Recent advancements in AI have accelerated the evolution of versatile robot designs. Chess provides a standardized environment for evaluating the impact of robot behavior on human behavior. This article presents an open-source chess robot for human-robot interaction research, specifically focusing on verbal and non-verbal interactions. The OpenChessRobot recognizes chess pieces using computer vision, executes moves, and interacts with the human player through voice and robotic gestures. We detail the software design, provide quantitative evaluations of the efficacy of the robot, and offer a guide for its reproducibility. An online survey examining people’s views of the robot in three possible scenarios was conducted with 597 participants. The robot received the highest ratings in the robotics education and the chess coach scenarios, while the home entertainment scenario received the lowest scores. The code is accessible on GitHub: https://github.com/renchizhhhh/OpenChessRobot.

The use of situation awareness (SA) measures to assess relative safety in driving is common, with higher levels of SA being interpreted as safer. These relative interpretations do not allow researchers to determine whether the level of SA could be considered “safe” or “unsafe”. In contrast to such interpretations based on relative performance, the current position paper explores the potential for a normative interpretation of situation awareness with regard to safety assessment in driving. A series of expert interviews yielded viewpoints on the current relation between SA and safe driving, theoretical underpinnings for a normative approach, and potential actions towards an SA criterion for safe or unsafe driving. Methodological challenges regarding a normative approach are discussed together with considerations towards a weighted criterion-based approach to SA. The selection of SA requirements relevant for safety and the differentiation and weighting of these requirements on high and lower importance is presented. A method towards objective determination of relevance and weight of SA requirements may increase the usefulness of SA measures for assessment of safety in a driving context.

As automated vehicles (AVs) become increasingly popular, the question arises as to how cyclists will interact with such vehicles. This study investigated (1) whether cyclists spontaneously notice if a vehicle is driverless, (2) how well they perform a driver-detection task when explicitly instructed, and (3) how they carry out these tasks. Using a Wizard-of-Oz method, 37 participants cycled a designated route and encountered an AV multiple times in two experimental sessions. In Session 1, participants cycled the route uninstructed, while in Session 2, they were instructed to verbally report whether they detected the presence or absence of a driver. Additionally, we recorded participants’ gaze behaviour with eye-tracking and their responses in post-session interviews. The interviews revealed that 30% of the cyclists spontaneously mentioned the absence of a driver (Session 1), and when instructed (Session 2), they detected the absence and presence of the driver with 93% accuracy. The eye-tracking data showed that cyclists looked more frequently and for longer at the vehicle in Session 2 compared to Session 1. Additionally, participants exhibited intermittent sampling of the vehicle, and they looked at the area in front of the vehicle when it was far away and towards the windshield region when it was closer. The post-session interviews also indicated that participants were curious, but felt safe, and reported a need to receive information about the AV's driving state. In conclusion, cyclists can detect the absence of a driver in the AV, and this detection may influence their perception of safety. Further research is needed to explore these findings in real-world traffic conditions.

Despite extensive prevention, speeding remains a major contributor to traffic casualties. Understanding drivers’ perceived awareness and the subjective reasons for their speed behaviour could improve intervention strategies, and specifically inform the potential of speed feedback. A self-confrontation study was conducted in which 25 regular drivers recorded one of their drives using GoPro cameras, capturing both the road view and their speed, and selected video excerpts were later discussed with these participants. The study explored participants’ awareness and reasons for their speed behaviour, as well as general attitudes towards speeding, perceptions of its problematic nature, the acceptability of exceeding speed limits, and decision-making in speed choice. This study design aimed to provide an objective basis for the interviews and reduce recall biases. The results revealed that drivers show a latent awareness of their speeding behaviour, which they most often justified as usual, normal and safe. This general tolerance towards speeding suggests the normalisation of speed violations. As a result, individual safety interventions, such as feedback on driving behaviour, may not be effective. Prevention efforts should focus on changing norms, common beliefs and systemic factors regarding speeding.

