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Malicious insider threats in cybersecurity: A fraud triangle and Machiavellian perspective 网络安全中的恶意内部威胁:欺诈三角和马基雅维利主义视角
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-24 DOI: 10.1016/j.chb.2025.108809
Chelsea Idensohn , Stephen Flowerday , Karl van der Schyff , Yi Ting Chua
Malicious insiders remain among the most persistent cybersecurity concerns, yet existing frameworks often overlook the psychological predispositions that drive unethical intent. This study examines how Machiavellianism, a dark personality trait characterized by manipulation, strategic self-interest, and moral disengagement, influences the elements of the well-established criminological framework of the Fraud Triangle to shape insider threat intention. Using a sample of 768 full-time U.S.-based employees and partial least squares structural equation modeling (PLS-SEM), the analysis investigates how Machiavellianism affects perceptions of pressure, opportunity, and rationalization. Results reveal that Machiavellianism significantly influences all three constructs, with rationalization emerging as the strongest and most significant pathway to the intention to commit malicious insider behavior. These findings highlight how individuals high in Machiavellianism cognitively justify unethical actions, positioning rationalization as a key psychological mechanism in threat formation. Theoretically, this study extends insider threat literature by demonstrating the relevance of personality traits, specifically Machiavellianism, in shaping key situational perceptions. It advances understanding of the Fraud Triangle by emphasizing justification not merely as a cognitive condition, but as a pivotal mechanism through which individuals justify malicious intent. By integrating a dark personality trait into a situational framework, this study refines our understanding of how insider threats emerge and supports more behaviorally informed approaches to cybersecurity risk modeling.
恶意的内部人员仍然是最持久的网络安全问题之一,但现有的框架往往忽视了驱动不道德意图的心理倾向。本研究探讨了马基雅维利主义(一种以操纵、战略自利和道德脱离为特征的黑暗人格特征)如何影响欺诈三角犯罪学框架的要素,从而形成内部威胁意图。本文以768名美国全职员工为样本,采用偏最小二乘结构方程模型(PLS-SEM),分析了马基雅维利主义如何影响人们对压力、机会和合理化的看法。结果显示,马基雅维利主义显著影响了所有三个构念,其中合理化是导致恶意内部行为意图的最强烈和最重要的途径。这些发现强调了马基雅维利主义高的个体如何在认知上为不道德行为辩护,将合理化定位为威胁形成的关键心理机制。从理论上讲,本研究通过展示人格特质,特别是马基雅维利主义在塑造关键情境感知方面的相关性,扩展了内部威胁文献。它通过强调正当性不仅是一种认知条件,而且是个体为恶意意图辩护的关键机制,促进了对欺诈三角的理解。通过将黑暗人格特征整合到情境框架中,本研究完善了我们对内部威胁如何出现的理解,并支持更多行为知情的网络安全风险建模方法。
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引用次数: 0
The impact of a robot's agreement (or disagreement) on human-human interpersonal closeness in a two-person decision-making task 在两人决策任务中,机器人的同意(或不同意)对人与人之间的亲密关系的影响
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-25 DOI: 10.1016/j.chb.2025.108807
Ting-Han Lin , Yuval Rubin Kopelman , Madeline Busse , Sarah Sebo , Hadas Erel
Robots and artificial agents are becoming increasingly integrated into our lives and show promise in assisting people in decision-making tasks. Despite their advantages, robot-assisted decision-making systems may have negative effects on the relationships between human team members. In this work, we examine the influence of the robot's agreement (or disagreement) on the interpersonal closeness between two participants in a two-person decision-making task. We test the robot's impact in two experiments: Experiment 1 (N = 172, 86 pairs) with a High Anthropomorphism Robot and Experiment 2 (N = 150, 75 pairs) with a Low Anthropomorphism Robot. For both experiments, we use a 2 x 2 study design to examine how the perceived interpersonal closeness between two participants was influenced by two aspects of robot behavior, namely the valence of the robot's feedback (positive feedback or negative feedback) and the treatment of the two participants (equal treatment or unequal treatment). Our results demonstrate that interacting with the High Anthropomorphism Robot led to greater interpersonal closeness between participants when the robot provided positive feedback as opposed to negative feedback. The Low Anthropomorphism Robot had a different and opposite effect: interactions with this robot led to greater interpersonal closeness when the robot's feedback was equal as opposed to unequal and when the robot provided negative feedback as opposed to positive feedback. Our results indicate that robots can shape human-human relationships when indicating their agreement with people's perspectives in two-person decision-making tasks and that the robot's influence depends on its appearance and communication style.
