Pub Date : 2026-01-01Epub Date: 2025-10-06DOI: 10.1016/j.chb.2025.108819
Gen-Yih Liao , Shih-I Tai , Nga Yan Ng , T.C.E. Cheng , Ching-I Teng
Online games are prevalent computer systems. Game makers need to constantly revise gaming elements to motivate players to continue to use the game use and to incorporate features that meet player expectations. We know that players have strong goal-pursuit motivations, but we do not yet know how player expectations interact with game elements to effectively increase players' usage, thereby revealing a knowledge gap. To address this gap, we adopted the goal-gradient theory to construct a research model. We collected complete responses from two data sources: 1724 game players as the first and 59 game experts as the second. The player-perception responses were combined with the expert-rated responses to examine which players' expectations and game elements would have both direct and moderating effects on game usage. The study findings uniquely indicate that expectancy for character growth increases players' game usage by being both an antecedent and a moderator, thereby theoretically deepening the understanding of goal-gradient theory. Our findings provide novel insights that online games with visible avatars and round-based design elements do not increase players' game usage, but round-based game design elements strengthen the influence of players; goal-attaining motivations on game usage.
{"title":"How gaming goal pursuit and expert-rated computer game features interact to affect human game use behavior","authors":"Gen-Yih Liao , Shih-I Tai , Nga Yan Ng , T.C.E. Cheng , Ching-I Teng","doi":"10.1016/j.chb.2025.108819","DOIUrl":"10.1016/j.chb.2025.108819","url":null,"abstract":"<div><div>Online games are prevalent computer systems. Game makers need to constantly revise gaming elements to motivate players to continue to use the game use and to incorporate features that meet player expectations. We know that players have strong goal-pursuit motivations, but we do not yet know how player expectations interact with game elements to effectively increase players' usage, thereby revealing a knowledge gap. To address this gap, we adopted the goal-gradient theory to construct a research model. We collected complete responses from two data sources: 1724 game players as the first and 59 game experts as the second. The player-perception responses were combined with the expert-rated responses to examine which players' expectations and game elements would have both direct and moderating effects on game usage. The study findings uniquely indicate that <em>expectancy for character growth</em> increases players' game usage by being both an antecedent and a moderator, thereby theoretically deepening the understanding of goal-gradient theory. Our findings provide novel insights that online games with visible avatars and round-based design elements do not increase players' game usage, but round-based game design elements strengthen the influence of players; goal-attaining motivations on game usage.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108819"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-10-10DOI: 10.1016/j.chb.2025.108826
Amon Rapp , Arianna Boldi
In contemporary gaming, players increasingly rely on numerical data to guide in-game decisions, interact with others, and enhance performance. Especially with the rise of esports and streaming practices, game “numbers” have become central to player development. This study aims to explore how game metrics affect the gaming experience of different kinds of players, investigating whether and how game data influence their performance and shape their sense of agency. With this aim, we adopted an interpretive qualitative approach and conducted forty semi-structured interviews with casual players, esports players, and streamers, asking participants to recount how they use and interpret game data. We then interpreted the collected material through the lens of the extended mind theory and analyzed it using thematic analysis. Study findings reveal that different types of players vary in how they track, understand, and trust game metrics, and that such metrics may extend their cognitive processes. Moreover, the findings show that a player's level of game knowledge influences how players process game data and adjust their behavior accordingly. These findings also suggest that an overemphasis on the “objectivity” of game metrics may lead players to rely excessively on external numerical validation, potentially diminishing their performance and sense of agency. By contrast, players who develop an in-depth understanding of game mechanics and refine their game sense retain greater control over their in-game decisions and behavior. In sum, this study contributes to the understanding of self-tracking in gaming and its implications for player agency, cognition, and performance.
