Computers in Human Behavior: Artificial Humans最新文献

{"title":"The reasoning-like capabilities of large language models across different languages: Insights from representational similarity analysis","authors":"Chris M. Stolle ,&nbsp;Rongjun Yu ,&nbsp;Yi Huang","doi":"10.1016/j.chbah.2026.100250","DOIUrl":"10.1016/j.chbah.2026.100250","url":null,"abstract":"<div><div>Recent research shows that Large Language Models (LLMs) demonstrate human-comparable performance on various cognitive tasks, suggesting reasoning-like capabilities. However, the language dependency of these capabilities and the contribution of their neural network states remain underexplored. This study investigates how different prompts and languages influence the reasoning performance of LLMs compared to humans, while exploring the internal cognitive-like processes of LLMs through representational similarity analysis (RSA). Using scenario-based and mathematical Cognitive Reflection Test (CRT) questions across four languages, we evaluated the reasoning capabilities of LLM Qwen 2.5 (including Gemma 2.9 and Llama 3.1 replications). Results showed that language significantly impacts performance in scenario-based CRT that requires nuanced semantic processing. However, RSA of the inner state activations revealed that the LLM processed identical questions similarly across languages, suggesting that the model encodes semantics in a language-independent latent space. Additionally, the LLM's performance improved when it verbalised its reasoning, and this verbalisation increased similarity in activations. Layer-wise analyses revealed a U-shaped similarity pattern across early to late layers in Qwen and Gemma but not Llama. Furthermore, scenario-based and equivalent mathematical CRT versions elicited similar activation patterns for the paired questions, even after controlling for input and output confounds, pointing to format-agnostic reasoning mechanisms. These results highlight that while LLMs exhibit language-invariant semantic representations and format-agnostic reasoning, their performance remains sensitive to linguistic nuances and self-generated verbalisations, offering insights into both the strengths and limitations of their cognitive-like processing.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-supported problem-based learning for enhancing computational thinking skills in STEM education","authors":"Musa Adekunle Ayanwale ,&nbsp;Christian Basil Omeh","doi":"10.1016/j.chbah.2026.100263","DOIUrl":"10.1016/j.chbah.2026.100263","url":null,"abstract":"<div><div>By 2030, over 80% of skilled jobs will require higher order thinking such as computational thinking skills and problem-solving capabilities, yet most university students, especially in developing countries remain underprepared for such demands. This skills gap is particularly evident in computer robotics programming education, where traditional pedagogies fall short in fostering computational thinking (CT) and academic achievement. This study investigates the effectiveness of Artificial Intelligence-supported Problem-Based Learning (AI-PBL) in enhancing students' CT and performance in computer robotics programming within a Nigerian university context. Grounded in Vygotsky's Social Constructivist Theory, the study positions AI tools as “more capable peers,” offering adaptive scaffolding through intelligent systems embedded in inquiry-driven instruction. A quasi-experimental design with pre-test–post-test non-equivalent groups was employed to randomly assign the students. A total of 103 students were randomly assigned to experimental (AI-PBL) and control (conventional PBL) groups. Data was analyzed using ANCOVA and simple main effects tests. Results showed that students in the AI-PBL group significantly outperformed their peers in both posttest CT and academic achievement, controlling baseline scores. While gender did not significantly moderate the overall effects, both male and female students benefited from the AI-PBL approach. These results affirm the pedagogical potential of AI-enhanced PBL in STEM education, particularly in under-resourced contexts. The integration of intelligent systems not only improves student learning outcomes but also aligns with future workforce needs. The study calls for institutional and policy-level adoption of AI-PBL frameworks, investments in teacher training, and further research to ensure scalability. AI-supported pedagogy is not just innovative; it is essential for equitable skill acquisition and making student future-ready for world of work.