Computers in Human Behavior: Artificial Humans最新文献

Artificial Intelligence (AI) can infer one's personality from online behavior, which offers an interesting alternative to traditional, self-reported personality assessments. Recent studies comparing AI-inferred personality to personality derived from traditional assessments have found noticeable differences between the two (meta-analyses have found mean correlations of 0.3 between AI-inferred personality and personality from surveys). One important but unanswered question is how users perceive their personality derived from both methods. Which do users perceive to be more accurate, and more satisfying to use? To answer this question, we used both methods to conduct personality assessments of 595 participants and then asked users how well the two sets of results fit them, as well as their satisfaction and intention to use them. Participants reported that both results fit them equally well, even though the two methods reported different personality scores. Users were equally satisfied with both methods but were more likely to use the survey, likely because it took less time. Our findings imply that both methods measure different aspects of user personality, and both may be useful. We discuss the pros and cons of AI-inferred versus traditional, self-reported personality and indicate future research directions of AI-inferred personality assessment and the implications of their use for real-world applications.

The purpose of this paper is to explain how perception of AI- user interaction (PAIUI) impacts organizational attractiveness among external AI users. Moreover, this paper tests the mediating variables that explain this relation among them. The samples include both customers and job applicants who have interacted previously with AI tools. A questionnaire was Developed, tested, and distributed among the AI users. The results of 194 valid questionnaires revealed that perception of AI directly impacts organizational attractiveness only for job applicants. Moreover, it seems from the findings that both fairness and anxiety explain the mediating impacts between perceptions of AI and attractiveness. On the other hand, the results showed that perception of AI doesn't directly impact corporate attractiveness among customers. However, it can impact communication quality dimensions among them. This paper is considered one of the first papers that investigate the impacts of AI among different stakeholders. Moreover, it is one of the limited papers that explain AI-attractiveness relationship.

Applications of artificial intelligence (AI) to material design have attracted increasing attention in recent years. Although AI-aided material design holds great promise for some applications, whether it has surpassed human creativity remains uncertain. The aim of the current study was to compare the divergent exploration capacity of AI with that of humans on a material design task. Human participants were asked to find a high-performance lubricant molecule under searching conditions comparable to a state-of-the-art AI system. Results indicated that, on average, AI was able to find significantly better lubricant molecules. However, the best molecule found by AI fell short of the best molecule found by a human participant. Furthermore, the structural characteristics of the molecules found by AI and human participants differed significantly. These findings suggest that a state-of-the-art AI system is capable of surpassing human divergent exploration capacity in material design, as in other fields in which AI has advanced. Nevertheless, our results also demonstrate that human intelligence and AI can play complementary roles in covering a broader search space. This investigation opens up new possibilities for collaborative systems involving both AI agents and humans in material design.

The surging global demand for mental healthcare (MH) services has amplified the interest in utilizing AI-assisted technologies in critical MH components, including assessment and triage. However, while reducing practitioner burden through decision support is a priority in MH-AI integration, the impact of AI systems on practitioner decisions remains under-researched. This study is the first to investigate the interplay between practitioner judgments and AI recommendations in MH diagnostic decision-making. Using a between-subjects vignette design, the study deployed a mock AI system to provide information about patient triage and assessments to a sample of MH professionals and psychology students with a strong understanding of assessments and triage procedures. Findings showed that participants were more inclined to trust and accept AI recommendations when they aligned with their initial diagnoses and professional intuition. Moreover, those claiming higher expertise demonstrated increased skepticism when AI's suggestions deviated from their professional judgment. The study underscores that MH practitioners neither show unwavering trust in, nor complete adherence to AI, but rather exhibit confirmation bias, predominantly favoring suggestions mirroring their pre-existing beliefs. These insights suggest that while practitioners can potentially correct faulty AI recommendations, the utility of implementing debiased AI to counteract practitioner biases warrants additional investigation.

Avatar marketing is an emerging topic on the agenda of both scholars and practitioners. However, the literature on this topic is fragmented, lacking clear conceptualizations and a solid foundation. To fill this gap, this paper delineates the field, the conceptual boundaries, setting the theoretical underpinnings for proposing a unifying concept of avatar marketing. Drawing on the TCCM framework and 5W1H approach, a systematic literature review was carried out between 2005 and 2022 within peer-reviewed journals. This paper traces the historical development of the theme, both theoretically and methodologically, uncovers the key agents involved in the avatar marketing strategy, and identifies the reasons for studying and utilizing avatar marketing. For theory, this study contributes by developing an integrated conceptual framework, a conceptual spectrum of avatar marketing conceptualizations, and providing a unifying concept for avatar marketing. Practical implications and future research directions are also established, helping to set guidelines for the advancement of the area.

This paper examines the recent emergence of AI-powered chatbots such as ChatGPT through the lens of the Cognitive Mediation Networks Theory (CMNT), deducing that the introduction of this radically new technology will likely create a new stage of collective cognitive functioning, called “Sophotechnic Mediation”, with characteristics that can be extrapolated from the way these new tools work and the dynamics of the sociocultural structures being created around them. From that description, the Sophotechnic Mediation Scale is proposed as a means to assess the extent of an individual's internalization of the new form of thinking. A preliminary empirical investigation with 132 higher education professors and students found the instrument to be statistically consistent, yielding a unidimensional and Gaussian score that behaves as a developmental trait emerging from the interaction with generative AIs, mediated by age and mastery of previous digital technologies and their cultural elements. It is concluded that the results are suggestive of the validity of the new scale and warrant further research.