To obtain a driver’s licence, one must successfully complete a practical driving test and a theory test. Although the theory test is widely regarded as an important element of driving competence, little is known about the predictors of theory test performance, and in particular the extent to which the acquired knowledge is retained over the years. All individuals who passed a car theory test in the Netherlands between November 2019 and October 2023 were invited to complete a questionnaire, which included a retention test (i.e., a representative retake test) consisting of 20 items not used before. The results based on 50,857 respondents revealed that those with a lower level of education exhibited lower performance on the retention test. Moreover, respondents who took a course with an instructor, an approach mostly used by those with a lower level of education, had a relatively high likelihood of passing the official car theory test on the first attempt. It was also found that the extent to which knowledge increased or decreased over the years was item-dependent, a pattern possibly explained by whether the test item measures functionally relevant driving experiences or if it primarily assesses isolated rules. The results of this study are relevant for training institutes and policymakers.

Neuroscience evidence suggests that personalized, task-specific, high-intensity training is essential for maximizing recovery after acquired brain injury. Robotic devices combined with immersive virtual reality (VR) games, visualized through head-mounted displays (HMDs), can support such intensive training within naturalistic virtual environments with audio-visual stimuli tailored to individual needs. However, the impact of these auditory and visual demands on cognitive load remains an open question. To address this, we conducted an experiment with 22 healthy participants to explore how varying levels of visual, auditory, and cognitive demands affect users’ cognitive load and performance during a shopping task in immersive VR. We found that mental demand had the most significant impact on increasing cognitive load and hampering task performance. Visual demands, although affecting gaze behavior, did not significantly affect cognitive load or performance. Auditory demands showed small effects on cognitive load.

This study describes the impact of ChatGPT use on the nature of work from the perspective of academics and educators. We elucidate six phenomena: (1) the cognitive workload associated with conducting Turing tests to determine if ChatGPT has been involved in work productions; (2) the ethical void and alienation that result from recondite ChatGPT use; (3) insights into the motives of individuals who fail to disclose their ChatGPT use, while, at the same time, the recipient does not reveal their awareness of that use; (4) the sense of ennui as the meanings of texts dissipate and no longer reveal the sender’s state of understanding; (5) a redefinition of utility, wherein certain texts show redundancy with patterns already embedded in the base model, while physical measurements and personal observations are considered as unique and novel; (6) a power dynamic between sender and recipient, inadvertently leaving non-participants as disadvantaged third parties. This paper makes clear that the introduction of AI tools into society has far-reaching effects, initially most prominent in text-related fields, such as academia. Whether these implementations represent beneficial innovations for human prosperity, or a rather different line of social evolution, represents the pith of our present discussion.

BACKGROUND: Head-mounted displays can be used to offer personalized immersive virtual reality (IVR) training for patients who have suffered an Acquired Brain Injury (ABI) by tailoring the complexity of visual and auditory stimuli to the patient's cognitive capabilities. However, it is still an open question how these virtual environments should be designed. METHODS: We used a human-centered design approach to help define the characteristics of suitable virtual training environments for ABI patients. We conducted (i) observations, (ii) interviews with eleven neurorehabilitation experts, and (iii) an online questionnaire with 24 neurorehabilitation experts to examine how therapists modify current training environments to promote patients' recovery in conventional sensorimotor neurorehabilitation settings. Finally, (iv) we involved eight neurorehabilitation experts in a participatory design workshop to co-create examples of IVR training environments. RESULTS: Five phases of the recovery process (Screening, Planning, Training, Reflecting, and Discharging) and six key themes describing the characteristics of suitable (physical) training environments (Specific, Meaningful, Versatile, Educational, Safe, and Supportive) were identified. The experts agreed that modulating the number of elements (e.g., objects, people) or distractions (e.g., background noise) in the physical training environment enables therapists to provide their patients with suitable conditions to execute functional tasks. Additionally, the experts highlighted the importance of developing IVR training environments that are meaningful and realistic. CONCLUSIONS: Through consultations with neurorehabilitation experts, we gained insights into how therapists adjust physical training environments to promote the execution of functional sensorimotor tasks in patients with diverse cognitive capabilities. Their recommendations on how to modulate and make IVR environments meaningful may contribute to increased motivation and skill transfer. Future studies on IVR-based neurorehabilitation should involve patients themselves.