{"title":"The quantification of the gaming experience: Self-tracking practices and game metrics among casual players, esports players, and streamers","authors":"Amon Rapp , Arianna Boldi","doi":"10.1016/j.chb.2025.108826","DOIUrl":"10.1016/j.chb.2025.108826","url":null,"abstract":"<div><div>In contemporary gaming, players increasingly rely on numerical data to guide in-game decisions, interact with others, and enhance performance. Especially with the rise of esports and streaming practices, game “numbers” have become central to player development. This study aims to explore how game metrics affect the gaming experience of different kinds of players, investigating whether and how game data influence their performance and shape their sense of agency. With this aim, we adopted an interpretive qualitative approach and conducted forty semi-structured interviews with casual players, esports players, and streamers, asking participants to recount how they use and interpret game data. We then interpreted the collected material through the lens of the extended mind theory and analyzed it using thematic analysis. Study findings reveal that different types of players vary in how they track, understand, and trust game metrics, and that such metrics may extend their cognitive processes. Moreover, the findings show that a player's level of game knowledge influences how players process game data and adjust their behavior accordingly. These findings also suggest that an overemphasis on the “objectivity” of game metrics may lead players to rely excessively on external numerical validation, potentially diminishing their performance and sense of agency. By contrast, players who develop an in-depth understanding of game mechanics and refine their <em>game sense</em> retain greater control over their in-game decisions and behavior. In sum, this study contributes to the understanding of self-tracking in gaming and its implications for player agency, cognition, and performance.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108826"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-22DOI: 10.1016/j.chb.2025.108805
Soyoung Park
Wealth has long influenced digital inequality by shaping access to and benefits from technologies, yet its role in digital overuse—characterized by perceived dissatisfaction and negative consequences rather than mere screen time—remains underexplored. This study investigates the relationship between income and digital overuse, using data from the 2019–2022 Korean Smartphone Overuse Survey, with 101,625 respondents. Digital overuse is both defined and assessed in terms of self-control failure, behavioral salience, and negative after-effects, analyzed using quantile regression across income percentiles. This study examines whether the unintended consequences of digital engagement, like overuse, are also stratified along socioeconomic lines—just as the benefits of technology have been. To explore this, we test two hypotheses in the context of COVID-19: the Affluence Dependency Hypothesis, which suggests that affluent individuals are more prone to digital overuse due to greater access, and the Resourceful Autonomy Hypothesis, which posits that higher-income individuals are better able to regulate their usage. Results indicate that while affluent individuals exhibited higher overuse during the pandemic, this effect diminished by 2022, suggesting a recovery of control. By extending the discussion of digital inequality beyond access and benefits to include overuse, this study expands the landscape of digital inequalities, revealing a new form of stratification in which economic resources shape not only digital advantages but also the ability to mitigate digital risks.
{"title":"Wealth, digital overuse, and the changing landscape of digital inequality","authors":"Soyoung Park","doi":"10.1016/j.chb.2025.108805","DOIUrl":"10.1016/j.chb.2025.108805","url":null,"abstract":"<div><div>Wealth has long influenced digital inequality by shaping access to and benefits from technologies, yet its role in digital overuse—characterized by perceived dissatisfaction and negative consequences rather than mere screen time—remains underexplored. This study investigates the relationship between income and digital overuse, using data from the 2019–2022 Korean Smartphone Overuse Survey, with 101,625 respondents. Digital overuse is both defined and assessed in terms of self-control failure, behavioral salience, and negative after-effects, analyzed using quantile regression across income percentiles. This study examines whether the unintended consequences of digital engagement, like overuse, are also stratified along socioeconomic lines—just as the benefits of technology have been. To explore this, we test two hypotheses in the context of COVID-19: the Affluence Dependency Hypothesis, which suggests that affluent individuals are more prone to digital overuse due to greater access, and the Resourceful Autonomy Hypothesis, which posits that higher-income individuals are better able to regulate their usage. Results indicate that while affluent individuals exhibited higher overuse during the pandemic, this effect diminished by 2022, suggesting a recovery of control. By extending the discussion of digital inequality beyond access and benefits to include overuse, this study expands the landscape of digital inequalities, revealing a new form of stratification in which economic resources shape not only digital advantages but also the ability to mitigate digital risks.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108805"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-10-10DOI: 10.1016/j.chb.2025.108824
Hyoseok Kim
While algorithms increasingly outperform human experts and gain widespread adoption, many individuals still resist using them due to algorithmic aversion. Although prior research has examined the appreciation and avoidance of algorithmic advice, the underlying mechanisms driving these decisions remain underexplored. This paper investigates the role of individuals’ readiness to act, specifically whether they adopt a deliberative or implemental mindset, in shaping their openness to algorithmic advice. Across three hypothetical studies and one incentive-compatible study, results show that individuals in a deliberative mindset, characterized by thoughtful evaluation, tend to prefer advice from human sources. In contrast, those in an implemental mindset, characterized by action-oriented thinking, are more likely to prefer algorithmic advice. Additionally, the findings reveal that perceived uncertainty moderates the influence of mindset on algorithmic receptiveness. These findings offer nuanced insights into the psychological mechanisms that drive engagement with algorithms and suggest practical strategies to enhance collaboration with both algorithmic and human recommendations.
