Human-like Characteristics Research Articles

Playing with robots in a nursery: a sociomaterial focus on interaction and learning

In the field of educational robotics, it is important to understand the processes trough which child-robot interactions are established during play activities. In terms of socio-material characteristics, robots can vary widely, from more mechanical robots to more anthropomorphic ones. Research has shown that the degree of anthropomorphization of the robot has an impact on how children perceive and interact with the robot. The role of the socio-material characteristics is still poorly explore in the 18–36-month age group. The aim of the study was to investigate how the presence of two robots, which differed in their socio-material characteristic of anthropomorphization, shapes both the individual and group play activities of 25 children aged 18–36 months. The children were observed during free group play sessions in which they had access to two types of robots: Idol, with more human-like features, and Pixy, a more mechanical robot with minimal anthropomorphism. Observations made through video recordings were transcribed. Qualitative analysis was conducted, and six units of analysis of children’s interaction with robots were identified. The main finding from our study is that children as early as 18 months are sensitive to the socio-material characteristics of the robotic artefact, influencing the way they interact with the robot and with each other. Notably, children displayed more imitation behaviors and social interactions with Idol, the more anthropomorphic robot, while Pixy, the mechanical robot, was primarily explored for its mechanical features. From an educational point of view, we highlight the importance of the construction of the learning environment and the choice of materials to propose to the children in play; the robot could be used to reinforce symbolic play, imitation, and to support group interaction.

‘They’re creepy creatures with human-like features’: children’s experiences of visual hallucinations in Charles Bonnet syndrome—a qualitative study

ObjectiveCharles Bonnet syndrome (CBS) refers to the presence of visual hallucinations occurring secondary to visual impairment. The aim of this study was to understand the phenomenology of CBS in children...

Human-Robot Relations and Robotic Attachment in Kazuo Ishiguro’s <i>Klara and the Sun</i>

Kazuo Ishiguro, the Nobel-winning writer, explores human-robot relations in his 2021 science fiction novel, Klara and the Sun. This story is narrated from the perspective of Klara, an anthropomorphic social robot with humanlike characteristics such as observing, learning, empathising, and interacting. She changes her identity from an attractive object to a reliable attachment figure, forming close bonds with, and even becoming an extended self of, her owner. Thus, this story challenges the traditional human-robot relations away from the master-slave model and develops into an intimate and beneficial connection. Drawing on John Bowlby’s concepts of “safe haven” and “secure base” and David Levy’s new interpretations of attachment theory, this research explores why human characters seek robotic attachment instead of human companions and how the close attachment is revealed in human-robot relations. The findings of this study make more contributions to the human-robot relations in literature in a positive perspective.

Literary characters and GPT-4: from William Shakespeare to Elena Ferrante

Abstract We prompted GPT-4 (a large language model) to play the Dictator game, a classic behavioral economics experiment, as 148 literary fictional characters from the 17th century to the 21st century. There is a general and mainly monotonic decrease in selfish behavior over time in literary characters. Fifty per cent of the decisions of characters from the 17th century are selfish compared to just 19 per cent from the 21st century. Historical literary characters have a surprisingly strong net positive valence across 2,785 personality traits generated by GPT-4 (3.2× more positive than negative). However, valence varied significantly across centuries. Positive traits were 10× more common than negative in the 21st century, but just 1.8× more common in the 17th century. ‘Empathetic’, ‘fair’, and ‘selfless’, were the most overweight traits in the 20th century. Conversely, ‘manipulative’, ‘ambitious’, and ‘ruthless’ were the most overweight traits in the 17th century. Male characters were more selfish than female characters. The skew was highest in the 17th century, where selfish decisions for male and female were 62 and 20 per cent, respectively. This analysis also offers a quantifiable partial Turing test. The key human-like characteristics of the model are the zero price effect, lack of spitefulness, and altruism. However, the model does not have human sensitivity to relative ordinal position and has significantly lower price elasticity than humans.

