European Journal of Histochemistry

Automation is becoming increasingly complex, playing a larger role in driving and expanding its operational design domain to dynamic urban roads. Explainable AI (XAI) research in computer science aims to craft explanations of automation that help people understand the behavior of complex algorithms. However, many XAI approaches rely on fixed-format explanations, which may not effectively support drivers with varying levels of automation knowledge and tasks with different timescales. Maintaining common ground is a multilevel process, in which individuals and automation must adjust communication format and abstraction based on knowledge and time constraints. We first draw on existing research to suggest that common ground is a shared understanding between drivers and automation that requires constant maintenance. We applied the abstraction hierarchy (AH) modeling method, which describes complex systems across multiple abstraction levels to match drivers’ cognitive capacity. We modified it to translate vehicle and traffic data into multilevel explanations of automation behavior. We expanded the model into the abstraction–decomposition space, naming it the Driver–Automation Teaming model, designed to generate explanations that account for task timescale. With this modified model, we developed three human–machine interface concepts to demonstrate how it can improve XAI’s support for driver–automation collaboration.

Effective human-autonomy teaming is increasingly important to ensuring mission success in operational environments. Modeling operators’ cognitive states, including trust, situation awareness (SA), and mental workload (WL), may improve human-autonomy team performance by informing autonomous systems about human operators. Subjective questionnaires are often used to measure these states but are obtrusive and impractical for real-world operations. Integrating observable and physiological measures could enable unobtrusive measurements of cognitive states. We created models to estimate trust, SA, and WL using observable, physiological, and operator background information (OBI) measures. We collected data from 15 subjects during a spacecraft docking simulation. We developed a LASSO-based algorithm to select features, generated multivariate regression models, and assessed predictive capabilities. Observable and OBI features combined led to the best performing model, indicating that physiological signals do not add significant predictive power. Simultaneous feature selection of SA and WL yielded performance comparable to that of models fit to a single cognitive state, but did not reduce the number of required physiological sensors. The developed algorithm, use of multiple feature modalities, and simultaneously fitting capability can be leveraged for better estimating human cognitive states for human-autonomy teaming.

Among different components of empathy, cognitive empathy is a necessary facet to understand a patient’s perspectives tapping into their thoughts and reasonings. With the rise of AI chatbots within healthcare settings, it has become important to mirror these cognitive empathetic elements within these chatbots too. Symptom assessment AI chatbots can be valuable tools for initial screening or triage in healthcare settings. This paper seeks to understand the cognitive empathy elements present in patient-physician interactions and offers recommendations for integrating these elements into AI chatbots. This represents a novel exploration of cognitive empathy within the patient-physician context and the potential for incorporating it into patient-AI interactions. We interviewed 10 first-time mothers to explore possible communication patterns for establishing cognitive empathy between patient-physician. We identified incidents with critical interactions where the physician or midwife could develop a shared understanding or common ground with their patients even if they were not aligned at the beginning of the incidents. Six themes emerged from the interviews: (1) Shared Information, (2) Shared Decision-making, (3) Shared Sensemaking, (4) Shared Goals, (5) Communication about Outcomes, and (6) Tailoring to Circumstances. Based on the themes and the interactions extracted from the interviews, we developed design recommendations for symptom assessment/diagnostic reasoning AI chatbots. The design recommendations we provided in this paper offer guidance in developing cognitively more empathic AI chatbots as they are becoming an important part of building robust digital health solutions. Cognitive empathetic elements within these chatbots would allow the users or patients to make improved and empowered choices, decisions, and outcomes through optimal contribution from them with a complete information base.

