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How gaming goal pursuit and expert-rated computer game features interact to affect human game use behavior 游戏目标追求和专家级电脑游戏功能是如何相互作用影响人类游戏使用行为的
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-06 DOI: 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.
网络游戏是流行的计算机系统。游戏开发者需要不断修改游戏元素,以激励玩家继续使用游戏,并融入符合玩家期望的功能。我们知道玩家有强烈的目标追求动机,但我们还不知道玩家的期望如何与游戏元素互动,从而有效地提高玩家的使用率,从而揭示了知识差距。为了弥补这一空白,我们采用目标梯度理论构建了研究模型。我们从两个数据源收集了完整的回答:1724名游戏玩家作为第一个数据源,59名游戏专家作为第二个数据源。玩家感知反应与专家评价反应相结合,以检查玩家的期望和游戏元素会对游戏使用产生直接和缓和的影响。该研究结果独特地表明,对角色成长的期望既是先决条件,也是调节因素,从而提高了玩家的游戏使用率,从而从理论上深化了对目标梯度理论的理解。我们的研究结果提供了新颖的见解,即带有可见虚拟角色和基于回合的设计元素的在线游戏不会增加玩家的游戏使用率,但基于回合的游戏设计元素会增强玩家的影响力;关于游戏使用的目标实现动机
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引用次数: 0
The quantification of the gaming experience: Self-tracking practices and game metrics among casual players, esports players, and streamers 游戏体验的量化:休闲玩家、电子竞技玩家和主播的自我跟踪实践和游戏参数
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-10 DOI: 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.
在当代游戏中,玩家越来越依赖数字数据来指导游戏决策、与他人互动并提高表现。特别是随着电子竞技和流媒体实践的兴起,游戏“数字”已经成为玩家发展的核心。本研究旨在探讨游戏参数如何影响不同类型玩家的游戏体验,调查游戏数据是否以及如何影响他们的表现并塑造他们的代理感。为此,我们采用了解释性定性方法,并对休闲玩家、电子竞技玩家和主播进行了40次半结构化访谈,要求参与者讲述他们如何使用和解释游戏数据。然后,我们通过扩展思维理论的镜头来解释收集到的材料,并使用主题分析进行分析。研究结果显示,不同类型的玩家追踪、理解和信任游戏参数的方式各不相同,这些参数可能会扩展他们的认知过程。此外,研究结果还表明,玩家的游戏知识水平会影响他们处理游戏数据并相应地调整行为的方式。这些发现还表明,过度强调游戏参数的“客观性”可能会导致玩家过度依赖外部数值验证,从而削弱他们的表现和代理感。相比之下,深入了解游戏机制并完善游戏感觉的玩家能够更好地控制自己的游戏决策和行为。总之,这项研究有助于理解游戏中的自我跟踪及其对玩家代理、认知和表现的影响。
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引用次数: 0
Wealth, digital overuse, and the changing landscape of digital inequality 财富、数字设备的过度使用,以及不断变化的数字不平等现象
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-22 DOI: 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.
长期以来,财富通过影响技术的获取和受益,影响着数字不平等,但它在数字过度使用中的作用——其特征是感知到的不满和负面后果,而不仅仅是屏幕时间——仍未得到充分探讨。这项研究调查了收入与数字过度使用之间的关系,使用了2019-2022年韩国智能手机过度使用调查的数据,共有101625名受访者。数字过度使用的定义和评估是根据自我控制失败、行为突出和负面后果来进行的,并使用跨收入百分位数的分位数回归进行分析。这项研究考察了数字参与的意外后果,比如过度使用,是否也像技术的好处一样,按照社会经济线分层。为了探讨这一点,我们在COVID-19的背景下测试了两个假设:富裕依赖假设,这表明富裕的个人更容易过度使用数字产品,因为更多的访问,以及资源自治假设,这假设高收入的个人能够更好地调节他们的使用。结果表明,虽然富裕的个人在大流行期间表现出更高的过度使用,但这种影响到2022年减弱,表明控制权的恢复。通过将对数字不平等的讨论从获取和收益扩展到过度使用,本研究扩大了数字不平等的格局,揭示了一种新的分层形式,在这种分层形式中,经济资源不仅塑造了数字优势,还塑造了减轻数字风险的能力。
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引用次数: 0
Beyond algorithm aversion: The impact of psychological readiness on algorithmic advice 超越算法厌恶:心理准备对算法建议的影响
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-10 DOI: 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.
