The reliability of assistance systems modulates the sense of control and acceptability of human operators.

Individuals are increasingly required to interact with complex and autonomous technologies, which often has a significant impact on the control they experience over their actions and choices. A better characterization of the factors responsible for modulating the control experience of human operators is therefore a major challenge to improve the quality of human-system interactions. Using a decision-making task performed in interaction with an automated system, we investigated the influence of two key properties of automated systems, their reliability and explicability, on participants' sense of agency (SoA), as well as the perceived acceptability of system choices. The results show an increase in SoA associated with the most explicable system. Importantly, the increase in system explicability influenced participants' ability to regulate the control resources they engaged in the current decision. In particular, we observed that participants' SoA varied with system reliability in the "explained" condition, whereas no variation was observed in the "non-explained" condition. Finally, we found that system reliability had a direct impact on system acceptability, such that the most reliable systems were also considered the most acceptable systems. These results highlight the importance of studying agency in human-computer interaction in order to define more acceptable automation technologies.

Investigating risk-taking and executive functioning as predictors of driving performances and habits: a large-scale population study with on-road evaluation.

Introduction: Maladaptive behavior often results from poor decision-making and by extension poor control over decisions. Since maladaptive behavior in driving, such as excessive speed, can lead to dramatic consequences, identifying its causes is of particular concern. The present study investigated how risk-taking and executive functioning are related to driving performance and habits among the general population. Method: Five hundred and eighty-nine participants completed an on-road driving session with a professional driving instructor and a self-reported checklist of difficult driving situations typically avoided. Additionally, participants completed a set of experimental tasks assessing risk-taking tendencies, reactive adaptive mechanisms, and two distinct forms of inhibition: interference control and response inhibition. Results: The results of the present study revealed several significant findings. Firstly, poor driving performance was associated with a high avoidance of challenging driving situations. Secondly, neither form of inhibition studied (interference control or response inhibition) predicted driving performance. Thirdly, while greater involvement in reactive adaptive mechanisms did not predict better on-road performance, it was associated with a reduced tendency to avoid difficult situations. Surprisingly, a higher propensity for risk-taking predicted better on-road performance. Discussion: Overall, these results underline limited links between executive functioning and driving performance while highlighting a potentially more complex relationship between risk-taking tendencies and driving. Executive functioning, however, appears to be linked to driving habits.

Imbalanced weighting of proactive and reactive control as a marker of risk-taking propensity.

According to the dual mechanisms of control (DMC), reactive and proactive control are involved in adjusting behaviors when maladapted to the environment. However, both contextual and inter-individual factors increase the weight of one control mechanism over the other, by influencing their cognitive costs. According to one of the DMC postulates, limited reactive control capacities should be counterbalanced by greater proactive control to ensure control efficiency. Moreover, as the flexible weighting between reactive and proactive control is key for adaptive behaviors, we expected that maladaptive behaviors, such as risk-taking, would be characterized by an absence of such counterbalance. However, to our knowledge, no studies have yet investigated this postulate. In the current study, we analyzed the performances of 176 participants on two reaction time tasks (Simon and Stop Signal tasks) and a risk-taking assessment (Balloon Analog Risk Taking, BART). The post-error slowing in the Simon task was used to reflect the spontaneous individuals' tendency to proactively adjust behaviors after an error. The Stop Signal Reaction Time was used to assess reactive inhibition capacities and the duration of the button press in the BART was used as an index of risk-taking propensity. Results showed that poorer reactive inhibition capacities predicted greater proactive adjustments after an error. Furthermore, the higher the risk-taking propensity, the less reactive inhibition capacities predicted proactive behavioral adjustments. The reported results suggest that higher risk-taking is associated with a smaller weighting of proactive control in response to limited reactive inhibition capacities. These findings highlight the importance of considering the imbalanced weighting of reactive and proactive control in the analysis of risk-taking, and in a broader sense, maladaptive behaviors.

Leveraging human agency to improve confidence and acceptability in human-machine interactions.

Repeated interactions with automated systems are known to affect how agents experience their own actions and choices. The present study explores the possibility of partially restoring sense of agency in operators interacting with automated systems by providing additional information about the system's decision, i.e. its confidence. To do so, we implemented an obstacle avoidance task with different levels of automation and explicability. Levels of automation were varied by implementing conditions in which the participant was free or not free to choose which direction to take, whereas levels of explicability were varied by providing or not providing the participant with the system's confidence in the direction to take. We first assessed how automation and explicability interacted with participants' sense of agency, and then tested whether increased self-agency was systematically associated with greater confidence in the decision and improved system acceptability. The results showed an overall positive effect of system assistance. Providing additional information about the system's decision (explicability effect) and reducing the cognitive load associated with the decision itself (automation effect) was associated with stronger sense of agency, greater confidence in the decision, and better performance. In addition to the positive effects of system assistance, acceptability scores revealed that participants perceived "explicable" systems more favorably. These results highlight the potential value of studying self-agency in human-machine interaction as a guideline for making automation technologies more acceptable and, ultimately, improving the usefulness of these technologies.