{"title":"Beyond algorithm aversion: The impact of psychological readiness on algorithmic advice","authors":"Hyoseok Kim","doi":"10.1016/j.chb.2025.108824","DOIUrl":"10.1016/j.chb.2025.108824","url":null,"abstract":"<div><div>While algorithms increasingly outperform human experts and gain widespread adoption, many individuals still resist using them due to algorithmic aversion. Although prior research has examined the appreciation and avoidance of algorithmic advice, the underlying mechanisms driving these decisions remain underexplored. This paper investigates the role of individuals’ readiness to act, specifically whether they adopt a deliberative or implemental mindset, in shaping their openness to algorithmic advice. Across three hypothetical studies and one incentive-compatible study, results show that individuals in a deliberative mindset, characterized by thoughtful evaluation, tend to prefer advice from human sources. In contrast, those in an implemental mindset, characterized by action-oriented thinking, are more likely to prefer algorithmic advice. Additionally, the findings reveal that perceived uncertainty moderates the influence of mindset on algorithmic receptiveness. These findings offer nuanced insights into the psychological mechanisms that drive engagement with algorithms and suggest practical strategies to enhance collaboration with both algorithmic and human recommendations.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108824"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-10-04DOI: 10.1016/j.chb.2025.108818
Julie A. Corrigan , Elena Forzani
While there has been a growing body of research demonstrating how people use critical online reasoning and evaluation in a variety of contexts, there has been little research comparing the cognitive and metacognitive strategies of emerging versus established evaluators. This gap makes it difficult to design curriculum when little is known about the progression of learning required to evaluate online. Therefore, the purpose of this study is to investigate the spectrum of evaluation strategies used by emerging and established evaluators. To do this, we invited 20 participants—ranging from emerging to expert evaluators—to complete a task involving the evaluation of online science information. We then used think-alouds, semi-structured interviews, and analyses of their school/work artefacts to further observe their evaluation strategies. We found that both emerging and established evaluators used a range of cognitive and metacognitive strategies to evaluate online information. However, compared to novices, experts used more instances of corroboration; demonstrated more criticality and reflexivity; and exhibited more metacognitive strategies. The study resulted in the description of a range of cognitive and metacognitive strategies that could be used as a starting point to chart a progression of learning, which describes qualitatively more complex and varied skills and knowledge used to critically evaluate online information. This study offers a holistic synthesis of the strategies necessary to evaluate online information credibility.
{"title":"“I would have gone to the original source”: Emerging and established readers’ cognitive and metacognitive strategies during online evaluation","authors":"Julie A. Corrigan , Elena Forzani","doi":"10.1016/j.chb.2025.108818","DOIUrl":"10.1016/j.chb.2025.108818","url":null,"abstract":"<div><div>While there has been a growing body of research demonstrating how people use critical online reasoning and evaluation in a variety of contexts, there has been little research comparing the cognitive and metacognitive strategies of emerging versus established evaluators. This gap makes it difficult to design curriculum when little is known about the progression of learning required to evaluate online. Therefore, the purpose of this study is to investigate the spectrum of evaluation strategies used by emerging and established evaluators. To do this, we invited 20 participants—ranging from emerging to expert evaluators—to complete a task involving the evaluation of online science information. We then used think-alouds, semi-structured interviews, and analyses of their school/work artefacts to further observe their evaluation strategies. We found that both emerging and established evaluators used a range of cognitive and metacognitive strategies to evaluate online information. However, compared to novices, experts used more instances of corroboration; demonstrated more criticality and reflexivity; and exhibited more metacognitive strategies. The study resulted in the description of a range of cognitive and metacognitive strategies that could be used as a starting point to chart a progression of learning, which describes qualitatively more complex and varied skills and knowledge used to critically evaluate online information. This study offers a holistic synthesis of the strategies necessary to evaluate online information credibility.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108818"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-22DOI: 10.1016/j.chb.2025.108806
