CIFAR-100-C, and 24.9% on TinyImageNet-C.

SOCA-PRNet: Spatially Oriented Attention-Infused Structured-Feature-Enabled PoseResNet for 2D Human Pose Estimation.

In the recent era, 2D human pose estimation (HPE) has become an integral part of advanced computer vision (CV) applications, particularly in understanding human behaviors. Despite challenges such as occlusion, unfavorable lighting, and motion blur, advancements in deep learning have significantly enhanced the performance of 2D HPE by enabling automatic feature learning from data and improving model generalization. Given the crucial role of 2D HPE in accurately identifying and classifying human body joints, optimization is imperative. In response, we introduce the Spatially Oriented Attention-Infused Structured-Feature-enabled PoseResNet (SOCA-PRNet) for enhanced 2D HPE. This model incorporates a novel element, Spatially Oriented Attention (SOCA), designed to enhance accuracy without significantly increasing the parameter count. Leveraging the strength of ResNet34 and integrating Global Context Blocks (GCBs), SOCA-PRNet precisely captures detailed human poses. Empirical evaluations demonstrate that our model outperforms existing state-of-the-art approaches, achieving a Percentage of Correct Keypoints at 0.5 (PCKh@0.5) of 90.877 at a 50% threshold and a Mean Precision (Mean@0.1) score of 41.137. These results underscore the potential of SOCA-PRNet in real-world applications such as robotics, gaming, and human-computer interaction, where precise and efficient 2D HPE is paramount.

Interactive Hand Gesture Control System for Augmented Reality-based Games

This research paper presents the development of an innovative 2D augmented reality (AR) car game utilizing Python, OpenCV, and Pygame. The primary objective of the game is to provide an interactive platform for individuals with motor skill challenges, such as Parkinson’s disease, to enhance their hand and gesture control abilities. The game involves controlling a virtual car superimposed onto the real-world environment using an AR marker. Hand gestures captured by a webcam control the car's movements, creating a novel and accessible gaming experience. The implementation incorporates computer vision techniques from the OpenCV library to detect an AR marker, interpret hand gestures, and map them to specific actions within the game. The Pygame library facilitates the creation of an engaging gaming environment, where users can employ intuitive hand movements to navigate the virtual car. The system's performance is evaluated in varying lighting conditions, and the results demonstrate a promising accuracy rate in gesture detection. The limitations and potential improvements are discussed, emphasizing the system's application in assisting individuals with motor impairments and its broader implications for human-computer interaction. This research provides insights into the intersection of augmented reality, computer vision, and assistive technology, offering a foundation for further advancements in accessible gaming experiences. Keywords: Augmented Reality, Hand Gesture Control, Computer Vision, Game Interaction, Assistive Technology

Research on Industrial Economic Development and Electricity Price Adjustment Strategy Based on Multi-Agent Game

With the deepening of the electricity market reform and the accelerating transformation of the new power system, the number of new electricity customers in the market with local development characteristics has increased. The local industrial and economic development have been affected by the ability to guide electricity prices. In this paper, using the multi-agent game based on exploring the impact of electricity price on the industrial economy, the specific optimization scheme of electricity price adjustment is proposed with the aim of improving the overall efficiency of the power grid and reducing the cost of electricity for industrial users. After the game, the overall efficiency of the electricity supplier will be increased by about 3.06 percent, and the cost of electricity used by various special industries will be reduced by about 6.86 percent to 4.48 percent. The win-win situation of the electricity suppliers’ income and the featured industries’ development can be achieved by the optimization results. As well as the cost of electricity consumption in featured industries can be reduced, and the comprehensive benefits of electricity suppliers can be improved by using the electricity price adjustment scheme after game interaction, which promotes the rational use of energy and meets the current social requirements for the green and sustainable development of the electricity industry.