Computers in Human Behavior: Artificial Humans最新文献

{"title":"Enhancing emotional support in human-robot interaction: Implementing emotion regulation mechanisms in a personal drone","authors":"Ori Fartook ,&nbsp;Zachary McKendrick ,&nbsp;Tal Oron-Gilad ,&nbsp;Jessica R. Cauchard","doi":"10.1016/j.chbah.2025.100146","DOIUrl":"10.1016/j.chbah.2025.100146","url":null,"abstract":"<div><div>We propose that social robots can enhance their social abilities by supporting peoples' emotional needs. We examined this concept by implementing four different mechanisms aimed at providing Emotional Support in a personal drone. These mechanisms (Affective Empathy, Cognitive Empathy, Positive Emotion Regulation (PER), and a Reasoning mechanism (yoU-turn)) provide various aspects of support ranging on the Emotional-Reasoning spectrum. In an online study (<em>N</em> = 95), first, participants were asked to sequentially recall situations where they experienced one of six emotional states (i.e., being calm, bored, excited, hyperactivated, scared, or sleepy).</div><div>Following each induced emotion, participants ranked their preferred drone response to their specific emotional state. Results indicate that participants' preferences were based on the valence of their emotional state, emphasizing the need for social drones to have multiple response mechanisms to support their users. This work contributes to the field of human-robot interaction by implementing validated support mechanisms into a robotic system as its emotional responses.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"If ChatGPT can do it, where is my creativity? generative AI boosts performance but diminishes experience in creative writing","authors":"Peidong Mei ,&nbsp;Deborah N. Brewis ,&nbsp;Fortune Nwaiwu ,&nbsp;Deshan Sumanathilaka ,&nbsp;Fernando Alva-Manchego ,&nbsp;Joanna Demaree-Cotton","doi":"10.1016/j.chbah.2025.100140","DOIUrl":"10.1016/j.chbah.2025.100140","url":null,"abstract":"<div><div>As generative AI (GenAI) becomes more sophisticated, it is increasingly being used as a tool to enhance creative expression and innovation. Along with its potential benefits, it is imperative that we examine pitfalls in how generative AI may affect the quality of creative thinking and possibly lead to a narrowing of diversity both in representation and thought. In this study, we employed an experimental design with 225 university students who completed a creative writing task with pre- and post-task surveys to assess ChatGPT's impact on their performance and experiences compared to a control group who did not use ChatGPT. Results show that using ChatGPT enhanced creativity of output and reduced the difficulty and effort required for the task, particularly for non-native English speakers. However, it also diminished the value and enjoyment of the task and raised moral concerns. We contribute to the nascent literature on GenAI by showing how ChatGPT assistance could potentially bolster human creativity by facilitating content delivery or providing useful counterpoint ideas. We also significantly advance scholarship on understanding experience of GenAI, demonstrating that bypassing the cognitive effort required for creativity by using ChatGPT could be harmful to the creative process and experience of creative tasks, especially when steps are not taken to address the use of AI in a transparent manner. Finally, our novel mixed-method study design offers a contribution to the methodological frameworks for the study of the effects and experience of GenAI. We discuss the study results in relation to implications for educational practices and social policy and argue that our results support recommending an integration of generative AI into higher education alongside practices that help to mitigate the negative impacts of AI use on student experience.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quid pro Quo: Information disclosure for AI feedback in Human-AI collaboration","authors":"Izabel Cvetkovic ,&nbsp;Imke Grashoff ,&nbsp;Ana Jovancevic ,&nbsp;Eva Bittner","doi":"10.1016/j.chbah.2025.100137","DOIUrl":"10.1016/j.chbah.2025.100137","url":null,"abstract":"<div><div>This paper explores the intersection of AI-driven teamwork facilitation and user privacy concerns in virtual collaboration. Specifically, we investigate the extent to which individuals in AI-supported teamwork environments are willing to disclose personal information to improve collaboration and performance. Leveraging a vignette-based study, we assess participants' intentions to disclose different different types of personal information, such as language and choice of words, talk times, body language or sentiments, during a professional virtual collaboration process. Thereby, conditions under which the information is disclosed vary, referring to whether the results of information disclosure are either shared individually or with the whole group and whether they are shared anonymously or non-anonymously. Drawing on privacy calculus theory, our investigation further incorporates crucial contextual factors—Trust in AI, Perceived Risks, and Perceived Benefits—to comprehensively examine their influence on individuals' intentions to disclose information. Our findings reveal notable differences in Intention to disclose across various Information types, with lower intentions observed particularly for emotion and attention level disclosures. Surprisingly, other manipulated factors, including the level of anonymity, do not show an effect in influencing disclosure intentions. Crucially, our study underscores the pivotal role of Trust in AI, emerging as a consistent predictor of Intention to disclose across all Information types. Furthermore, its impact on disclosure intentions is mediated by individuals’ perceptions of risks and benefits associated with disclosure. Our research contributes to the evolving field of privacy calculus theory by shedding light on the nuanced interplay between Trust, Perceived Risks, Benefits, and information disclosure to AI in teamwork scenarios. These insights bear implications for the effective deployment of AI in facilitating teamwork within the workplace, emphasizing the need for cultivating trust and understanding the specific sensitivities associated with different types of information.