Diwakar Y. Dube , Mathy Vandhana Sannasi , Markos Kyritsis , Stephen R. Gulliver
The automatic identification of human emotion, from low-resolution cameras is important for remote monitoring, interactive software, pro-active marketing, and dynamic customer experience management. Even though facial identification and emotion classification are active fields of research, no studies, to the best of our knowledge, have compared the performance of humans and Machine Learning Algorithms (MLAs) when classifying facial emotions from media suffering from systematic feature loss. In this study, we used singular value decomposition to systematically reduce the number of features contained within facial emotion images. Human participants were then asked to identify the facial emotion contained within the onscreen images, where image granularity was varied in a stepwise manner (from low to high). By clicking a button, participants added feature vectors until they were confident that they could categorise the emotion. The results of the human performance trials were compared against those of a Convolutional Neural Network (CNN), which classified facial emotions from the same media images. Findings showed that human participants were able to cope with significantly greater levels of granularity, achieving 85 % accuracy with only three singular image vectors. Humans were also more rapid when classifying happy faces. CNNs are as accurate as humans when given mid- and high-resolution images; with 80 % accuracy at twelve singular image vectors or above. The authors believe that this comparison concerning the differences and limitations of human and MLAs is critical to (i) the effective use of CNN with lower-resolution video, and (ii) the development of useable facial recognition heuristics.
{"title":"Facial emotion recognition from feature loss media: Human versus machine learning algorithms","authors":"Diwakar Y. Dube , Mathy Vandhana Sannasi , Markos Kyritsis , Stephen R. Gulliver","doi":"10.1016/j.chb.2025.108806","DOIUrl":"10.1016/j.chb.2025.108806","url":null,"abstract":"<div><div>The automatic identification of human emotion, from low-resolution cameras is important for remote monitoring, interactive software, pro-active marketing, and dynamic customer experience management. Even though facial identification and emotion classification are active fields of research, no studies, to the best of our knowledge, have compared the performance of humans and Machine Learning Algorithms (MLAs) when classifying facial emotions from media suffering from systematic feature loss. In this study, we used singular value decomposition to systematically reduce the number of features contained within facial emotion images. Human participants were then asked to identify the facial emotion contained within the onscreen images, where image granularity was varied in a stepwise manner (from low to high). By clicking a button, participants added feature vectors until they were confident that they could categorise the emotion. The results of the human performance trials were compared against those of a Convolutional Neural Network (CNN), which classified facial emotions from the same media images. Findings showed that human participants were able to cope with significantly greater levels of granularity, achieving 85 % accuracy with only three singular image vectors. Humans were also more rapid when classifying happy faces. CNNs are as accurate as humans when given mid- and high-resolution images; with 80 % accuracy at twelve singular image vectors or above. The authors believe that this comparison concerning the differences and limitations of human and MLAs is critical to (i) the effective use of CNN with lower-resolution video, and (ii) the development of useable facial recognition heuristics.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108806"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-19DOI: 10.1016/j.chb.2025.108799
Shikang Peng , Wilma A. Bainbridge
Visual content on social media plays a key role in entertainment and information sharing, yet some images gain more engagement than others. We propose that image memorability – the ability to be remembered – may predict viral potential. Using 1247 Reddit image posts across three timepoints, we assessed memorability with neural network ResMem and correlated the predicted memorability scores with virality metrics. Memorable images were consistently associated with more comments, even after controlling for image categories with ResNet-152. Semantic analysis revealed that memorable images relate to more neutral-affect comments, suggesting a distinct pathway to virality from emotional contents. Additionally, visual consistency analysis showed that memorable posts inspired diverse, externally-associated comments. By analyzing ResMem's layers, we found semantic distinctiveness was key to both memorability and virality. This study highlights memorability as a unique correlate of social media virality, offering insights into how visual features and human cognitive behavioral interactions are associated with online engagement.
