Lean back or lean in? Exploring social loafing in human-robot teams.

Introduction: Thanks to technological advances, robots are now being used for a wide range of tasks in the workplace. They are often introduced as team partners to assist workers. This teaming is typically associated with positive effects on work performance and outcomes. However, little is known about whether typical performance-reducing effects that occur in human teams also occur in human-robot teams. For example, it is not clear whether social loafing, defined as reduced individual effort on a task performed in a team compared to a task performed alone, can also occur in human-robot teams. Methods: We investigated this question in an experimental study in which participants worked on an industrial defect inspection task that required them to search for manufacturing defects on circuit boards. One group of participants worked on the task alone, while the other group worked with a robot team partner, receiving boards that had already been inspected by the robot. The robot was quite reliable and marked defects on the boards before handing them over to the human. However, it missed 5 defects. The dependent behavioural measures of interest were effort, operationalised as inspection time and area inspected on the board, and defect detection performance. In addition, subjects rated their subjective effort, performance, and perceived responsibility for the task. Results: Participants in both groups inspected almost the entire board surface, took their time searching, and rated their subjective effort as high. However, participants working in a team with the robot found on average 3.3 defects. People working alone found significantly more defects on these 5 occasions-an average of 4.2. Discussion: This suggests that participants may have searched the boards less attentively when working with a robot team partner. The participants in our study seemed to have maintained the motor effort to search the boards, but it appears that the search was carried out with less mental effort and less attention to the information being sampled. Changes in mental effort are much harder to measure, but need to be minimised to ensure good performance.

The potential of robot eyes as predictive cues in HRI-an eye-tracking study.

Robots currently provide only a limited amount of information about their future movements to human collaborators. In human interaction, communication through gaze can be helpful by intuitively directing attention to specific targets. Whether and how this mechanism could benefit the interaction with robots and how a design of predictive robot eyes in general should look like is not well understood. In a between-subjects design, four different types of eyes were therefore compared with regard to their attention directing potential: a pair of arrows, human eyes, and two anthropomorphic robot eye designs. For this purpose, 39 subjects performed a novel, screen-based gaze cueing task in the laboratory. Participants' attention was measured using manual responses and eye-tracking. Information on the perception of the tested cues was provided through additional subjective measures. All eye models were overall easy to read and were able to direct participants' attention. The anthropomorphic robot eyes were most efficient at shifting participants' attention which was revealed by faster manual and saccadic reaction times. In addition, a robot equipped with anthropomorphic eyes was perceived as being more competent. Abstract anthropomorphic robot eyes therefore seem to trigger a reflexive reallocation of attention. This points to a social and automatic processing of such artificial stimuli.

(Hu)man-Like Robots: The Impact of Anthropomorphism and Language on Perceived Robot Gender.

Implementing anthropomorphic features to robots is a frequently used approach to create positive perceptions in human-robot interaction. However, anthropomorphism does not always lead to positive consequences and might trigger a more gendered perception of robots. More precisely, anthropomorphic features of robots seem to evoke a male-robot bias. Yet, it is unclear if this bias is induced via a male appearance of higher anthropomorphic robots, a general male-technology bias, or even due to language aspects. As the word robot is differently grammatically gendered in different languages, this might be associated with the representation of robot gender. To target these open questions, we investigated how the degree of anthropomorphism and the way the word robot is gendered in different languages, as well as within one language influence the perceived gender of the robot. We therefore conducted two online-studies in which participants were presented with pictures of differently anthropomorphic robots. The first study investigated two different samples from which one was conducted in German, as grammatically-gendered language, and one in English as natural gender language. We did not find significant differences between both languages. Robots with a higher degree of anthropomorphism were perceived as significantly more male than neutral or female. The second study investigated the effect of grammatically-gendered descriptions (feminine, masculine, neuter) on the perception of robots. This study revealed that masculine grammatical gender tends to reinforce a male ascription of gender-neutral robots. The results suggest that the male-robot bias found in previous studies seems to be associated with appearance of most anthropomorphic robots, and the grammatical gender the robot is referenced by.

A New Experimental Paradigm to Manipulate Risk in Human-Automation Research.

OBJECTIVE: Two studies serve as a manipulation check of a new experimental multi-task paradigm that can be applied to human-automation research (Virtual Reality Testbed for Risk and Automation Studies; ViRTRAS), in which a subjectively experienceable risk can be manipulated as part of a virtual reality environment. BACKGROUND: Risk has been postulated as an important contextual factor affecting human-automation interaction. However, experimental evidence is scarce due to the difficulty operationalizing risk in an ethical way. In the new paradigm, risk is varied by the altitude at which participants carry out the task, including the possibility of virtually falling in case of a mistake. METHOD: Key components of the paradigm were used to investigate participants' risk perception in a low (0.5 m) and high altitude (70 m) using subjective self-reports and objective behavioral measures. RESULTS: In the high-altitude condition risk perception was significantly higher with medium to large effect sizes. In addition, results of the behavioral measures reveal that participants habituated with length of exposure. However, this habituation seems to occur similarly in both altitude conditions. CONCLUSION: The manipulation checks were successful. The new paradigm is a promising tool for automation research. It incorporates the contextual factor of risk and creates a situation which is more comparable to what real-life operators experience. Additionally, it meets the same requirements of other multi-task environments in human-automation research. APPLICATION: The new paradigm provides the basis to vary the contextual factor of risk in human-automation research, which has previously been either neglected or operationalized in an arguably inferior way.

Humans Can't Resist Robot Eyes - Reflexive Cueing With Pseudo-Social Stimuli.

