Human-like Characteristics Research Articles

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.

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 textand 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 socialpreferences, 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-3.5suggestions reflect human social preferences? (2) How do socio-demographic features of the advisee and (3) technicalparameters of the model influence the suggestions of GPT-3.5? We present detailed empirical evidence from extensiveexperiments with GPT-3.5, analyzing its responses to various game scenarios while manipulating the demographics of theadvisee 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. Thisresearch 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 andbehaviors 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.

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.

Do AI-powered digital assistants influence customer emotions, engagement and loyalty? An empirical investigation

PurposeDigital voice assistants (DVAs) are revolutionising consumers’ interactions with technology and businesses. Whilst research on the adoption of these devices is rapidly expanding, few have explored post-adoption behaviour. To fill this gap, we investigate how functionality and human-like features shape customers’ emotions, engagement and loyalty towards DVAs.Design/methodology/approachThe data were collected through a self-administered online survey from 509 DVA users. Structural equation modelling was employed for data analysis.FindingsThe results reveal that distinct human-like and functional factors of DVA independently explain customers’ positive emotions and engagement with DVAs. Positive emotions and engagement significantly impact customer loyalty to DVAs. The study shows that localisation of DVAs has a significant positive moderating influence on the service experience-customer engagement relationship but a negative moderating influence on the anthropomorphism-customer engagement relationship.Originality/valueUnlike previous research, this study contributes to the literature by delving into post-adoption phenomena. It explains how DVAs’ human-like and functional attributes drive customers’ positive emotional responses, engagement and loyalty towards DVAs. The findings not only unveil new insights into the moderating role of localisation but also provide a crucial understanding regarding the boundary conditions of the influence of anthropomorphism and service experience on customer engagement.

Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.

기술수용모델(TAM)을 적용한 호텔 로봇 서비스의 의인화와 사용의도에 관한 연구

This study focuses on the introduction of anthropomorphized robot services that exhibit human-like characteristics in Human-Robot Interaction (HRI). Furthermore, considering that studies on customer behavior following the adoption of new technologies mainly utilize the Technology Acceptance Model (TAM), this research verifies the influence relationship on future intention to continue usage based on TAM. Through empirical analysis using SPSS 27.0 and AMOS 27.0 programs to conduct path analysis via structural equation modeling, the following results were obtained: First, anthropomorphism of hotel service robots showed significant influence on perceived usefulness and perceived ease of use of technology acceptance. Second, based on technology acceptance, hotel customers commonly verified significant influence on intention to continue usage. Third, it was confirmed that there is a partial mediation effect on intention to continue usage depending on technology acceptance and anthropomorphism of hotel service robots. Through the results, it was confirmed that there is a need to enhance the level of similarity between humans and robots, which is an anthropomorphic element of robots, in order to increase customers' HRI experience level in service environments. Accordingly, directions and strategies for continuous adoption and utilization of robot services were suggested.

Managing social-educational robotics for students with autism spectrum disorder through business model canvas and customer discovery.

Social-educational robotics, such as NAO humanoid robots with social, anthropomorphic, humanlike features, are tools for learning, education, and addressing developmental disorders (e.g., autism spectrum disorder or ASD) through social and collaborative robotic interactions and interventions. There are significant gaps at the intersection of social robotics and autism research dealing with how robotic technology helps ASD individuals with their social, emotional, and communication needs, and supports teachers who engage with ASD students. This research aims to (a) obtain new scientific knowledge on social-educational robotics by exploring the usage of social robots (especially humanoids) and robotic interventions with ASD students at high schools through an ASD student-teacher co-working with social robot-social robotic interactions triad framework; (b) utilize Business Model Canvas (BMC) methodology for robot design and curriculum development targeted at ASD students; and (c) connect interdisciplinary areas of consumer behavior research, social robotics, and human-robot interaction using customer discovery interviews for bridging the gap between academic research on social robotics on the one hand, and industry development and customers on the other. The customer discovery process in this research results in eight core research propositions delineating the contexts that enable a higher quality learning environment corresponding with ASD students' learning requirements through the use of social robots and preparing them for future learning and workforce environments.

Children's anthropomorphism of inanimate agents.

This review article examines the extant literature on animism and anthropomorphism in infants and young children. A substantial body of work indicates that both infants and young children have a broad concept of what constitutes a sentient agent and react to inanimate objects as they do to people in the same context. The literature has also revealed a developmental pattern in which anthropomorphism decreases with age, but social robots appear to be an exception to this pattern. Additionally, the review shows that children attribute psychological properties to social robots less so than people but still anthropomorphize them. Importantly, some research suggests that anthropomorphism of social robots is dependent upon their morphology and human-like behaviors. The extent to which children anthropomorphize robots is dependent on their exposure to them and the presence of human-like features. Based on the existing literature, we conclude that in infancy, a large range of inanimate objects (e.g., boxes, geometric figures) that display animate motion patterns trigger the same behaviors observed in child-adult interactions, suggesting some implicit form of anthropomorphism. The review concludes that additional research is needed to understand what infants and children judge as social agents and how the perception of inanimate agents changes over the lifespan. As exposure to robots and virtual assistants increases, future research must focus on better understanding the full impact that regular interactions with such partners will have on children's anthropomorphizing. This article is categorized under: Psychology > Learning Cognitive Biology > Cognitive Development Computer Science and Robotics > Robotics.

