Program

Continuous and implicit measures of individuals’ attention would be useful for human computer interaction. Brain responses can tell us about individuals’ level and focus of attention, but it is not straightforward to retrieve this information in real life scenarios. In this talk, I will discuss research showing that the degree to which EEG signals vary in a similar way over time between individuals is associated with attentional engagement. Our findings that this also holds for other physiological signals (heart rate and skin conductance), under various real-life or life-like circumstances, and that it predicts subsequent behavior, make interpersonal physiological synchrony a promising marker of attention for applied settings as well as ecologically valid research.

Fighter pilots have to deal with environments with increasing complexity. Consequently, their mental demand increases because more information needs to be processed during time-critical situations. An imbalance in increasing task demands and limited available cognitive resources can lead to a drop in the pilot’s performance and raise the risk of human error. The Enhanced Pilot Interfaces & Interactions for fighter Cockpit (EPIIC) project funded by the European Defense Fund (EDF) aims to design the next-generation fighter cockpit, focussing on speeding up the pilot’s Observe, Orient, Decide and Act (OODA) loop. Part of this fighter cockpit design is the integration of a real-time mental workload monitoring system, aiding in pilots’ operational efficiency. As a contribution to the EPIIC project, Thales NL will develop a machine learning model for mental workload classification. The model will be integrated within Thales’ HumAn peRformance MonitOring and eNhancement sYstem (HARMONY). During our invited talk, we will elaborate on our data acquisition protocol, the challenges in the development of a mental workload machine learning model, and our motivation for using the HARMONY system.

Artificial intelligence (AI) applications are currently explored in multiple fields of organizational work, seeking to exploit efficiency potentials but also bringing considerable changes in work processes and roles. For instance, AI systems can be increasingly found in typical Human Resource Management fields, such as leadership or recruiting. However, a core precondition for successful application of AI-based technologies is that involved persons place sufficient trust in these technologies, for example, by following recommendations of the AI-system, by allowing AI-technologies to access personal data, or by deciding to implement an AI system in the first place. Extant models of trust in technology fall short of considering the black box nature of AI systems. Moreover, extant models focus on rational assessments and neglect the fact that different users approach technologies with different needs and expectations. A motivational perspective is important for a conclusive understanding of trust in AI, and can explain trust differences between stakeholder groups, such as managers, employees, or HR professionals. This presentation, based on a recent publication (Hertel, Fisher & Van Fossen, 2024), introduces a new integrative model of trust in AI that considers both cognitive and motivational processes. The model enables specific predictions for different stakeholder groups across different levels of analysis (organization, team, individual). Instead of direct linkages between system features and trust, we argue that the impact of system features depends on gains and losses different stakeholders expect as a consequence of AI usage. For example, managers and HR professionals will receive and seek different information about the reliability of an AI-based recruiting system than will job applicants, leading to different assessments of the AI system. In addition to suggesting avenues for future research, the integrative model provides practical implications for building and maintaining trust in AI systems for different stakeholder groups.

In this talk, the potentials of digital solutions for health in the current European scenario will be explored, together with the barriers to implementation perceived both from the healthcare professionals and other stakeholders involved.

In this view, the regulatory aspects relevant to software as medical device, as defined by the EU Medical Device Regulation (2017/745), and its post-market surveillance will be described, also including a view on ongoing parallel legislative initiatives, such as the AI Act and the European Health Data Space, and on path for reimbursement. Finally, an example of how ICT solutions for regulatory science could help involved stakeholders in improving transparency of medical device performances, and improve patient safety, will be presented.

Around new technologies with large potential impact, such as artificial intelligence, there is often a call for responsible innovation. This requires early involvement of stakeholders to identify relevant values, in order to make sure that those values can be taken into account in the design. However, it is not always clear to what extent such adaptations in the design are meaningful in the practices surrounding the use of the new technology in work environments. For example, if artificial intelligence is made explainable to improve transparency, why and how would users want to make use of such a feature?  Social sciences can play a key role in addressing the practical aspects of responsible digitalization, which is why “the human factor in new technologies” is one of the interdisciplinary themes in the Dutch investment in social sciences via the so-called “sector plans”. In this talk, I will outline how social sciences can provide an essential contribution to responsible digitalization through studying “responsibility practices”, including for example seeking, receiving and challenging advice, searching for information, or communicating decisions, and the impact of technological developments on those practices. In particular, I will look at the challenges of studying those practices in situations where interaction with digital technologies goes beyond 1-on-1 situations, as in human-AI teams.

When customers process information, they also process emotions. Therefore, identifying emotions during information processing is crucial for marketers and businesses to generate an effective customer experience, in both B2C and B2B settings. However, many individual internal neurophysiological processes remain unexplored or difficult to access using traditional marketing methods. Neuroscience allows researchers to study emotions by monitoring neural activity and physiological responses through the various stages of the customer experience. In B2B marketing, neuroscience offers valuable insights into decision-making, price setting, and negotiation processes, among other areas. This talk will present the current state of neuroscience in B2B marketing, along with a fresh methodological perspective on how different communication styles impact negotiation process.