Unveiling the potential of digital human avatars in modern marketing strategies

PurposeThis study aims to develop a theoretical framework that marketing practitioners and scholars can adopt to enhance their understanding of how firms can effectively deploy and use digital human avatars as part of their global digital marketing strategy. By doing so, we inform investors of ongoing digital transformations of marketing practices that will equip marketeers to provide scalable, tailored, reliable and relevant digital self-service interactions to users, consequently improving the user/customer experience.Design/methodology/approachThematic analysis was used to discover factors to enable the successful implementation of digital human avatars, drawing on in-depth interviews with fourteen executives of digital human avatars developer companies worldwide and analysis of ten podcasts and webinars with artificial intelligence (AI) experts.FindingsDigital human avatars revitalise the international dynamic marketing capabilities (IDMCs) of firms by integrating advanced technologies that transform user interactions, improve engagement and facilitate knowledge acquisition, dissemination and usage across various sectors and business units globally. This integration promotes a dynamic approach to international brands, customer relationships and marketing knowledge management capabilities, offering profound value to users and firms.Research limitations/implicationsOur first limitation is a lack of diversity in data sources. As digital human avatars are an emerging field, we had to limit our study to 14 experts in AI and 10 podcasts. While this method provides deep insights into the perspectives of those directly involved in the development and implementation of digital human avatars, it may not capture the views of end-users or consumers who interact with these avatars, which can be an avenue for further research. Our second limitation is the potential bias in the interpretation of our interview data and podcasts. This study’s approach to data analysis, where themes are derived from the data itself, carries a risk of subjective interpretation by the researchers. Future studies are encouraged to investigate the impact of digital human avatars across different organisational contexts and ecosystems, especially focusing on how these technologies are integrated and perceived in various international markets.Practical implicationsThe novel framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.Social implicationsThe adoption of digital human avatars can alleviate loneliest elderly and vulnerable people by being a companion. The human-like characteristics can impact sense of presence and attachment.Originality/valueThe novelty of our study lies in exploring the characteristics of technologies and practical factors that maximise the successful adoption of digital human avatars. We advance and contribute to the emerging theory of avatar marketing, IDMCs and absorptive capacity by demonstrating how digital human avatars could be adopted as part of a firm’s global digital marketing strategy. We focus specifically on six dimensions: outcomes and benefits, enhancements and capabilities, applications and domains, future implications, foundational elements and challenges and considerations. This framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

Examining the persuasiveness of text and voice agents: prosody aligned with information structure increases human-likeness, perceived personalisation and brand attitude

ABSTRACT To give product and brand recommendations, marketers make use of conversational agents which increasingly communicate via voice rather than text. Existing research comparing the persuasiveness of text and voice agents showed mixed results. The quality of the speech synthesis employed may strongly influence consumers’ responses. This study investigates to what extent a voice agent with pragmatically aligned prosody is more persuasive (i.e. yields a more positive brand attitude) than an agent with a standard voice or text, and whether perceived human-likeness and perceived personalisation provide an underlying mechanism to explain these differences. In an experiment (n = 212), participants interacted with a conversational agent that recommended a camera. Results showed that a voice agent using prosody aligned to the information state of the user is more persuasive than a text agent. This effect is mediated by perceived human-likeness and perceived personalisation. Hence, aligned prosody can make synthetic speech meet a certain quality threshold to be perceived as more human-like. Theoretically, this study helps to unravel why conversational agents with human-like features are more persuasive.

Dental evidence for extended growth in early Homo from Dmanisi

Human life history is characterized by an extended period of immaturity during which there is a disjunction between cerebral and somatic growth rates1. This mode of ontogeny is thought to be essential for the acquisition of advanced cognitive capabilities in a socially complex environment while the brain is still growing2. Key information about when and how this pattern evolved can be gleaned from the teeth of fossil hominins because dental development informs about the pace of life history3–5. Here we show that the first evolutionary steps towards an extended growth phase occurred in the genus Homo at least 1.77 million years ago, before any substantial increase in brain size. We used synchrotron phase-contrast tomography6 to track the microstructural development of the dentition of a subadult early Homo individual from Dmanisi, Georgia. The individual died at the age of 11.4 ± 0.6 years, shortly before reaching dental maturity. Tooth growth rates were high, similar to rates in living great apes. However, the Dmanisi individual showed a human-like delayed formation of the posterior relative to the anterior dentition, and a late growth spurt of the dentition as a whole. The unique combination of great-ape-like and human-like features of dental ontogeny suggests that early Homo had evolved an extended growth phase before a general slow-down in life history, possibly related to biocultural reproduction7 rather than brain growth.

Balancing Human Likeness in Social Robots: Impact on Children’s Lexical Alignment and Self-disclosure for Trust Assessment

While there is evidence that human-like characteristics in robots could benefit child-robot interaction in many ways, open questions remain about the appropriate degree of human likeness that should be implemented in robots to avoid adverse effects on acceptance and trust. This study investigates how human likeness, appearance and behavior, influence children’s social and competency trust in a robot. We first designed two versions of the Furhat robot with visual and auditory human-like and machine-like cues validated in two online studies. Secondly, we created verbal behaviors where human likeness was manipulated as responsiveness regarding the robot’s lexical matching. Then, 52 children (7–10 years old) played a storytelling game in a between-subjects experimental design. Results show that the conditions did not affect subjective trust measures. However, objective measures showed that human likeness affects trust differently. While low human-like appearance enhanced social trust, high human-like behavior improved children’s acceptance of the robot’s task-related suggestions. This work provides empirical evidence on manipulating facial features and behavior to control human likeness in a robot with a highly human-like morphology. We discuss the implications and importance of balancing human likeness in robot design and its impacts on task performance, as it directly impacts trust-building with children.