Proximal Policy Optimization With Time-Varying Muscle Synergy for the Control of an Upper Limb Musculoskeletal System

Because of their unique adaptability, flexibility, and robustness, musculoskeletal robotic systems are regarded potentially as next-generation robots. However, motion learning and generation of such a robotic system are still challenging. This paper presents a neuromuscular control method, namely, TMS-PPO, based on time-varying muscle synergy (TMS) and proximal policy optimization (PPO). The electromyogram (EMG) activation signals of actual human motions are decomposed to obtain TMSs based on the temporal properties of the TMS. The weights of networks are trained to generate the scale and phase coefficients through the PPO. The coefficients modulate the TMSs to generate appropriate activation patterns to optimize motion learning of the musculoskeletal system. To verify the effectiveness of the proposed method, the TMSs are extracted from human upper limb muscle activation signals, and we compare TMS-PPO with PPO in the motion learning and generation process of an upper limb musculoskeletal system. The results show that TMS-PPO can complete the control tasks because the average errors of the joints are less than 0.05 rad. In the meantime, TMSs are used as motion primitives of the musculoskeletal system to simulate the process of the human CNS controlling muscles. It shows that TMS-PPO reduces the energy consumption and improves the learning rate significantly compared with the PPO. The learning episodes reduce from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">\(10^4\)</tex-math> </inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">\(10^3\)</tex-math> </inline-formula> , which indicates that TMS-PPO has a stronger learning ability and better physiological explanation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Due to the superiorities of the musculoskeletal system, humanoid robots that imitate human driven mechanisms are vigorously carried out worldwide. Taking advantages of human-like characteristics, the musculoskeletal robot provides new opportunities to understand and validate the human mechanisms of muscle control and motion learning, to compare the performance of the robot to that of humans as well as work in real world, e.g., human interactive robots, amusement robots and medical training robots in the future. However, strong redundancy, coupling, and nonlinearity of the system also raises many challenges for the investigation of the control problem. Inspired by how the human CNS controls a musculoskeletal system and realize motion generalization, a novel muscle-synergies-based neuromuscular control that combines time-varying muscle synergy (TMS) and Proximal Policy Optimization (PPO), namely, TMS-PPO is proposed in this paper. The learning efficiency of PPO and the physiological interpretation of the control process are improved during the motion learning and generation processes of the musculoskeletal system. Preliminary simulation experiments suggest that this method is feasible in terms of control accuracy and efficiency. Moreover, the performance of the TMS-PPO is comparable to the PPO without significant improvement. To solve this problem, in future work, we will introduce the cerebellar model into the control method which plays the role of adjusting and correcting the motions of the limbs to achieve accurate and stable control in the actions process of humans.

The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents

Given the interest in imbuing voice-based conversational agents with humanlike features, understanding how this affects user satisfaction is ultimately important for business performance. Mind perception theory explains how ascribing the mental capacity for agency and experience to artificial intelligence shapes subsequent user attitudes. Hence, we estimate the effect of mind perception on satisfaction in users with high/low innovativeness using data from text-based online reviews, which better reflect actual usage than traditional surveys. Methodologically, where numerous controls affect the cause and outcome variables in a model, traditional machine learning methods produce biased estimates. We overcome this by deploying Double/Debiased Machine Learning (combined with text analytics). Results show that user satisfaction is increased by two forms of perceived experience: directed at moral agents, or moral patients. Perceived agency, however, has no significant influence. The increase in satisfaction from both types of perceived experience is stronger among users with high (vs. low) innovativeness.

Optimization of inhaled anesthesia for Octodon degus using electroencephalography.

Physiological responses to inhaled anesthetics vary among species. Therefore, a precise anesthetic technique is important for each individual species. In this study, we focused on the degu (Octodon degus), a small herbivorous rodent. Degus have recently begun to be used as laboratory models for brain research because of certain human-like characteristics, such as spontaneous development of Alzheimer's disease. In this study, we evaluated appropriate induction and maintenance anesthesia conditions for isoflurane and sevoflurane in degus by a stimulation test, electroencephalography (EEG), minimum alveolar concentration (MAC), and vital signs. During induction, more rapid time to loss of the righting reflex and deeper anesthesia in degus were observed in isoflurane. The MAC value for degus were 1.75 ± 0.0% in isoflurane and 2.25 ± 0.27% in sevoflurane. Whereas some degus were awake during maintenance anesthesia using both anesthetics at concentrations of ≤2%, no rats were awake when using sevoflurane at a concentration of 2%. The duration of the total flat EEG, a measure of the depth of maintenance anesthesia, was longer for isoflurane than for sevoflurane. Furthermore, higher concentrations of both anesthetics suppressed the respiratory rate in degus. These new findings regarding inhalation anesthesia in degus will contribute to future developments in the fields of laboratory animals and veterinary medicine.