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teaming up with robots: Analysing potential and challenges with healthcare workers and defining teamwork","authors":"Anna M.H. Abrams,&nbsp;Lena Plum,&nbsp;Astrid M. Rosenthal-von der Pütten","doi":"10.1016/j.chbah.2025.100136","DOIUrl":"10.1016/j.chbah.2025.100136","url":null,"abstract":"<div><div>In interviews with healthcare workers, we explore the potential and challenges of future deployment of robotic assistance systems in healthcare. We focus on individual expectations, wishes and fears. We especially emphasize the potential role of robotic systems in team dynamics. Will robots be coworkers in the future or are they expected to be tools? Irrespective of that, are they anticipated to change coworking within a team? Following a grounded theoretical approach, we aim to generate new theories and research questions on robotic assistance systems from the perspective of healthcare workers. We find that healthcare workers are generally optimistic about the implementation of technology in their workplace and not all have pressing concerns. Paradoxically, we further find that reasoning for why a robot could be part of a team are similar among participants who are opposed to robots in teams and those who are in favour. While participants only focused on work- and task-related criteria when arguing why a robot could be a future colleague, they presented work-and task-unrelated criteria for why a robot could not be one. We discuss the expected impact of robotic assistance systems on work teams in healthcare and pose resulting questions to inspire future hypothetico-inferential research on human-robot teams. We conclude with arguing why current team definitions do not fit human-robot teams and propose a new theoretical model for teams that include humans and robots.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence and human decision making: Exploring similarities in cognitive bias","authors":"Hanna Campbell,&nbsp;Samantha Goldman,&nbsp;Patrick M. Markey","doi":"10.1016/j.chbah.2025.100138","DOIUrl":"10.1016/j.chbah.2025.100138","url":null,"abstract":"<div><div>This research explores the extent to which Artificial Personas (APs) generated by Large Language Models (LLMs), like ChatGPT, can exhibit cognitive biases similar to those observed in humans. Four studies focusing on well-documented psychological biases were conducted: the Halo Effect, In-Group Out-Group Bias, the False Consensus Effect, and the Anchoring Effect. Each study was designed to test whether APs respond to specific scenarios consistent with typical human responses documented in psychological literature. The findings reveal that APs can replicate these biases, suggesting that APs can model some aspects of human cognitive processing. However, the effect sizes observed were unusually large, suggesting that APs replicate and exaggerate these biases, behaving more like caricatures of human cognitive behavior. This exaggeration highlights the potential of APs to magnify underlying cognitive processes but also necessitates caution in applying these findings directly to human behavior.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individual differences and young children's engagement with a social robot","authors":"Ruby-Jane Barry ,&nbsp;Michelle M. Neumann ,&nbsp;David L. Neumann","doi":"10.1016/j.chbah.2025.100139","DOIUrl":"10.1016/j.chbah.2025.100139","url":null,"abstract":"<div><div>Social robots are being introduced in preschool to support early learning. However, little is known about the relationship between young children's individual differences and their engagement with social robots. In the present study, English-speaking children (<em>n</em> = 66; <em>M</em> age = 4.5 years, range = 2.7–5.6 years) completed a Simon Says and Question and Answer activity with a social robot and their behavioural engagement, positive emotional engagement, and verbal engagement (vocalisations) were measured. Child temperament (surgency, negative affect, and effortful control) and social and generalised anxiety were measured using parent reports. During the child-robot interactions, children engaged with the social robot overall, with greater verbal engagement for children with higher levels of surgency. In contrast, positive emotional engagement was lower for children with higher generalised anxiety. The findings suggest that aspects of children's individual differences are important and should be considered when using social robots at preschool to support young learners.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What makes children perceive or not perceive minds in generative AI?","authors":"Ying Xu ,&nbsp;Trisha Thomas ,&nbsp;Chi-Lin Yu ,&nbsp;Echo Zexuan Pan","doi":"10.1016/j.chbah.2025.100135","DOIUrl":"10.1016/j.chbah.2025.100135","url":null,"abstract":"<div><div>Children are increasingly engaging in dialogue and interactions with generative AI agents that can mimic human behaviors, raising questions about how children perceive and communicate with AI compared to humans. In an experimental study with 119 children aged 4–8, participants co-created stories in three conditions: with a generative AI agent via a speaker, with a physically present human partner, or with a human partner who was hidden and audible only through a speaker. Results showed a clear distinction in children's communication and perception of visible human partners compared to AI. Nuanced differences also emerged in children's perceptions of hidden human partners versus AI. When physical appearance was absent, children relied on linguistic and paralinguistic cues to assess human-likeness and form perceptions, but physical appearance became a more dominant factor when available. These results shed light on implications for the design of child-facing AI technologies, offering insights into how speech and physical features can be optimized to meet children's developmental and communicative needs.</div></div>","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"4 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Human divergent exploration capacity for material design: A comparison with artificial intelligence” [Comput. Hum. Behav.: Artificial Humans 2/1 (2024) 100064]","authors":"Hiroyuki Sakai,&nbsp;Kenroh Matsuda,&nbsp;Nobuaki Kikkawa,&nbsp;Seiji Kajita","doi":"10.1016/j.chbah.2025.100119","DOIUrl":"10.1016/j.chbah.2025.100119","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"3 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Erratum to “Choosing between human and algorithmic advisors: The role of responsibility sharing”[Comput. Hum. Behav.: Artificial Humans 1/2 (2023) 100009]","authors":"Lior Gazit ,&nbsp;Ofer Arazy ,&nbsp;Uri Hertz","doi":"10.1016/j.chbah.2025.100118","DOIUrl":"10.1016/j.chbah.2025.100118","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"3 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Science in a troubled era: Transforming challenges into opportunities for artificial intelligence, social robots, and artificial humans research","authors":"Matthieu J. Guitton","doi":"10.1016/j.chbah.2025.100125","DOIUrl":"10.1016/j.chbah.2025.100125","url":null,"abstract":"","PeriodicalId":100324,"journal":{"name":"Computers in Human Behavior: Artificial Humans","volume":"3 ","pages":"Article 100125"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}