Comprehensive Preview Decision Method for Direction and Speed of Intelligent Vehicle Based on Rules and Learning

Decision-making is a key part of autonomous driving systems. Human-like decision-making in complex scenarios is important for enhancing drivers’ trust in and acceptance of autonomous driving systems. In order to improve their human-like features and their adaptability to complex scenarios, this paper proposes a comprehensive preview decision method for direction and speed based on rules and learning. First, a decision-making structure including maneuvering ability judgment, motion trajectory prediction, safety judgment, legitimacy judgment, comprehensive performance evaluation, and parameters learning is presented. Second, a learning method for the longitudinal safety distance threshold based on a BP neural network is introduced. Third, inverse reinforcement learning (IRL) is used to learn the comprehensive performance evaluation weight coefficient for different driving styles. Finally, the DJI AD4CHE dataset is processed and used to train the parameters. Car-following and lane-changing scenarios are simulated to verify the effectiveness of decision-making. The simulation results show that the proposed method can reflect human-like decision-making in multiple scenarios.

Hybrid lane change strategy of autonomous vehicles based on SOAR cognitive architecture and deep reinforcement learning

Research on lane change strategies for autonomous vehicles holds paramount importance in optimizing traffic flow efficiency, enhancing driving safety, and adapting to complex traffic environments. While numerous rule-based or machine-learning approaches have been explored to tackle the challenge of lane change on highways, they frequently exhibit limited performance owing to the complexity of driving environments. This study proposes a novel lane change strategy for autonomous vehicles, which utilizes a hybrid framework integrating the SOAR cognitive architecture and deep reinforcement learning (DRL) to address the lane change challenge on highways. First, we introduce a rule extraction algorithm, the RuleCOSI+, which is based on tree ensemble algorithms, designed to extract concise lane change rules from large-scale human driving data. These straightforward rules, together with traffic regulations and safety rules, constitute the long-term memory of the SOAR cognitive architecture, enabling transparent decision-making processes. Next, by analyzing the clipping mechanism of the proximal policy optimization (PPO) algorithm, we propose an Adaptive Clipping PPO (ACPPO) algorithm which is based on the importance of samples. This algorithm adopts different clipping strategies for SOAR samples and ACPPO samples during the training process, enabling the algorithm to more effectively utilize samples with different levels of importance. Then, we propose a hybrid decision-making algorithm: SOAR-ACPPO, which combines the SOAR cognitive architecture with the ACPPO algorithm. This algorithm leverages SOAR’s prior knowledge to effectively and safely guide agent learning. Finally, by selecting appropriate intervention probability and weaning strategy, the system avoids inappropriate knowledge intervention and ensures adequate environment exploration. Simulation experiments conducted using the CARLA simulator illustrate that the proposed strategy not only improves model learning efficiency but also enhances driving efficiency and safety. Additionally, it demonstrates a certain degree of human-like characteristics and interpretability.

Current Insights on Using Social Robots to Support Second Language (L2) International Students in Higher Education

New digital technologies such as social robots are embodied computers with human-like features and conversational capabilities that can socially interact with people. Social robots have been used in education as a learning tool to support second language learning. This essay discusses current research literature that has explored how social robots could be utilized to support second language (L2) international students studying at English-speaking universities. Insights into the potential application and limitations of using social robots to support L2 students outside their home countries to promote their social and academic well-being will also be discussed. Based on a synthesis of current and relevant research gathered from the literature, the affordances of using social robots for L2 students included learning L2 through human-robot interactions, enhanced motivation, and engagement in their learning environments. However, the limitations of the use of this technology included attitudes towards robot-assisted learning (e.g., unfamiliarity with learning with a social robot) and the novelty effect of social robots. Further research is needed to deepen our understanding of not only the role of social robots for supporting language learning, but also how they could aid L2 students in their successful transition to a foreign university, culture, and social context.