Robots are becoming more capable and can autonomously perform tasks such as navigating between locations. However, human oversight remains crucial. This study compared two touchless methods for directing mobile robots: voice control and gesture control, to investigate the efficiency of these methods and the preference of users. We tested these methods in two conditions: one in which participants remained stationary and one in which they walked freely alongside the robot. We hypothesized that walking alongside the robot would result in higher intuitiveness ratings and improved task performance, based on the idea that walking promotes spatial alignment and reduces the effort required for mental rotation. In a 2×2 within-subject design, 218 participants guided the quadruped robot Spot along a circuitous route with multiple 90° turns using rotate left, rotate right, and walk forward commands. After each trial, participants rated the intuitiveness of the command mapping, while post-experiment interviews were used to gather the participants’ preferences. Results showed that voice control combined with walking with Spot was the most favored and intuitive, whereas gesture control while standing caused confusion for left/right commands. Nevertheless, 29% of participants preferred gesture control, citing increased task engagement and visual congruence as reasons. An odometry-based analysis revealed that participants often followed behind Spot, particularly in the gesture control condition, when they were allowed to walk. In conclusion, voice control with walking produced the best outcomes. Improving physical ergonomics and adjusting gesture types could make gesture control more effective.

This driving simulator study, which focused on supporting drivers through feedback rather than automating the driving task, examined the effect of real-time feedback based on different stages of information processing on driving behaviour. The stages investigated included providing information alone, assessment of that information, and a decision based on that assessment, following Parasuraman, Sheridan, and Wickens’s (2000) model of information-processing automation. The acceptability and effectiveness of the different stages of feedback were assessed on two key driving behaviours: speed and distance from the vehicle ahead. The results indicated that feedback had a limited effect on driving behaviour. However, the stage of information processing in the feedback did affect a number of outcomes, with decision-oriented feedback leading to improved behaviours but less favourable attitudinal results. Future safety interventions should consider altering risk perception and beliefs, or providing external motivation for behavioural change.

Many of the commentators use the opportunity to highlight the value of Ergonomics and Human Factors (EHF) and challenge the notion that the discipline is fading. In response, we argue that EHF science is lagging behind the rapid developments in AI, remains entrenched in past-century achievements, and is in decline. Indices such as membership counts, conference attendance numbers, and new regulations reflect activity, but not necessarily impact. Important questions about human-AI collaboration are being addressed by other disciplines, often without the involvement of EHF. Rather than advocating for systemic frameworks, we advocate for new skill development and the adoption of data-driven AI methods. To illustrate the potential of AI within EHF, we demonstrate how a vision-language model can replicate findings from a classic knobs-and-dials study. In conclusion, we acknowledge EHF’s knowledge base but foresee existential risks unless the field undergoes major reforms to remain relevant in an AI-dominated future.

Attention bias towards social threat has been linked to loneliness and anxiety, though findings are mixed and concerns about measurement reliability persist. This study examined whether state and trait loneliness, along with personality, self-esteem, social anxiety, and life satisfaction, are associated with attention bias towards social threat images (indicating rejection or exclusion) in young adults (N = 241). AI-generated images were used to enhance control over stimulus content and category distinctions. Participants completed an eye-tracking free-viewing task comprising 40 image matrices (four images per matrix, displayed for 6000 ms). We then computed attention bias (dwell time percentage, total fixation duration percentage, and fixation count percentage) and initial orientation of attention (first fixation percentage). The attention bias measures showed adequate-to-good internal consistency (α = 0.61–0.86). No significant associations emerged between loneliness and attention to socially threatening stimuli, suggesting that heightened vigilance to social threat may not be a feature of loneliness in non-clinical young adults. However, it was found that females exhibited greater attention to social positive images, and baseline pupil diameter was associated with social anxiety. Future research should assess whether loneliness-specific attention bias is a replicable phenomenon, ideally by using an extreme-sampling approach with very lonely individuals.

The advent of self-driving cars has sparked discussions about eye contact in traffic, particularly due to challenges that automated vehicles face in non-verbal communication with human road users. In his 1992 book, Turn Signals Are The Facial Expressions Of Automobiles, Don Norman describes how drivers in Mexico City deliberately avoid eye contact when entering a roundabout to create uncertainty in the minds of other drivers, leading the latter to yield right of way. Norman argued that such manipulative or aggressive behavior would not be tolerated in the United States. In the present study, we tested these claims through an online survey involving 3,857 respondents from 20 countries. The results confirmed that Mexican drivers reported a higher frequency of non-speeding ‘aggressive’ violations compared to those from most other countries. Regarding eye contact in the roundabout scenario presented in the survey, national differences were found not so much in the frequency of eye contact but in the reasons behind its use. Mexican drivers tended to avoid eye contact to reduce tension or avoid conflict with other drivers. However, they also frequently reported making eye contact to assert or subtly enforce their right of way. In higher-income countries like the United States, driver-driver eye contact is often deemed unnecessary. In conclusion, our findings partially correspond with Norman's anecdote based on his experiences in 1950s Mexico City. These results may have implications for understanding the stability of traffic cultures and the challenges related to eye contact and non-verbal communication faced by developers of automated vehicles.