Joint attention is a key mechanism for humans to coordinate their social behavior. Whether and how this mechanism can benefit the interaction with pseudo-social partners such as robots is not well understood. To investigate the potential use of robot eyes as pseudo-social cues that ease attentional shifts we conducted an online study using a modified spatial cueing paradigm. The cue was either a non-social (arrow), a pseudo-social (two versions of an abstract robot eye), or a social stimulus (photographed human eyes) that was presented either paired (e.g. two eyes) or single (e.g. one eye). The latter was varied to separate two assumed triggers of joint attention: the social nature of the stimulus, and the additional spatial information that is conveyed only by paired stimuli. Results support the assumption that pseudo-social stimuli, in our case abstract robot eyes, have the potential to facilitate human-robot interaction as they trigger reflexive cueing. To our surprise, actual social cues did not evoke reflexive shifts in attention. We suspect that the robot eyes elicited the desired effects because they were human-like enough while at the same time being much easier to perceive than human eyes, due to a design with strong contrasts and clean lines. Moreover, results indicate that for reflexive cueing it does not seem to make a difference if the stimulus is presented single or paired. This might be a first indicator that joint attention depends rather on the stimulus' social nature or familiarity than its spatial expressiveness. Overall, the study suggests that using paired abstract robot eyes might be a good design practice for fostering a positive perception of a robot and to facilitate joint attention as a precursor for coordinated behavior.

Absence of DOA Effect but No Proper Test of the Lumberjack Effect: A Reply to Jamieson and Skraaning (2019).

OBJECTIVE: The aim was to evaluate the relevance of the critique offered by Jamieson and Skraaning (2019) regarding the applicability of the lumberjack effect of human-automation interaction to complex real-world settings. BACKGROUND: The lumberjack effect, based upon a meta-analysis, identifies the consequences of a higher degree of automation-to improve performance and reduce workload-when automation functions as intended, but to degrade performance more, as mediated by a loss of situation awareness (SA) when automation fails. Jamieson and Skraaning provide data from a process control scenario that they assert contradicts the effect. APPROACH: We analyzed key aspects of their simulation, measures, and results which we argue limit the strength of their conclusion that the lumberjack effect is not applicable to complex real-world systems. RESULTS: Our analysis revealed limits in their inappropriate choice of automation, the lack of a routine performance measure, support for the lumberjack effect that was actually provided by subjective measures of the operators, an inappropriate assessment of SA, and a possible limitation of statistical power. CONCLUSION: We regard these limitations as reasons to temper the strong conclusions drawn by the authors, of no applicability of the lumberjack effect to complex environments. Their findings should be used as an impetus for conducting further research on human-automation interaction in these domains. APPLICATIONS: The collective findings of both Jamieson and Skraaning and our study are applicable to system designers and users in deciding upon the appropriate level of automation to deploy.

Human performance consequences of stages and levels of automation: an integrated meta-analysis.

OBJECTIVE: We investigated how automation-induced human performance consequences depended on the degree of automation (DOA). BACKGROUND: Function allocation between human and automation can be represented in terms of the stages and levels taxonomy proposed by Parasuraman, Sheridan, and Wickens. Higher DOAs are achieved both by later stages and higher levels within stages. METHOD: A meta-analysis based on data of 18 experiments examines the mediating effects of DOA on routine system performance, performance when the automation fails, workload, and situation awareness (SA). The effects of DOA on these measures are summarized by level of statistical significance. RESULTS: We found (a) a clear automation benefit for routine system performance with increasing DOA, (b) a similar but weaker pattern for workload when automation functioned properly, and (c) a negative impact of higher DOA on failure system performance and SA. Most interesting was the finding that negative consequences of automation seem to be most likely when DOA moved across a critical boundary, which was identified between automation supporting information analysis and automation supporting action selection. CONCLUSION: Results support the proposed cost-benefit trade-off with regard to DOA. It seems that routine performance and workload on one hand, and the potential loss of SA and manual skills on the other hand, directly trade off and that appropriate function allocation can serve only one of the two aspects. APPLICATION: Findings contribute to the body of research on adequate function allocation by providing an overall picture through quantitatively combining data from a variety of studies across varying domains.

Manual flying skills under the influence of performance shaping factors.

This paper describes an experimental study investigating pilots' manual flying skills. In today's line oriented flight training, basic flying skills are neglected frequently. So, the study examines the manual flying skills of commercial airline pilots under the influence of several performance shaping factors like training, practice or fatigue in a landing scenario. The landing phase shows a disproportionate high percentage of aircraft accidents and it is typically flown by hand. The study is to be undertaken with randomly selected pilots in a full motion flight simulator to ensure a high validity of the results.

Human performance consequences of automated decision aids in states of sleep loss.

OBJECTIVE: The authors investigated how human performance consequences of automated decision aids are affected by the degree of automation and the operator's functional state. BACKGROUND: As research has shown, decision aids may not only improve performance but also lead to new sorts of risks.Whereas knowledge exists about the impact of system characteristics (e.g., reliability) on human performance, little is known about how these performance consequences are moderated by the functional state of operators. METHOD: Participants performed a simulated supervisory process control task with one of two decision aids providing support for fault identification and management. One session took place during the day, and another one took place during the night after a prolonged waking phase of more than 20 hr. RESULTS: Results showed that decision aids can support humans effectively in maintaining high levels of performance, even in states of sleep loss, with more highly automated aids being more effective than less automated ones. Furthermore, participants suffering from sleep loss were found to be more careful in interaction with the aids, that is, less prone to effects of complacency and automation bias. However, cost effects arose that included a decline in secondary-task performance and an increased risk of return-to-manual performance decrements. CONCLUSION: Automation support can help protect performance after a period of extended wakefulness. In addition, operators suffering from sleep loss seem to compensate for their impaired functional state by reallocating resources and showing a more attentive behavior toward possible automation failures. APPLICATION: Results of this research can inform the design of automation, especially decision aids.