GPT-3.5 altruistic advice is sensitive to reciprocal concerns but not to strategic risk

Pre-trained large language models (LLMs) have garnered significant attention for their ability to generate human-like text and responses across various domains. This study delves into examines the social and strategic behavior of the commonly used LLM GPT-3.5 by investigating its suggestions in well-established behavioral economics paradigms. Specifically, we focus on social preferences, including altruism, reciprocity, and fairness, in the context of two classic economic games: the Dictator Game (DG) and the Ultimatum Game (UG). Our research aims to answer three overarching questions: (1) To what extent do GPT-3.5 suggestions reflect human social preferences? (2) How do socio-demographic features of the advisee and (3) technical parameters of the model influence the suggestions of GPT-3.5? We present detailed empirical evidence from extensive experiments with GPT-3.5, analyzing its responses to various game scenarios while manipulating the demographics of the advisee and the model temperature. Our findings reveal that, in the DG Dictator Game, model suggestions are more altruistic than in humans. We further show that it also picks up on more subtle aspects of human social preferences: fairness and reciprocity. This research contributes to the ongoing exploration of AI-driven systems’ alignment with human behavior and social norms, providing valuable insights into the behavior of pre-trained LLMs and their implications for human-AI interactions. Additionally, our study offers a methodological benchmark for future research examining human-like characteristics and behaviors in language models.

The combined effect of behavioural activation system and perceived anthropomorphism on individuals’ intention to use digital therapeutics tools for asthma and respiratory-related diseases

ABSTRACT The integration of Digital Therapeutics (DTx) is profoundly transforming patients’ care management. Original combinations of IoT medical devices and related mobile applications resulting in DTx, may boost patients’ engagement and adherence to treatment regimens, resulting as ‘intelligent’ assistants in patients’ health recovery. In this paper we conducted a study among a sample of 507 participants by setting a prototype of DTx able to monitor respiratory-related diseases as a setting. Results demonstrated that individuals’ perceived product intelligence of such digital tools led to higher intention to use, through the effect of their Behavioural Activation System, that is, the psychological mechanism influencing their motivation towards engaging in rewarding and goal-directed behaviours. Moreover, individuals’ perceived anthropomorphism of these tools, in terms of individuals’ tendency attribute human-like characteristics or emotions to non-human entities such in these technological tools, increase such effect.

Facial animacy in anthropomorphised designs: Insights from leveraging self-report and facial expression analysis for multimedia learning

Anthropomorphism is the act of attributing human-like characteristics to non-human objects and has played a key role in the field of emotional design in multimedia learning. Despite its significance, the relationship between animacy and anthropomorphism, particularly facial animacy, remains underexplored albeit its potential impact on learning engagement and emotional responses. Hence, this study aims to address this gap by examining the effects of facial animacy in anthropomorphised designs using a 3 × 2 design (none vs. static vs. animated) based on self-reported measure (SRM) and facial emotion recognition (FER) and how both measures are associated. The findings revealed discrepancies between both measures, with mostly moderate to weak correlations between hypothesised associations. SRM results indicated that face animacy decreased perceived boredom, while static and dynamic anthropomorphised designs increased curiosity. The FER results revealed notable similarities between designs without anthropomorphism and the static versions, highlighting that facial animacy led participants to express more joy and less neutral expressions. Additionally, neutral expressions were associated with lower enjoyment perception, while negative emotions, especially boredom, were linked to reduced attention.

Humanlike service robots: A systematic literature review and research agenda

AbstractHumanlike robots are increasingly employed to provide frontline services. They are frequently designed with stereotypically feminine or masculine humanlike features which affect or bias consumer behavior in service encounters. This systematic review of 118 peer‐reviewed journal papers aims to comprehensively capture the current status of the field and identify important research gaps requiring further investigation. Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses protocol, a comprehensive framework is developed to conceptualize the process of customer interactions with humanlike service robots, depicting how humanlike service robots influence consumer behavior. Specifically, we identify interaction antecedents, consumer processing factors, outcomes of the interactions, and strengthening/attenuating factors. Based on the framework, the review concludes by identifying issues that future research should seek to solve to contribute to the field. This paper provides a deep understanding of service robot anthropomorphism in marketing and consumer research and proposes a future research agenda to advance knowledge of the field.