The Naturalistic Decision Making (NDM) paradigm is an emerging shift in how researchers study decision making in complex, real-world situations and design decision supports. The purpose of this scoping review was to describe how the NDM paradigm was applied in studies of mental health professionals. Six bibliographic databases were searched to identify NDM studies. Each study was charted for study features, participant demographics, decision contexts, and the essential characteristics of NDM research. The search identified 26 studies published from 1989 to June 2023. Approximately 35% of studies were published in a peer-reviewed journal. Quantitative (30.8%), qualitative (34.6%), and mixed (34.6%) methods were utilized in similar percentages of studies, with social workers (61.5%) most frequently represented in these studies. Approximately 69% of studies examined assessment decisions (versus diagnosis or treatment) and roughly 96% of studies examined individuals (versus teams). Most studies explored professionals’ decision making process (73.1%) and how proficient decision makers utilized their experience to make decisions (38.5%). The NDM literature among mental health professionals is growing, with many opportunities to understand clinical decision making using well-established NDM concepts and methods. The review concludes with recommendations for both NDM and mental health services researchers.

In this study, we investigated strategies to address trust issues arising from errors in large language models (LLMs). The study examined the impact of confidence scores, system capability explanations, and user feedback on trust restoration post-error. 68 participants viewed the responses of an LLM to 20 general trivia questions, with an error introduced on the third trial. Each participant was presented with one mitigation strategy. Participants rated their overall trust in the model and the reliability of the answer. Results showed an immediate drop in trust after the error; however, there were no differences across the three strategies in trust recovery. All conditions had a logarithmic trend in trust recovery following error. Differences in overall trust were predicted by perceived reliability of the answer, suggesting that participants were evaluating results critically and using that to inform their trust in the model. Qualitative data supported this finding; participants expressed lasting distrust despite the LLM’s later accuracy. Results showcase the need to prioritize accuracy in LLM deployment, because early errors may irrevocably damage user trust calibration and later adoption.

Trust development will play a critical role in remote vehicle operations transitioning from automated (e.g., requiring human oversight) to autonomous systems. Factors that affect trust development were collected during a high-fidelity remote uncrewed aerial system (UAS) simulation. Six UAS operators participated in this study, which consisted of 17 trials across two days per participant. Trust in two highly automated systems were measured pre- and post-study. Perceived risk and familiarity with the systems were measured before the study. Main effects showed performance-based trust and purpose-based trust increased between the pre- and post-study measurements. System familiarity predicted process-based trust. An interaction indicated that operators who rated the systems as riskier showed an increase in a single-item trust scale between the pre- and post-study measurement, whereas participants that rated the systems as less risky maintained a higher trust rating. Individual differences showed operators adapted to why the automation was being used, and trust improved between measurements. Qualitative analysis of open-ended responses revealed themes related to behavioral responses of the aircraft and transparency issues with the automated systems. Results can be used to support training interventions and design recommendations for appropriate trust in increasingly autonomous remote operations, as well as guide future research.

The study tested the role of cue utilization and cognitive reflection tendencies in email users’ phishing decision capabilities in both controlled and naturalistic settings. 94 university students completed measures of their phishing cue utilization and cognitive reflection, a phishing decision task, and a naturalistic simulated phishing campaign, in which they were sent simulated phishing emails to their personal inboxes. For the phishing decision task, results revealed that participants with lower cognitive reflection tendencies were more likely to misclassify genuine emails as phishing, compared to participants with higher cognitive reflection. Further, participants with higher cognitive reflection and lower cue utilization took the most time to diagnose emails, but participants low in both cue utilization and cognitive reflection demonstrated the shortest response latencies. These findings suggest that greater cognitive reflection can offset lower levels of cue utilization. For the naturalistic simulation, neither cue utilization nor cognitive reflection predicted an increased propensity to interact with a suspicious email. This result highlights a potential gap between phishing investigations conducted in controlled and naturalistic settings. The implications extend to future research, emphasizing the need for studies that employ naturalistic methodologies to better understand and address phishing threats in real-world environments.

Fifteen commentaries have either responded to, or been inspired by, our article about the automation failure construct, which introduced a taxonomy of failure mechanisms for consideration in the design of cognitive engineering experiments. Our rejoinder organizes these responses into those aligned with the objectives of the target article, those inspired by it, a couple that rehash our earlier articles, and one that seeks to put us all in our place. We conclude with an assessment of points of consensus and divergence in our shared apprehension of the automation failure construct.