The processes underlying human cognition are often divided into System 1, which involves fast, intuitive thinking, and System 2, which involves slow, deliberate reasoning. Previously, large language models were criticized for lacking the deeper, more analytical capabilities of System 2. In September 2024, OpenAI introduced the o1 model series, designed to handle System 2-like reasoning. While OpenAI’s benchmarks are promising, independent validation is still needed. In this study, we tested the o1-preview model twice on the Dutch ‘Mathematics B’ final exam. It scored a near-perfect 76 and 74 out of 76 points. For context, only 24 out of 16,414 students in the Netherlands achieved a perfect score. By comparison, the GPT-4o model scored 66 and 62 out of 76, well above the Dutch students’ average of 40.63 points. Neither model had access to the exam figures. Since there was a risk of model contamination (i.e., the knowledge cutoff for o1-preview and GPT-4o was after the exam was published online), we repeated the procedure with a new Mathematics B exam that was published after the cutoff date. The results again indicated that o1-preview performed strongly (97.8th percentile), which suggests that contamination was not a factor. We also show that there is some variability in the output of o1-preview, which means that sometimes there is ‘luck’ (the answer is correct) or ‘bad luck’ (the output has diverged into something that is incorrect). We demonstrate that the self-consistency approach, where repeated prompts are given and the most common answer is selected, is a useful strategy for identifying the correct answer. It is concluded that while OpenAI’s new model series holds great potential, certain risks must be considered.

There is a need to improve the validity of the driving test as a measure of an individual’s ability to drive safely. This paper explores the use of algorithms to analyze acceleration and GPS data from a smartphone and a GoPro to distinguish between different driving styles, as performed by experienced examiners portraying stereotypical driving test candidates. Measures from nine driving tests were analyzed, including (harsh) accelerations, jerk, mean speed, and speeding. Results showed that the type of car, instructed driving style, and driving route impacted the dependent measures. It is concluded that GPS and accelerometer data can effectively distinguish between cautious, normal, and aggressive driving. However, it is important to consider additional sensors, such as cameras, to allow for more context-aware assessments of driving behavior. Furthermore, we demonstrate methods to quantify variations in road conditions and provide suggestions for presenting the data to driving examiners.

As automated vehicles require human drivers to resume control in critical situations, predicting driver takeover behaviour could be beneficial for safe transitions of control. While previous research has explored predicting takeover behaviour in relation to driver state and traits, little work has examined the predictive value of manual driving style. We hypothesised that drivers’ behaviour during manual driving is predictive of their takeover behaviour when resuming control from an automated vehicle. We assessed 38 drivers with varying experience in a high-fidelity driving simulator. After completing manual driving sessions to assess their driving style, participants performed an automated driving task, typically on a subsequent date. Measures of driving style from manual driving sessions, including headway and lane change speed, were found to be predictive of takeover behaviour. The level of driving experience was associated with the behavioural measures, but correlations between measures of manual driving style and takeover behaviour remained after controlling for driver experience. Our findings demonstrate that how drivers reclaim control from their automated vehicle is not an isolated phenomenon but is associated with manual driving behaviour and driving experience. Strategies to improve takeover safety and comfort could be based on driving style measures, for example by the automated vehicle adapting its behaviour to match a driver's driving style.

Personality research has traditionally relied on questionnaires, which bring with them inherent limitations, such as response style bias. With the emergence of large language models such as ChatGPT, the question arises as to what extent these models can be used in personality research. In this study, ChatGPT (GPT-4) generated 2000 text-based personas. Next, for each persona, ChatGPT completed a short form of the Big Five Inventory (BFI-10), the Brief Sensation Seeking Scale (BSSS), and a Short Dark Triad (SD3). The mean scores on the BFI-10 items were found to correlate strongly with means from previously published research, and principal component analysis revealed a clear five-component structure. Certain relationships between traits, such as a negative correlation between the age of the persona and the BSSS score, were clearly interpretable, while some other correlations diverged from the literature. An additional analysis using four new sets of 2000 personas each, including a set of ‘realistic’ personas and a set of cinematic personas, showed that the correlation matrix among personality constructs was affected by the persona set. It is concluded that evaluating questionnaires and research hypotheses prior to engaging with real individuals holds promise.