{"title":"Image memorability predicts social media virality and externally-associated commenting","authors":"Shikang Peng , Wilma A. Bainbridge","doi":"10.1016/j.chb.2025.108799","DOIUrl":"10.1016/j.chb.2025.108799","url":null,"abstract":"<div><div>Visual content on social media plays a key role in entertainment and information sharing, yet some images gain more engagement than others. We propose that image memorability – the ability to be remembered – may predict viral potential. Using 1247 Reddit image posts across three timepoints, we assessed memorability with neural network ResMem and correlated the predicted memorability scores with virality metrics. Memorable images were consistently associated with more comments, even after controlling for image categories with ResNet-152. Semantic analysis revealed that memorable images relate to more neutral-affect comments, suggesting a distinct pathway to virality from emotional contents. Additionally, visual consistency analysis showed that memorable posts inspired diverse, externally-associated comments. By analyzing ResMem's layers, we found semantic distinctiveness was key to both memorability and virality. This study highlights memorability as a unique correlate of social media virality, offering insights into how visual features and human cognitive behavioral interactions are associated with online engagement.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108799"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-09-22DOI: 10.1016/j.chb.2025.108803
Yuqing Liu , Bu Zhong , Jiaxuan Wang , Yao Song
A novel approach is essential to assess viewers' emotional responses to online music videos, as the emotional coherence between perceived and induced reactions has not been thoroughly explored. This research investigates the relationship between perceived and induced emotional responses to music videos through a unique multimodal framework that integrates electroencephalography (EEG) analysis with natural language processing to examine danmu—user-generated scrolling marquee comments synchronized to specific playback times. Employing a time-synchronized methodology, our deep learning model predicted continuous emotional scores from EEG signals based on danmu sentiment. The findings revealed an over 80 % similarity between the two forms of induced emotional data: EEG-derived emotion curves and danmu sentiment curves across five music videos. We explored periods of divergence by contrasting peak emotional responses during the climaxes of the music, highlighting the significant influence of the multimodal sentiment tone on the alignment between neurophysiological and behavioral emotional trajectories. This study uncovers the coherence between emotion curves derived from EEG and danmu data—a methodology that notably diverges from traditional reliance on self-reports or surveys. The partial consistency observed between perceived and induced emotions, along with the effects of emotional valence and arousal on brain-behavior synchronization, underscores the shared nature of emotions elicited by music videos. Contributing factors include the diversity of emotional experiences and expressions among individuals, as well as the intrinsic rhythmicity within music videos, both of which enhance emotional elicitation.
{"title":"A study of danmu: Detecting emotional coherence in music videos through synchronized EEG analysis","authors":"Yuqing Liu , Bu Zhong , Jiaxuan Wang , Yao Song","doi":"10.1016/j.chb.2025.108803","DOIUrl":"10.1016/j.chb.2025.108803","url":null,"abstract":"<div><div>A novel approach is essential to assess viewers' emotional responses to online music videos, as the emotional coherence between perceived and induced reactions has not been thoroughly explored. This research investigates the relationship between perceived and induced emotional responses to music videos through a unique multimodal framework that integrates electroencephalography (EEG) analysis with natural language processing to examine danmu—user-generated scrolling marquee comments synchronized to specific playback times. Employing a time-synchronized methodology, our deep learning model predicted continuous emotional scores from EEG signals based on danmu sentiment. The findings revealed an over 80 % similarity between the two forms of induced emotional data: EEG-derived emotion curves and danmu sentiment curves across five music videos. We explored periods of divergence by contrasting peak emotional responses during the climaxes of the music, highlighting the significant influence of the multimodal sentiment tone on the alignment between neurophysiological and behavioral emotional trajectories. This study uncovers the coherence between emotion curves derived from EEG and danmu data—a methodology that notably diverges from traditional reliance on self-reports or surveys. The partial consistency observed between perceived and induced emotions, along with the effects of emotional valence and arousal on brain-behavior synchronization, underscores the shared nature of emotions elicited by music videos. Contributing factors include the diversity of emotional experiences and expressions among individuals, as well as the intrinsic rhythmicity within music videos, both of which enhance emotional elicitation.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108803"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Japan has the world's fastest ageing population. In 2024, people aged 65 and older accounted for 29.3 % of the population. Many of these people require long-term care, and a shortage of 570,000 care workers is projected by 2040. Home-care robots are expected to reduce caregiver burden and support older adults' independence. Widespread adoption requires collaboration among policymakers, healthcare professionals, and the robotics industry to ensure users' dignity and privacy.