虽然算法的表现越来越优于人类专家,并得到了广泛的采用,但由于对算法的厌恶,许多人仍然拒绝使用算法。尽管之前的研究已经研究了对算法建议的欣赏和回避,但驱动这些决策的潜在机制仍未得到充分探索。本文研究了个人的行动准备,特别是他们是否采取审慎或实施的心态,在塑造他们对算法建议的开放性方面的作用。在三项假设研究和一项激励相容研究中,结果表明,具有深思熟虑心态的个体,以深思熟虑的评估为特征,倾向于更喜欢来自人力资源的建议。相比之下,那些具有实施型思维的人,以行动为导向的思维为特征,更有可能喜欢算法建议。此外,研究结果表明,感知不确定性调节心态对算法接受性的影响。这些发现为推动与算法互动的心理机制提供了细致入微的见解,并提出了加强与算法和人类推荐合作的实用策略。
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引用次数: 0
“I would have gone to the original source”: Emerging and established readers’ cognitive and metacognitive strategies during online evaluation “我会去看原文”:在线评价过程中读者认知和元认知策略的形成与形成
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-04 DOI: 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.
虽然有越来越多的研究表明人们如何在各种情况下使用批判性的在线推理和评估,但很少有研究比较新兴评估者和成熟评估者的认知和元认知策略。当人们对在线评估所需的学习进度知之甚少时,这种差距使得课程设计变得困难。因此,本研究的目的是调查新兴和成熟的评估者使用的评估策略的频谱。为此,我们邀请了20名参与者——从新兴的评估者到专家评估者——来完成一项涉及评估在线科学信息的任务。然后,我们使用大声思考、半结构化访谈和对他们的学校/工作工件的分析来进一步观察他们的评估策略。我们发现,新兴和成熟的评估者都使用一系列认知和元认知策略来评估在线信息。然而,与新手相比,专家使用了更多的佐证实例;表现出更多的批判性和反身性;表现出更多的元认知策略。这项研究的结果是对一系列认知和元认知策略的描述,这些策略可以作为绘制学习进展图的起点,从质量上描述了用于批判性地评估在线信息的更复杂、更多样化的技能和知识。本研究提供了评估在线信息可信度所需策略的整体综合。
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引用次数: 0
Facial emotion recognition from feature loss media: Human versus machine learning algorithms 基于特征损失媒介的面部情感识别:人类与机器学习算法
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-22 DOI: 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.
从低分辨率摄像机中自动识别人类情感对于远程监控、交互式软件、主动营销和动态客户体验管理非常重要。尽管面部识别和情绪分类是活跃的研究领域,但据我们所知,还没有研究比较过人类和机器学习算法(MLAs)在从遭受系统特征丢失的媒体中对面部情绪进行分类时的表现。在这项研究中,我们使用奇异值分解来系统地减少面部情绪图像中包含的特征数量。然后要求人类参与者识别屏幕上图像中包含的面部情绪,其中图像粒度以循序渐进的方式变化(从低到高)。通过点击按钮,参与者添加特征向量,直到他们确信自己可以对情绪进行分类。人类表现试验的结果与卷积神经网络(CNN)的结果进行了比较,卷积神经网络从相同的媒体图像中分类面部情绪。研究结果表明,人类参与者能够处理更大的粒度水平,仅用三个单一图像向量就达到了85%的准确率。人类对快乐面孔进行分类的速度也更快。当给定中分辨率和高分辨率图像时,cnn和人类一样准确;在12个或以上的单一图像向量上具有80%的精度。作者认为,这种关于人类和mla的差异和局限性的比较对于(i)在低分辨率视频中有效使用CNN,以及(ii)开发可用的面部识别启发式至关重要。
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引用次数: 0
Image memorability predicts social media virality and externally-associated commenting 图像记忆性预测了社交媒体的病毒式传播和外部相关评论
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-19 DOI: 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.
社交媒体上的视觉内容在娱乐和信息分享中发挥着关键作用,但有些图片比其他图片更受欢迎。我们提出,图像记忆能力——被记住的能力——可以预测病毒的潜力。我们利用三个时间点的1247个Reddit图片帖子,用神经网络ResMem评估可记忆性,并将预测的可记忆性得分与病毒式传播指标联系起来。即使在用ResNet-152控制了图像类别之后,令人难忘的图像始终与更多的评论相关联。语义分析显示,令人难忘的图片与更中性的评论有关,这表明情感内容有一条独特的病毒式传播途径。此外,视觉一致性分析显示,令人难忘的帖子激发了不同的、外部相关的评论。通过分析ResMem的层次,我们发现语义独特性是记忆性和病毒性的关键。这项研究强调了记忆性是社交媒体病毒式传播的独特关联,为视觉特征和人类认知行为互动如何与在线参与相关提供了见解。
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引用次数: 0
A study of danmu: Detecting emotional coherence in music videos through synchronized EEG analysis 基于同步脑电图分析的音乐视频情感连贯性研究
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-22 DOI: 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.