Navigating the age of AI influence: A systematic literature review of trust, engagement, efficacy and ethical concerns of virtual influencers in social media

This systematic literature review (SLR) delves into the realm of Artificial Intelligence (AI)-powered virtual influencers (VIs) in social media, examining trust factors, engagement strategies, VI efficacy compared to human influencers, ethical considerations, and future trends. Analyzing 60 academic articles from 2012 to 2024, drawn from reputable databases, the study applies specific inclusion and exclusion criteria. Both automated and manual searches ensure a comprehensive review. Findings reveal a surge in VI research post-2012, primarily in journals, with quantitative methods prevailing. Geographically, research focuses on Europe, Asia Pacific, and North America, indicating gaps in representation from other regions. Key themes highlight trust and engagement’s critical role in VI marketing, navigating the balance between consistency and authenticity. Challenges persist regarding artificiality and accountability, managed through brand alignment and transparent communication. VIs offers advantages, including control and cost efficiencies, yet grapple with authenticity issues, addressed through human-like features. Ethically, VI emergence demands stringent guidelines and industry cooperation to safeguard consumer well-being. Looking ahead, VIs promises transformative storytelling, necessitating vigilance in ethical considerations. This study advocates for continued scholarly inquiry and industry reflection to navigate VI marketing evolution responsibly, shaping the future influencer marketing landscape.

How the Human-Like Characteristics of AI Assistants Affect Employee Creativity: A Social Network Ties Perspective

Generative artificial intelligence (AI) powered AI assistants provide opportunities to promote employee creativity. However, existing research ignores exploring the impact of AI assistants’ unique human-like characteristics on employee creativity, which means there are insufficient practical guidelines for implementing and designing AI assistants to promote employee creativity. Based on social network theory, this study constructs a theoretical model to investigate the relationship between the human-like characteristics of AI assistants and employee creativity and uses structural equation modeling to analyze questionnaire data from 243 employees. The results show that perceived intelligence and anthropomorphism of AI assistants positively influence employees-AI instrumental and expressive ties, both of which can enhance employee creativity. In particular, perceived intelligence has a stronger effect on the employee-AI instrumental ties, while perceived anthropomorphism has a stronger effect on the employee-AI expressive ties. In addition, the interaction intensity weakens the positive effects of perceived anthropomorphism on employee-AI instrumental and expressive ties. These findings provide practical guidance for implementing and designing AI assistants to promote employee creativity.

Anthropomorphizing Machines: Reality or Popular Myth?

According to a widespread view, people often anthropomorphize machines such as certain robots and computer and AI systems by erroneously attributing mental states to them. On this view, people almost irresistibly believe, even if only subconsciously, that machines with certain human-like features really have phenomenal or subjective experiences like sadness, happiness, desire, pain, joy, and distress, even though they lack such feelings. This paper questions this view by critiquing common arguments used to support it and by suggesting an alternative explanation. Even if people’s behavior and language regarding human-like machines suggests they believe those machines really have mental states, it is possible that they do not believe that at all. The paper also briefly discusses potential implications of regarding such anthropomorphism as a popular myth. The exercise illuminates the difficult concept of anthropomorphism, helping to clarify possible human relations with or toward machines that increasingly resemble humans and animals.

The Role of Generative AI in Shaping Millennials and Gen Z’s Orientation Toward Luxury Products

This study investigates the impact of Artificial Intelligence (AI)-generated promotional content and status motivations on the perception of luxury brands among Millennials and Generation Z. The author adopts a mixed-method research approach that integrates a quantitative survey and structural equation modeling analysis with qualitative Natural Language Processing (NLP). The findings reveal that generative AI promotional content impacts millennials and Gen Z’s purchase intention due to its perceived entertainment, transparency, and usefulness. Status motivations had a stronger impact on purchase intention, and demographic characteristics were significantly different in their results. The study is the first to examine the human characteristics of perceived expertise, transparency, and entertainment in an AI context in light of status consumption motivation. As AI is expected to have more human-like characteristics and behavior, testing theories centered on human influence in an AI context is crucial for marketing theory and practice.

Set up and validation of a method to analyse microplastics in stool and small intestine samples

The contamination of microplastics in humans is of increasing concern. Therefore, the aim of this study was to develop effective methods to determine the concentration and types of microplastics entering human digestive system. To study levels of MPs contamination in humans, an excellent indicator are stools. Indeed, stools, and thus the digestive system, can be an excellent indicator of the level of MPs contamination in humans. Hence, objective was to find effective methods to extract, quantify and characterize microplastics in stool and small intestine samples. The samples studied were human stools and pig jejunum (which has human-like characteristics). The methods were optimized by observing extraction efficiency, compatibility by Fourier-transform infrared spectroscopy (FTIR) characterization and non-deformation of the microplastics. The steps of the procedure were:• Sampling to avoid plastic contamination• Non-aggressive chemical and enzymatic digestion• Counting and characterizationThe methods were optimized and validated, observing recovery and repeatability. Therefore, two simple, effective methods with high analytical performance have been developed. The MPs present in the stool and intestine samples were counted by stereoscopic microscope and characterized by FTIR, finding several types of MPs such as synthetic cellulose, polyethylene, polypropylene, polystyrene, and polyethylene terephthalate, among others.