机器人和人工智能正越来越多地融入我们的生活,并有望帮助人们做出决策。尽管机器人辅助决策系统具有优势,但它可能对人类团队成员之间的关系产生负面影响。在这项工作中,我们研究了机器人的同意(或不同意)对两人决策任务中两个参与者之间的人际亲密度的影响。实验1 (N = 172,86对)采用高拟人化机器人,实验2 (N = 150,75对)采用低拟人化机器人。在这两个实验中,我们使用2 x 2的研究设计来检验两个参与者之间感知到的人际亲密度如何受到机器人行为的两个方面的影响,即机器人反馈的效价(积极反馈或消极反馈)和两个参与者的待遇(平等待遇或不平等待遇)。我们的研究结果表明,当机器人提供积极的反馈而不是消极的反馈时,与高度拟人化机器人的互动会导致参与者之间更大的人际关系亲密。低拟人化机器人有不同的和相反的效果:当机器人的反馈是平等的而不是不平等的,当机器人提供负面反馈而不是积极反馈时,与这个机器人的互动会导致更大的人际关系亲密。我们的研究结果表明,当机器人在两人决策任务中表明它们与人的观点一致时,机器人可以塑造人与人之间的关系,并且机器人的影响力取决于它的外观和沟通方式。
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引用次数: 0
How audiences make sense of deepfake resurrections: A multilevel analysis of realism, ethics, and cultural meaning 观众如何理解深度假复活:现实主义、伦理和文化意义的多层次分析
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.2025.108822
María T. Soto-Sanfiel , Qiaofei Wu
This exploratory qualitative study uses a multilevel framework, combining the Social Construction of Technology (SCOT) and the Cognitive-Affective-Normative (CAN) model, to examine how individual perceptions, emotions, and reflections shape acceptance of resurrection deepfakes, particularly those featuring deceased artists Salvador Dalí (surrealist painter, sculptor, and performance artist in a educative/museum campaign) and Lola Flores (folk singer, actress and dancer in a commercial/beer advertisement). Fifty-one participants (70.37 % female; Mean Age = 24.6) took part in six online focus groups, viewing two randomly selected deepfakes of deceased artists. Thematic analysis, using a phenomenological approach, identified primarily cognitive and evaluative reactions (e.g., judgments of authenticity and ethical considerations) with two key layers: first-order evaluations of realism and second-order societal impact assessments. These impacts spanned micro (individuals), meso (content creators/distributors), and macro (society-wide) levels. Most reactions were cognitively driven, while emotional responses were fewer and largely expressed discomfort or surprise. Moreover, cognitive, affective, and normative responses were shaped by institutional purposes and cultural narratives. This study makes a novel contribution by integrating SCOT and CAN into a multilevel framework for analyzing digital resurrection deepfakes, bridging micro-level appraisals with broader sociotechnical contexts. The findings yield theoretical insights and practical implications for media literacy, cultural heritage, and regulatory interventions in the governance of synthetic media, with particular relevance to resurrection deepfakes.
这个探索性质的研究使用了一个多层次的框架,结合了技术的社会建构(SCOT)和认知-情感-规范(CAN)模型,来研究个人的感知、情感和反思是如何塑造人们对复活的深度赝品的接受程度的,尤其是那些以已故艺术家萨尔瓦多Dalí(超现实主义画家、雕塑家和教育/博物馆活动中的行为艺术家)和洛拉弗洛雷斯(民谣歌手,商业广告(啤酒广告)中的女演员和舞者。51名参与者(70.37%为女性,平均年龄为24.6岁)参加了6个在线焦点小组,观看了随机选择的两幅已故艺术家的深度伪造作品。主题分析使用现象学方法,主要通过两个关键层确定认知和评价反应(例如,对真实性和伦理考虑的判断):一级现实主义评估和二级社会影响评估。这些影响跨越了微观(个人)、中观(内容创造者/分发者)和宏观(整个社会)层面。大多数反应是认知驱动的,而情绪反应较少,主要表达的是不适或惊讶。此外,认知、情感和规范性反应是由制度目的和文化叙事塑造的。本研究通过将SCOT和CAN整合到分析数字复活深度伪造的多层次框架中,将微观层面的评估与更广泛的社会技术背景联系起来,做出了新的贡献。这些发现为媒体素养、文化遗产和合成媒体治理中的监管干预提供了理论见解和实践意义,特别是与复活的深度造假有关。
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引用次数: 0
Substitution or augmentation? How organisational AI adoption approaches influence the willingness to stay of caregivers 替换还是增广?组织采用人工智能的方法如何影响护理人员留下来的意愿
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-21 DOI: 10.1016/j.chb.2025.108801
Li Jiang , Xuli Wang , Yuguang Xie , Junhong Zhu , Dongxiao Gu
In the care sector, the application of artificial intelligence (AI) has become a key strategy to alleviate workforce shortages. However, systematic research is still lacking on the differential effects of different AI adoption approaches on caregivers' willingness to stay and their underlying mechanisms. This study uses job demands-resources theory as basis to develop a dual-path model to explore the differences between AI augmentation and AI substitution in influencing caregivers' willingness to stay. Results of two experiments indicate that AI augmentation has a significantly stronger positive effect on willingness to stay than AI substitution, with the resource gain pathway of ‘self-efficacy–job satisfaction’ playing a partial mediating role. AI substitution also significantly enhances willingness to stay (compared with scenarios without AI), and its effect is mediated by the resource gain and job demand pathways of ‘identity threat–emotional exhaustion’. Additionally, AI learning anxiety significantly weakens the positive effects of both AI adoption approaches, whilst job replacement anxiety only negatively moderates the effect of AI substitution. This study provides a markedly nuanced theoretical perspective for research on organisational AI adoption and offers practical insights into the practical application of AI in the care sector.