This paper reports a qualitative exploration of gaze strategies during closed-circuit television (CCTV) tasks in remote nautical object control of a lock. Previous research has not examined gaze strategies in scenarios where systems, such as nautical objects, are operated remotely using CCTV. As contextual factors matter in nautical object control, a qualitative approach was necessary to uncover domain-specific terminology and insights into gaze strategies. We recorded eye gaze from professional lock operators and then conducted semi-structured interviews with domain experts to assess these recordings. Thematic analysis revealed that experts were able to identify (features of) gaze strategies but did not share the same terminology. Based on this analysis we defined four strategies: anticipating, verifying, overview, and movement-directed gazes (RQ1). All strategies, except movement-directed gaze, were observed consistently across operators (RQ2), with verifying gaze aligning with task steps in a predefined protocol (RQ3). More generally, our classification framework from thematic analysis could help to systematically define and verify gaze strategies based on domain and task features across various CCTV working contexts. For nautical object control, the framework can be instrumental in interpreting and verifying future (quantitative) eye tracking results and informing instructional procedures.

Clinical reasoning and decision-making in prehospital contexts are complex, and patient assessments may be influenced by stress or biases, thus potentially risking patient safety. Previous research has shown mixed results regarding cognitive interventions designed to counteract biases and improve decision-making. In educational settings, there are no tools that assess clinical reasoning while also measuring important decision-making outcomes. This study employed a mixed-methods design and a novel assessment model to evaluate clinical reasoning and decision-making among Swedish prehospital nurse specialists. Additionally, the effect of the metacognitive TWED mnemonic was investigated. Thirteen participants were randomly assigned to two groups and assessed patients in simulation settings, with groups switching cases after brief training on the TWED mnemonic. The primary outcomes included point-based scoring on decision-making, grading of potential risks of patient harm, and analysis of clinical reasoning through reflections. The results showed large variation, without overall differences between groups or demographics. A complex case presentation resulted in lower scores and greater risks of potential patient harm. Qualitative analysis highlighted participants’ ability to handle conflicting data, which correlated with better outcomes. The use of the TWED mnemonic may have increased commission bias. Further research is needed to validate and understand these findings.

This brief Introduction provides an overview and clustering of the invited commentaries to the Skraaning and Jamieson target article, as well as implications for future research in the area of automation failure.

This research provides a current benchmark of the cognitive skills and cognitive processes needed for flight path management (FPM) in current commercial air transportation flight operations. While some cognitive skills for aviation have been identified, it remains unclear which skills are most pertinent for different phases of flight, for different tasks, across different aircraft types, and during operational complexity. Further, there is concern that flight deck automation may contribute to cognitive skill degradation. Two expert pilots participated in cognitive walkthroughs to establish a current benchmark of the cognitive skills and cognitive processes needed for FPM. The tasks involved seven different phases of flight and two different aircraft; the results from two phases are reported—Preflight Briefing and Initial Climb. The findings indicate nineteen cognitive skills, and three metacognitive skills are used by pilots for FPM. In addition, the cognitive process models needed for FPM are all very similar, regardless of the aircraft type, task, phase of flight, or increased operational complexity. These results provide a foundation for future efforts on cognitive skill degradation, training of FPM cognitive skills, and may be used to inform the design of new automated systems to support pilot cognition.

Overconfidence in situation awareness (SA) can lead to various detrimental consequences, including risky behaviors. This study investigates the influence of stress and attention allocation on SA in manufacturing. Specifically, this study aims to (1) examine the effects of stress on objective SA, (2) explore the relationship between SA levels and SA overconfidence, and (3) investigate the potential alleviation of SA overconfidence through attention allocation to more challenging SA levels. These findings demonstrate that stress impairs the comprehension aspect of SA. Moreover, SA overconfidence increases with the SA level. In exploring strategies to mitigate SA overconfidence, allocating attention specifically to the most challenging SA level—SA projection—proved effective in alleviating overconfidence. These findings contribute to future SA research by clarifying the relationship between stress and SA, uncovering the link between SA levels and SA overconfidence, and exploring methods to alleviate overconfidence and improve decision accuracy in manufacturing systems.