一种新的方法对于评估观众对在线音乐视频的情感反应至关重要,因为感知和诱导反应之间的情感一致性尚未得到彻底的探索。本研究通过一个独特的多模态框架,将脑电图(EEG)分析与自然语言处理相结合,研究了感知和诱导对音乐视频的情绪反应之间的关系,以检查danmu用户生成的滚动字幕评论与特定播放时间同步。采用时间同步的方法,我们的深度学习模型根据脑电图信号预测基于丹木情绪的连续情绪评分。研究结果显示,两种形式的诱发情绪数据之间有超过80%的相似性:脑电图衍生的情绪曲线和五部音乐视频中的danmu情绪曲线。我们通过对比音乐高潮时的情绪反应高峰来探索分歧时期,强调了多模态情绪音调对神经生理和行为情绪轨迹之间一致性的重要影响。这项研究揭示了从脑电图和丹木数据中得出的情绪曲线之间的一致性,这种方法明显不同于传统的依赖自我报告或调查的方法。观察到的感知情绪和诱导情绪之间的部分一致性,以及情绪效价和觉醒对大脑行为同步的影响,强调了音乐视频引发的情绪的共同本质。影响因素包括个人情感体验和表达的多样性,以及音乐视频内在的节奏性,这两者都能增强情感的激发。
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引用次数: 0
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 在日本,使用家庭护理机器人的意愿和对提供个人信息的看法:实际或潜在用户与机器人开发商之间的比较
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-27 DOI: 10.1016/j.chb.2025.108817
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
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.
日本的人口老龄化速度是世界上最快的。2024年,65岁及以上的人口占总人口的29.3%。其中许多人需要长期护理,预计到2040年将短缺57万名护理人员。家庭护理机器人有望减轻照顾者的负担,并支持老年人的独立。广泛采用需要决策者、医疗保健专业人员和机器人行业之间的合作,以确保用户的尊严和隐私。本研究调查了家庭护理机器人的使用意愿,并从实际或潜在用户(老年人、家庭护理人员、护理人员)和开发此类机器人的公司员工的角度提供了个人信息。共向6个用户组和10家公司发放4786份问卷,得到1122份用户回复和83份开发者回复。数据采用单因素和多因素回归分析。研究结果表明,实际或潜在用户使用家庭护理机器人的意愿受到年龄、接受护理、对机器人相关新闻的兴趣以及对社会做出贡献的动机的影响。相比之下,开发者优先考虑安全性和隐私保护。两组都受到“使用机器人的开放性”和“即使在研发阶段也可以使用机器人的开放性”的影响。此外,80%的实际或潜在用户同意与医疗和护理专业人员共享个人信息,40%至50%的用户同意与开发公司共享个人信息,用于研究和开发目的。这项研究的结论是,一个涉及所有利益相关者的协作生态系统,与道德原则和共同利益保持一致,对于家庭护理机器人的成功开发和实施至关重要。
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引用次数: 0
Public attitudes towards police use of AI-driven face recognition technology 公众对警方使用人工智能驱动的人脸识别技术的态度
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-03 DOI: 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.
本研究调查了公众对警方使用人工智能驱动的面部识别技术(FRT)进行面部检测、识别、验证、跟踪、亲属关系验证和蒙面犯罪者识别的态度。在一项N = 507名参与者的基于场景的调查中,我们调查了一般人工智能知识、对执法的信任和应用类型如何影响对特定FRT应用的信任、公平、准确性和支持的看法。蒙面识别和亲属关系验证始终获得最低的信任度、公平性、准确性和支持度评级,而面部验证获得了最高的接受度。与预期相反,更深入的一般人工智能知识与警务环境中对frt的信任和支持减少有关。这表明,技术素养增强了对算法局限性和伦理问题的批判意识。与会者对算法偏见、隐私影响和监控能力表达了严重担忧。对执法部门的信任是FRT接受度的最强预测指标,这表明对人工智能的接受程度根植于更广泛的社会政治关系中,而不仅仅是由技术问题决定的。这些发现有助于我们理解人工智能技术的社会嵌入性,并强调需要建立治理框架,不仅要解决技术性能问题,还要解决执法环境中部署的算法系统中的机构问责制和透明度问题。
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引用次数: 0
期刊
Computers in Human Behavior
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