在护理领域,人工智能(AI)的应用已成为缓解劳动力短缺的关键战略。然而,关于不同人工智能采用方式对护理人员留下来意愿的差异影响及其潜在机制的系统研究仍然缺乏。本研究以工作需求-资源理论为基础,构建双路径模型,探讨人工智能增强与人工智能替代对照顾者留下来意愿的影响差异。两项实验结果表明,人工智能增强对留任意愿的正向影响显著强于人工智能替代,其中“自我效能感-工作满意度”的资源获取路径起部分中介作用。人工智能替代也显著提高了留下来的意愿(与没有人工智能的情景相比),其影响是由“身份威胁-情感耗竭”的资源获取和工作需求途径介导的。此外,人工智能学习焦虑显著削弱了两种人工智能采用方式的积极影响,而工作替代焦虑仅负向调节人工智能替代的影响。本研究为组织人工智能采用的研究提供了一个明显微妙的理论视角,并为人工智能在护理部门的实际应用提供了实际见解。
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引用次数: 0
Rethinking social media and mental health: The role of emotion regulation difficulties 重新思考社交媒体与心理健康:情绪调节困难的作用
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-08 DOI: 10.1016/j.chb.2025.108825
Daniel J. Brown , Riley Scott , Renee Ireland , Jacqueline Harness , Daniel J. Phipps , Jacob J. Keech
Research, on the whole, does not suggest that time spent on social media is associated with risks to mental health, although it is possible there are more nuances about how people use social media. Further, evidence suggests that individuals with emotion regulation difficulties may be drawn to certain social media behaviours as a means of coping with distress. The present study aimed to examine whether emotion regulation difficulties predict patterns of social media use and, in turn, symptoms of depression and anxiety. We examined four distinct types of social media use: (1) image management-based, (2) social comparison-based, (3) negative engagement-based, and (4) passive consumption-based. Sampling 548 adults aged 18–84 years (Mage = 33.16, SD = 17.37; 401 (73.2 %) female; 128 (23.2 %) male), we tested a structural equation model to examine whether the four distinct types of social media use mediated links between difficulties in emotion regulation at Time 1 and depression and anxiety symptomology at Time 2, one week later. Results suggested that, when controlling for age, difficulties in emotion regulation significantly predicted all types of social media use and symptoms of depression and anxiety over one week. Only comparison-based social media use predicted anxiety symptoms over time. The model explained 50.1 % and 52.1 % of the variance in depression and anxiety symptoms, respectively. Taken together, these findings suggest the critical importance of emotion regulation in predicting mental health. By contrast, with the exception of social comparison and anxiety, no form of social media use predicted mental health outcomes.