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Making sense of nonsense: A qualitative investigation of how brainrot content serves generation Z's media needs","authors":"Anna Götzfried ,&nbsp;Maxi Heitmayer","doi":"10.1016/j.chbah.2026.100255","DOIUrl":"10.1016/j.chbah.2026.100255","url":null,"abstract":"<div><div>Brainrot refers to low-quality content saturating digital spaces and the cognitive deterioration resulting from consuming it. Through semi-structured interviews with 24 participants aged 13-26, this study examines the functions that brainrot serves for Gen Z. Users report that brainrot facilitates aesthetic experience through deliberate absurdity, enables resistance to attention economy exploitation, supports generational in-group formation, and provides escapism from digital oversaturation. Importantly, the findings suggest that social media infrastructure, coupled with the spread of GenAI tools, induce mental states of brainrot which precede and shape content creation, rather than consumption of brainrot content causing cognitive decline. Gen Z uses brainrot as a subversive strategy to reclaim agency within oversaturated media environments, challenging deficit-based framings of digital youth culture. This study therefore introduces the concept of <em>anti-gratification</em> - a previously untheorized media need where users actively seek content that rejects productivity and meaning-making.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining academicians' views on using generative artificial intelligence applications: Case for using ChatGPT from a perspective of opportunities and threats","authors":"Nurgün Gençel ,&nbsp;Fatma Gizem Karaoğlan Yılmaz ,&nbsp;Ramazan Yılmaz","doi":"10.1016/j.chbah.2026.100252","DOIUrl":"10.1016/j.chbah.2026.100252","url":null,"abstract":"<div><div>An innovation that the rapidly developing artificial intelligence (AI) technology has added to our lives in recent years has been chatbots powered by artificial intelligence. In the last year, ChatGPT, a new AI chatbot that is becoming more widespread every day, has been launched. ChatGPT is an AI that has the ability to analyze, synthesize, and interpret the questions asked. In this respect, the reflections of education began to be seen in a long time. The presented work focus on positive and negative opinions of academicians about ChatGPT both in their academic studies and within the scope of students and to present a solution idea about the problem of having students prepare their homework for ChatGPT, which is widely seen as a negative aspect. For this purpose, 50 academicians from Türkiye participated in the study within the scope of snowball sampling. A developed questionnaire for the semi-structured interview form was utilized to collect qualitative data in addition to quantitative data from academicians. Quantitative data were analyzed using descriptive statistics. Content analysis methods were used in qualitative data. According to the findings, the majority of academics found ChatGPT useful for their own studies and students. Academicians have stated that ChatGPT has features such as reinforcement, repetition, material diversity as useful aspects as well as providing motivation, increasing imagination and creativity. The negative aspects are seen as doing it with addiction outside of ethical problems, accustoming students to ease and reducing motivation. Regarding the problem of preparing homework, it is stated that assignments based on applications, comments and analyzes where ChatGPT is not enabled should be given and plagiarism programs should be used when necessary. In the last part of the study, an exemplary model was given to integrate ChatGPT into education and suggestions were made for educators and researchers.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How mindsets shape trust in AI: A dual path mechanism of self-efficacy and risk perception","authors":"Sicen Shen ,&nbsp;Ping Zhang ,&nbsp;Yuchang Jin ,&nbsp;Junxiu An","doi":"10.1016/j.chbah.2026.100270","DOIUrl":"10.1016/j.chbah.2026.100270","url":null,"abstract":"<div><div>With the rapid advancement of artificial intelligence (AI) technologies, their applications in daily life have become increasingly pervasive. In human-robot interaction, trust is considered a critical predictor for the adoption of new technologies. Drawing on the lens of risk, this study integrates two cognitive pathways—internal self-assessment and external risk evaluation—within the framework of protection motivation theory(PMT). A parallel mediation model is proposed to examine how a growth mindset influences human-robot trust through the dual mechanisms of risk perception and self-efficacy. In Study 1, data were collected through a survey, revealing that a growth mindset positively predicts human-robot trust, with self-efficacy and risk perception serving as parallel mediators. To further clarify the causal relationships, Study 2 employed an experimental design, manipulating participants' growth mindset and assessing their trust behavior in a trust game paradigm to validate the model. The results demonstrated that different mindsets led to significant variations in human-robot trust investment, reinforcing the parallel mediating roles of risk perception and self-efficacy. The findings elucidate the underlying mechanisms through which a growth mindset affects human-robot trust, extend the application of PMT to the domain of human-robot interaction, and provide new insights into the trust formation process in such interactions.