In this commentary, we argue that the field of Ergonomics and Human Factors (EHF) has the tendency to present itself as a thriving and impactful science, while in reality, it is losing credibility. We assert that EHF science (1) has introduced terminology that is internally inconsistent and hardly predictive-valid, (2) has virtually no impact on industrial practice, which operates within frameworks of regulatory compliance and profit generation, (3) repeatedly employs the same approach of conducting lab experiments within unrealistic paradigms in order to complete deliverables, (4) suggests it is a cumulative science, but is neither a leader nor even an adopter of open-science initiatives that are characteristic of scientific progress and (5) is being assimilated by other disciplines as well as Big Tech. Recommendations are provided to reverse this trend, although we also express a certain resignation as our scientific discipline loses significance. Practitioner Summary: This paper offers criticism of the field of Ergonomics. There are issues such as unclear terminology, unrealistic experiments, insufficient impact and lack of open data. We provide recommendations to reverse the trend. This article concerns a critique of EHF as a science, and is not a critique of EHF practitioners.

With the growing capabilities of AI, technology is increasingly able to match or even surpass human performance. In the current study, focused on the game of chess, we investigated whether chess players could distinguish if they were playing against a human or a computer, and how they achieved this. A total of 24 chess players each played eight 5+0 Blitz games from different starting positions. They played against (1) a human, (2) Maia, a neural network-based chess engine trained to play in a human-like manner, (3) Stockfish 16, the best chess engine available, downgraded to play at a lower level, and (4) Stockfish 16 at its maximal level. The opponent’s move time was fixed at 10 seconds. During the game, participants verbalized their thoughts, and after each game, they indicated by means of a questionnaire whether they thought they had played against a human or a machine and if there were particular moves that revealed the nature of the opponent. The results showed that Stockfish at the highest level was usually correctly identified as an engine, while Maia was often incorrectly identified as a human. The moves of the downgraded Stockfish were relatively often labeled as ‘strange’ by the participants. In conclusion, the Turing test, as applied here in a domain where computers can perform superhumanly, is essentially a test of whether the chess computer can devise suboptimal moves that correspond to human moves, and not necessarily a test of computer intelligence.

Introduction: Communication from automated vehicles (AVs) to pedestrians using augmented reality (AR) could positively contribute to traffic safety. However, previous AR research for pedestrians was mainly conducted through online questionnaires or experiments in virtual environments instead of real ones. Methods: In this study, 28 participants conducted trials outdoors with an approaching AV and were supported by four different AR interfaces. The AR experience was created by having participants wear a Varjo XR-3 headset with see-through functionality, with the AV and AR elements virtually overlaid onto the real environment. The AR interfaces were vehicle-locked (Planes on vehicle), world-locked (Fixed pedestrian lights, Virtual fence), or head-locked (Pedestrian lights HUD). Participants had to hold down a button when they felt it was safe to cross, and their opinions were obtained through rating scales, interviews, and a questionnaire. Results: The results showed that participants had a subjective preference for AR interfaces over no AR interface. Furthermore, the Pedestrian lights HUD was more effective than no AR interface in a statistically significant manner, as it led to participants more frequently keeping the button pressed. The Fixed pedestrian lights scored lower than the other interfaces, presumably due to low saliency and the fact that participants had to visually identify both this AR interface and the AV. Discussion: In conclusion, while users favour AR in AV-pedestrian interactions over no AR, its effectiveness depends on design factors like location, visibility, and visual attention demands. In conclusion, this work provides important insights into the use of AR outdoors. The findings illustrate that, in these circumstances, a clear and easily interpretable AR interface is of key importance.

Marie Skłodowska-Curie Actions; Innovative Training Networks (ITN); SHAPE-IT; Grant number 860410

Publication date: 30 January 2024

DOI: 10.3389/frobt.2024.1324060