This study surveyed the willingness to use home-care robots and provide personal information from the perspective of actual or potential users (older adults, family caregivers, care staff) and employees of companies developing such robots. A total of 4786 questionnaires were distributed to 6 users groups and 10 companies, yielding 1122 user and 83 developer responses. Data were analyzed using univariate and multivariate regression.
The findings indicate that actual or potential users’ willingness to use home-care robots was influenced by age, receipt of care, interest in robot-related news, and motivation to contribute to society. In contrast, developers prioritised safety and privacy protection. Both groups were influenced by “openness to using robots” and “openness to use them even during the research and development stage”. Furthermore, 80 % of actual or potential users agreed to share personal information with medical and care professionals, and 40–50 % with development companies, for research and development purposes.
This study concludes that a collaborative ecosystem involving all stakeholders, aligned with ethical principles and shared interests, is essential for the successful development and implementation of home-care robots.
{"title":"Willingness to use home-care robots and views regarding the provision of personal information in Japan: comparison between actual or potential users and robot developers","authors":"Yumi Akuta , Sayuri Suwa , Tatsuhito Kamimoto , Hiroo Ide , Ayano Inuyama , Naonori Kodate , Atsuko Shimamura , Kieko Iida , Akiyo Yumoto , Nana Kawakami , Sachiho Jitsuisihi , Mayuko Tsujimura , Mina Ishimaru , Satoko Suzuki , Shunsuke Doi , Ayano Sakai , Seiko Iwase , Wenwei Yu","doi":"10.1016/j.chb.2025.108817","DOIUrl":"10.1016/j.chb.2025.108817","url":null,"abstract":"<div><div>Japan has the world's fastest ageing population. In 2024, people aged 65 and older accounted for 29.3 % of the population. Many of these people require long-term care, and a shortage of 570,000 care workers is projected by 2040. Home-care robots are expected to reduce caregiver burden and support older adults' independence. Widespread adoption requires collaboration among policymakers, healthcare professionals, and the robotics industry to ensure users' dignity and privacy.</div><div>This study surveyed the willingness to use home-care robots and provide personal information from the perspective of actual or potential users (older adults, family caregivers, care staff) and employees of companies developing such robots. A total of 4786 questionnaires were distributed to 6 users groups and 10 companies, yielding 1122 user and 83 developer responses. Data were analyzed using univariate and multivariate regression.</div><div>The findings indicate that actual or potential users’ willingness to use home-care robots was influenced by age, receipt of care, interest in robot-related news, and motivation to contribute to society. In contrast, developers prioritised safety and privacy protection. Both groups were influenced by “openness to using robots” and “openness to use them even during the research and development stage”. Furthermore, 80 % of actual or potential users agreed to share personal information with medical and care professionals, and 40–50 % with development companies, for research and development purposes.</div><div>This study concludes that a collaborative ecosystem involving all stakeholders, aligned with ethical principles and shared interests, is essential for the successful development and implementation of home-care robots.</div></div>","PeriodicalId":48471,"journal":{"name":"Computers in Human Behavior","volume":"174 ","pages":"Article 108817"},"PeriodicalIF":8.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-10-03DOI: 10.1016/j.chb.2025.108821
Anna Sagana , Mengying Zhang , Melanie Sauerland
This study examined public attitudes toward police use of AI-driven facial recognition technology (FRT) for face detection, identification, verification, tracking, kinship verification, and masked perpetrator recognition. In a scenario-based survey with N = 507 participants, we investigated how perceptions of trust, fairness, accuracy, and support for specific FRT applications were influenced by general AI knowledge, trust in law enforcement, and application type. Masked face identification and kinship verification consistently received the lowest trust, fairness, accuracy, and support ratings, while face verification gathered the highest levels of acceptance. Contrary to expectations, deeper general AI knowledge was linked with decreased trust and support for FRTs in policing contexts. This suggests that technological literacy enhanced critical awareness of algorithmic limitations and ethical concerns. Participants expressed significant concerns about algorithmic bias, privacy implications, and surveillance capabilities. Trust in law enforcement emerged as the strongest predictor of FRT acceptance, indicating that acceptance of AI is embedded in broader socio-political relationships rather than determined by technological concerns alone. These findings contribute to our understanding of the social embeddedness of AI technologies and emphasize the need for governance frameworks that address not only technical performance but also institutional accountability and transparency in algorithmic systems deployed within law enforcement contexts.
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