Gray and Wegner (2012) proposed that when robots look human, their appearance prompts attributions of experience, including sensations and feelings, which is uncanny. This theory, confusingly termed mind perception, differs from perceptual theories of the uncanny valley in that the robots' eeriness is not stimulus-driven. To explore this seminal theory, we conducted a meta-regression analysis of 10 experiments and a (de)humanization experiment. In the first part, experiments were identified in the literature that manipulated artificial entity's experience using descriptions. However, experiments with no observable stimuli yielded larger effects for experience and eeriness than those with robots and virtual reality characters. This finding undermines a theory that purports to explain how a robot's human likeness causes eeriness. Further, a second issue concerns Gray and Wegner's protocol based on a vignette design. Reading about an entity with experience activates thoughts that may not be activated when encountering it, and these thoughts may increase its eeriness. Therefore, the paper's second part focuses on an experiment we conducted with a novel humanization–dehumanization protocol. Participants' attitudes on robots' similarity to humans were gradually shifted to manipulate robots' perceived humanness, experience, and agency. However, the manipulation's effect on eeriness and coldness was mostly nonsignificant or counter to prediction. Differences in the robots' physical appearance had a much larger effect on their eeriness and coldness. In fact, as a mediator, experience mitigated the stimulus's overall effect of increasing eeriness. These results favor perceptual theories, rather than mind perception, in explaining the uncanny valley.

Many Human-Robot Interaction (HRI) researchers are exploring the use of healthcare robots. Due to the sensitive nature of care, privacy concerns play a significant role in determining robot utility and adoption. While HRI research has explored some dimensions of privacy for robots in general, to our knowledge, no prior work has empirically studied how human-like robot design affects people's privacy and utility perceptions of robots across different healthcare contexts and tasks. We conducted a 3 × 3 × 3 study (n = 239) to understand these relationships, varying robot Human Likeness (HL) (low, medium, and high) and scenario/task type (hospital waiting room/robot check-in support, hospital patient room/robot mobility support, home care/robot neurorehabilitation support) via a mixed between-within subjects design. To our knowledge, this is one of the first studies that operationalizes complex constructs of privacy, healthcare, and HL across multiple realistic healthcare contexts, with a high degree of cognitive fidelity. Our results suggest the tasks and contexts in which privacy is considered in healthcare contexts with robots is more impactful than other factors like robot HL appearance. In particular, some settings include more complex tradeoffs between privacy and utility for robots than others. For example, HRI researchers and practitioners who want to build healthcare robots intended for the home may encounter the greatest challenges for balancing privacy risks. Finally, for the community, we demonstrate that design fiction animations can be a useful way to facilitate cognitive fidelity for supporting studies in HRI and serving as a bridge between narrative methods and the use of real-world robots.

Background

Staying active has been one of the mainstays in promoting health in older adults. Despite the potential of innovative health technologies for fitness, there is limited understanding of the factors influencing older adults’ behavioral intention to engage in mixed reality physical exercise.

Purpose

To develop a theoretical model of technology usability and acceptance influencing behavioral intention to use a mixed reality physical exercise program among older adults.

Design

A cross-sectional study was conducted to develop a virtual exercise coach in a mixed-reality environment. Older adults (n = 64) were purposively selected from a community senior center in the Philippines and participated in the exercise program. Their responses were analyzed using a partial least square approach to structural equation modeling.

Results

The generated measurement model demonstrated valid and reliable estimates. Its structural model (R² = 0.64) had a good fit (GoF = 0.56) with consistent quality indices. It also indicated that performance expectancy, facilitating conditions, and information quality are significant (p < 0.05) factors explaining behavioral intention to use the virtual coach system among older adults.

Conclusion

The belief in technology usefulness, trust, data quality, and the value of shared responsibility between the technology promoters and users significantly facilitates behavioral intention to use mixed reality virtual coach systems. The study results are valuable in developing technologies and improving older adult participation in technology-driven community health programs.

The foot-in-the-door is a sequential request strategy (Freedman and Fraser, 1966): The probability of compliance with a larger request increases if a smaller request is submitted in advance. We conducted three studies in different experimental settings investigating the foot-in-the-door (FITD) effect in human-robot interaction (HRI): An online questionnaire, laboratory experiment and field study. We investigated the FITD effect when applied by a robot and varied anthropomorphic cues along two factors: (1) more or less facial features displayed in the robot face and (2) the robot either self-disclosed ‘personal’ information or not. The experimental setting significantly influenced compliance with the robot's requests, with more items answered in the laboratory experiment compared to the online questionnaire and field study conditions. There was no difference between the latter two. There were no significant effects of facial features, self-disclosure, or the use of the FITD technique, neither on compliance with the large request nor on robot perception. The participants' trusting disposition significantly affected how they perceived the robot. These results highlight the complexity of HRI and the need to carefully consider the experimental setting when conducting and comparing studies on the FITD effect in HRI.