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100270"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenomenologically human: Fine-tuning LLMs to simulate online group identity","authors":"Andrés Martínez Torres,&nbsp;Davide Morselli","doi":"10.1016/j.chbah.2026.100272","DOIUrl":"10.1016/j.chbah.2026.100272","url":null,"abstract":"<div><div>The study of online group identity can be constrained by methodological and ethical challenges, particularly in communities that are inaccessible, uncooperative, or promote harmful ideologies. This paper introduces a novel methodology that employs Large Language Models (LLMs) as experimental substitutes for community members. By fine-tuning Llama 3.2 (3B) and Mistral 0.3 (7B) on data from r/AskTRP, a banned forum within the “Manosphere”, we assess whether models can assimilate community language and function as group members. Using Term Frequency–Inverse Document Frequency (TF-IDF), qualitative analysis, and a Turing test with human evaluators, we find that fine-tuned models adopt the forum's linguistic and identity signals, producing outputs that are difficult to distinguish from those of real group members. We conceptualise such models as phenomenologically human: they can be studied before and after exposure to community discourse, enabling reproducible and ethically viable experiments on identity formation and language. Beyond descriptive text analysis, this approach advances a generative paradigm for social research, allowing scholars to probe community dynamics and ideologies through simulated members in ways that are not possible with human participants.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100272"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trusting in the competence of humans and artificially intelligent agents varying in generosity","authors":"Richa Gautam ,&nbsp;Nina Lauharatanahirun ,&nbsp;Jasmin Cloutier ,&nbsp;Jennifer T. Kubota","doi":"10.1016/j.chbah.2026.100265","DOIUrl":"10.1016/j.chbah.2026.100265","url":null,"abstract":"<div><div>Around the world and with increasing frequency, humans now interact with artificial intelligence (AI) in various domains and collaborative settings. Because of this rapid and growing integration, investigating human-AI interactions can identify effective ways for humans and AI to work together. In two preregistered experiments, we examined how competency (high or low), benevolence reputation (malevolent, neutral, or generous reciprocity), and recent feedback (meeting expectations for generous reciprocity) impact trust towards human and AI partners during iterative Trust Games. In Experiment 1, participants played with human or AI partners; in Experiment 2, participants played with both. Positive behavior from partners (generosity or reciprocity) was consistently rewarded with trust, regardless of context. However, the impact of agent type and competence were context dependent. In a comparative context, people initially trusted humans more, but as they learned about their partner from their behavior (reputation and recent actions (i.e., feedback)), differentiation between humans and AI generally decreased. However, when interacting with only a human or AI, they were treated similarly, and trust depended on the partner's behavior (reputation and feedback). Competency had a more nuanced, context-dependent effect: individuals initially trusted highly competent partners more when interacting with humans or AI, but competency shaped trust towards AI more than towards humans in a comparative context. Overall, these findings indicate that human-human and human-AI trust are context-dependent. Importantly, trust in AI can match trust in humans when AI exhibits benevolence.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100265"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adoption of AI-enabled mental health wearables in India: The roles of psychological assurance and algorithmic credibility","authors":"Sweeta Agrawal ,&nbsp;Abayomi O. Agbeyangi","doi":"10.1016/j.chbah.2026.100259","DOIUrl":"10.1016/j.chbah.2026.100259","url":null,"abstract":"<div><div>This study examines the willingness to adopt AI-based wearable devices for mental health diagnosis, with a focus on the importance of psychological safety and algorithmic trust. It highlights the need for early and ongoing diagnosis of mental health issues, as AI-enabled wearable devices with advanced digital biomarkers and machine learning can help achieve this goal. Unlike studies on trust and TAM in digital health that focus on the perceived usefulness of the system, this study is the first to show that psychological safety is the dominant factor with stronger effects on adoption, illustrating a unique trust-emotion mechanism in AI-assisted wearables for mental health. Developing and validating a multidimensional adoption model, this study is grounded within the Technology Acceptance Model (TAM), extended with trust-based constructs and the Information Systems Success Model. Data were collected through a cross-sectional online survey of 763 respondents from urban and semi-urban areas in the Indian states of Odisha, Maharashtra, Delhi, and Tamil Nadu, representing a range of educational qualifications and occupations. Using SmartPLS 4, the findings suggest that psychological assurance is the dominant predictor of behavioral intention (β = 0.659, p &lt; 0.001, 95% CI [0.608, 0.709]). While there was no direct effect on behavioral intention (β = −0.023, p = 0.452), a significant pathway trust was observed. Algorithmic