总的来说,研究并没有表明花在社交媒体上的时间与心理健康风险有关,尽管人们使用社交媒体的方式可能存在更多细微差别。此外,有证据表明,有情绪调节困难的个体可能会被某些社交媒体行为所吸引,作为应对痛苦的一种手段。本研究旨在研究情绪调节困难是否能预测社交媒体的使用模式,进而预测抑郁和焦虑的症状。我们研究了四种不同类型的社交媒体使用:(1)基于形象管理的,(2)基于社会比较的,(3)基于消极参与的,(4)基于被动消费的。548名18-84岁成人(年龄:Mage = 33.16, SD = 17.37),女性401名(73.2%);128(23.2%)男性),我们测试了一个结构方程模型,以检验四种不同类型的社交媒体使用是否介导了时间1的情绪调节困难与时间2(一周后)的抑郁和焦虑症状之间的联系。结果表明,在控制年龄的情况下,情绪调节困难显著预测了一周内所有类型的社交媒体使用以及抑郁和焦虑症状。随着时间的推移,只有基于比较的社交媒体使用才能预测焦虑症状。该模型分别解释了50.1%和52.1%的抑郁和焦虑症状差异。综上所述,这些发现表明情绪调节在预测心理健康方面至关重要。相比之下,除了社会比较和焦虑,没有任何形式的社交媒体使用预测心理健康结果。
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引用次数: 0
Beyond the dichotomy of use and Not-Use: Forms and motivations of user non-use behaviors toward AI customer service 超越使用和不使用的二分法:用户对人工智能客户服务的不使用行为的形式和动机
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-09-11 DOI: 10.1016/j.chb.2025.108793
Junchen Yao , Dongqi Yan , Hongzhong Zhang
In the era of artificial intelligence, the dichotomy of acceptance versus abandonment falls short in capturing the complexities of user behavior. This study focuses on AI customer service (AICS)—a pervasive but difficult-to-abandon AI application—to explore how users’ perceptions shape diverse non-use behaviors. Through a sequential mixed-methods design, we develop and test an extended Expectation Confirmation Model (ECM). Qualitative findings first reveal that users evaluate AICS through functional and emotional dimensions, and engage in two forms of non-use behavior: active (e.g., self-education, seeking help) and passive (e.g., teasing, verbal abuse). Building on these insights, quantitative results show that both functional and emotional performance perceptions significantly predict satisfaction. Crucially, cognitive satisfaction negatively predicts passive non-use, whereas emotional satisfaction positively predicts active non-use—a finding that challenges conventional models where satisfaction predicts continuance. Moreover, expectation confirmation significantly moderates the relationship between performance perception and satisfaction. Our research extends ECM by introducing a dual-pathway model of non-use and offers managers and designers nuanced insights for improving human-AI coexistence.
在人工智能时代,接受与放弃的二分法不足以捕捉用户行为的复杂性。本研究的重点是人工智能客户服务(AICS)——一种普遍但难以放弃的人工智能应用——以探索用户的感知如何塑造不同的非使用行为。通过顺序混合方法设计,我们开发并测试了扩展的期望确认模型(ECM)。定性研究结果首先揭示了用户通过功能和情感维度来评估AICS,并参与两种形式的非使用行为:主动(如自我教育、寻求帮助)和被动(如戏弄、言语虐待)。基于这些见解,定量结果表明,功能和情感表现感知都能显著预测满意度。关键是,认知满意度消极地预测被动不使用,而情感满意度积极地预测主动不使用——这一发现挑战了传统模型,即满意度预测持续使用。此外,期望确认显著调节绩效感知与满意度之间的关系。我们的研究通过引入非使用的双路径模型扩展了ECM,并为管理人员和设计师提供了改善人类与人工智能共存的细致见解。
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引用次数: 0
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
Tinder for teens: Youth digital intimate cultures and tech facilitated violence on Snapchat 青少年Tinder:青少年数字亲密文化和科技助长了Snapchat上的暴力
IF 8.9 1区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Pub Date : 2026-01-01 Epub Date: 2025-10-09 DOI: 10.1016/j.chb.2025.108823
Betsy Milne , Jessica Ringrose , Tanya Horeck , Kaitlynn Mendes
Snapchat has long been a pivotal space for youth digital intimate and sexual cultures, as well as gendered and sexual risks and harms. Despite being one of the most widely used social media platforms among youth, there has been little in-depth research that connects Snapchat's unique features and affordances with young users' practices, behaviours, and experiences on the platform. Responding to this gap, our study used mixed methods to explore British teens' diverse social, sexual, and intimate experiences on Snapchat. We discuss how Snapchat's unique features, such as disappearing images (“Snaps”), algorithmic friend recommendations (“Quick Adds”), and geolocation tracking technology ("Snap Maps”), form new conditions and environments for teens' experiences of socialising, courtship, sexting, and technology-facilitated gender-based and sexual violence. We explore how teens'desires for intimacy underpin their motivations to continue to engage in a range of risk-taking activities—despite their awareness of the dangers involved. We conclude with recommendations for better platform specific regulation and digital literacy that pays attention to teens' rights and agency.
长期以来,Snapchat一直是年轻人数字亲密和性文化的关键空间,也是性别和性风险和伤害的关键空间。尽管Snapchat是年轻人中使用最广泛的社交媒体平台之一,但很少有深入的研究将Snapchat的独特功能和功能与年轻用户在该平台上的实践、行为和体验联系起来。为了弥补这一差距,我们的研究采用了混合方法来探索英国青少年在Snapchat上的各种社交、性和亲密体验。我们将讨论Snapchat的独特功能,如消失图片(“Snaps”)、算法好友推荐(“Quick添加”)和地理位置跟踪技术(“Snap Maps”),如何为青少年的社交、求爱、性短信以及技术促进的性别暴力和性暴力体验形成新的条件和环境。我们探讨了青少年对亲密的渴望是如何支撑他们继续从事一系列冒险活动的动机的——尽管他们意识到其中的危险。最后,我们提出了更好的平台特定监管和关注青少年权利和代理的数字素养的建议。
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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
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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Computers in Human Behavior
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