credibility positively influenced psychological assurance (β = 0.276, p &lt; 0.001) and perceived diagnostic accuracy (β = 0.555, p &lt; 0.001), suggesting an indirect effect. Psychological assurance was positively influenced by perceived usefulness (β = 0.301, p &lt; 0.001), and a positive effect was observed on behavioural intention (β = 0.062, p = 0.029) in relation to perceived diagnostic accuracy. Finally, the authors concluded that a range of influencing factors affects perceived diagnostic accuracy, with a moderate effect (β = 0.102, p = 0.003) also being found. These results underscore the importance of examining both the emotional trust aspect and the technical accuracy aspect of AI-enabled mental health wearables. For practitioners and policymakers, the findings underscore the importance of focusing on explainable AI, clinician endorsement, and reassurance feedback loops to enhance the adoption of mental health technologies and improve mental health care outcomes.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal robotic storytelling integrating sound effects and background music","authors":"Sophia C. Steinhaeusser,&nbsp;Sophia Maier,&nbsp;Birgit Lugrin","doi":"10.1016/j.chbah.2025.100248","DOIUrl":"10.1016/j.chbah.2025.100248","url":null,"abstract":"<div><div>Music can induce emotions and is often used to enhance emotional experiences of storytelling media, while sound effects can convey information on a story’s environmental setting. While these non-speech sounds are well-integrated into traditional media, their use in newer forms such as robotic storytelling is still developing. To address this gap, we developed guidelines for emotion-inducing music based on theoretical knowledge from music theory, psychology, and media studies, and validated them in an online perception study. Subsequently, a laboratory prestudy compared the effects of the music’s source during robotic storytelling, finding no significant differences between the robotic storyteller and an external loudspeaker. Building on these results, our main study compared storytelling with added background music, sound effects, a combination of both, and a control condition without non-speech sounds. Results showed that while subjective evaluations of presentation liking and qualitative feedback did not significantly differ, background music alone yielded the best outcomes on standardized measures, enhancing transportation, cognitive absorption, emotion induction, and objectively assessed attention-related affects. These findings support incorporating emotion-inducing background music into robotic storytelling to enhance its immersive and emotional effects.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100248"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implicit neural measures of trust in artificial intelligence","authors":"Tobias Feldmann-Wüstefeld ,&nbsp;Eva Wiese","doi":"10.1016/j.chbah.2026.100274","DOIUrl":"10.1016/j.chbah.2026.100274","url":null,"abstract":"<div><div>Trust in AI systems is critical for effective collaboration, yet traditional measures—such as self-reports and behavioral proxies—are limited in capturing its dynamic and latent nature. This study introduces the <em>contralateral delay activity</em> (CDA), a neural marker of visual working memory load, as a novel, implicit index of trust. While the CDA has been widely used in change detection tasks to track memory load, we repurpose it here to measure how many working memory resources users offload to an AI-framed automated partner. Participants performed a lateralized memory task under low and high working memory load, collaborating with an AI whose reliability was experimentally manipulated across three phases: trust formation, violation, and restoration. The system was a rule-based automated agent framed to participants as an AI collaborator, as the psychological effects of AI framing are central to trust and offloading behavior. In dyadic trials, where the AI was responsible for one hemifield, the CDA amplitude served as an index of how much information participants chose to maintain themselves versus offload to the AI. As AI reliability increased, CDA amplitude rose, indicating greater trust and reliance. When AI reliability dropped during the violation phase, participants encoded more from both hemifields, and CDA amplitude declined. During trust restoration, CDA amplitude returned to pre-violation levels, indicating renewed reliance—though it did not match the high amplitude of solo trials, suggesting lingering mistrust. Behavioral measures (e.g., reliance, compliance, response time) tracked the CDA dynamics but lacked the resolution and specificity of the CDA. Together, these results establish the CDA as a powerful neural index of dynamic cognitive offloading that closely tracks behavioral and reliability-based indicators of trust. It captures trial-by-trial fluctuations in offloading behavior that reflect users’ evolving confidence in AI assistance, offering a continuous, covert, and cognitively grounded measure of trust in interactive settings.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"7 ","pages":"Article 100274"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}