Matthew B Thompson

Professions such as military, aviation, submarine operation, and emergency response require individuals to navigate complex environments characterized by limited information, stringent time constraints, and significant pressures. Effective decision making under pressure is crucial in safety–critical professions, yet measuring this expertise remains challenging. Inspired by the military context, this article introduces the virtual reality decision-making expertise (VR-DMX) environment, designed to evaluate decision-making expertise under time constraints within a virtual reality scenario. VR-DMX simulates an amusement arcade where users must decide how to allocate time across various games to maximize ticket earnings. Through two validation studies (N = 60 and N = 76), we examined two metrics: Total Tickets (measuring overall performance) and DMX score (isolating decision-making quality). Both metrics demonstrated symmetrical distributions without floor or ceiling effects, with coefficients of variation comparable to established individual difference measures (32.4–37.4% for Total Tickets; 20.8–27.6% for DMX score). The moderate correlation between metrics (meta-analysis r = 0.771, 95% CI [0.599, 0.943]) indicates they measure related but distinct constructs. Our findings indicate that VR-DMX effectively differentiates individual performance levels and captures a distinct decision-making component that is separate from general cognitive abilities. Comparing decision-making expertise between professionals in safety–critical fields with those without such experience would be a sensible next step to help validate the potential for selection and training applications. VR-DMX was designed to measure decision-making expertise in safety–critical contexts, and initial validation data demonstrating effective differentiation of individual performance levels suggest that continued development could fulfill this design intention for applications in selection, training, and performance prediction.

Objective
We examined whether allowing operators to self-select automation transparency level (adaptable transparency) could improve accuracy of automation use compared to nonadaptable (fixed) low and high transparency. We examined factors underlying higher transparency selection (decision risk, perceived difficulty).

Background
Increased fixed transparency typically improves automation use accuracy but can increase bias toward agreeing with automated advice. Adaptable transparency may further improve automation use if it increases the perceived expected value of high transparency information.

Methods
Across two studies, participants completed an uninhabited vehicle (UV) management task where they selected the optimal UV to complete missions. Automation advised the optimal UV but was not always correct. Automation transparency (fixed low, fixed high, adaptable) and decision risk were manipulated within-subjects.

Results
With adaptable transparency, participants selected higher transparency on 41% of missions and were more likely to select it for missions perceived as more difficult. Decision risk did not impact transparency selection. Increased fixed transparency (low to high) did not benefit automation use accuracy, but reduced decision times. Adaptable transparency did not improve automation use compared to fixed transparency.

Conclusion
We found no evidence that adaptable transparency improved automation use. Despite a lack of fixed transparency effects in the current study, an aggregated analysis of our work to date using the UV management paradigm indicated that higher fixed transparency improves automation use accuracy, reduces decision time and perceived workload, and increases trust in automation.

Application
The current study contributes to the emerging evidence-base regarding optimal automation transparency design in the modern workplace.

Visual inference involves using prior knowledge and contextual cues to make educated guesses about incomplete or ambiguous information. This study explores the role of visual inference as a function of expertise in the context of fingerprint examination, where professional examiners need to determine whether two fingerprints were left by the same person, or not, often based on limited or impoverished visual information. We compare expert and novice performance on two tasks: inferring the missing details of a print at an artificial blank spot (Experiment 1) and identifying the missing surrounds of a print given only a small fragment of visual detail (Experiment 2). We hypothesized that experts would demonstrate superior performance by leveraging their extensive experience with global fingerprint patterns. Consistent with our predictions, we found that while both experts and novices performed above chance, experts consistently outperformed novices. These findings suggest that expertise in fingerprint examination involves a heightened sensitivity to gist, or global image properties within a print, enabling experts to make more accurate inferences about missing details. These results align with prior research on perceptual expertise in other expert domains, such as radiology, and extend our understanding of scene and face recognition to fingerprint examination. Our findings show that expertise emerges from an ability to combine local and global visual information—experts skillfully process both the fine details and overall patterns in fingerprints. This research provides insight into how perceptual expertise supports accurate visual discrimination in a high-stakes, real-world task with broader implications for theoretical models of visual cognition.

Emergency management operations are a complex system involving multiagency hierarchical command structures requiring coordination of significant volumes of information, resourcing and effort across expansive disaster zones within restricted timeframes and with potentially catastrophic consequences. Within these environments establishing a timely and shared understanding among different levels of command and across agencies is crucial yet remains a significant challenge. The problem is subsequently framed as ʻhow can the establishment of a systems‐level common operating picture across hierarchical commands and different agencies be improved in the context of complex emergency management and disaster response?ʼ To address this, we present the Maaloop Framework which builds upon the insights gained from previous research within the context of threat assessment, sensemaking, and critical decision‐making in police, military, ambulance, and firefighting contexts. To the observer trained in its application, it aims to provide an instant, point‐in‐time, global and detailed visual intelligent product of (i) the amount of information identified by decision makers at each location; (ii) how confident those decision makers are in the information and its application to resolve an incident; (iii) how the decision maker in adjacent command levels are using the information being fed to them. In turn, it will assist in improving individual and collective understanding regardless of position within a command hierarchy or agency jurisdiction. We submit that the Maaloop Framework does no more than describe the common constructs and concepts identified in the literature describing decision‐making within these industries and the broader study of decision‐making in the field of psychology. Therefore, this framework is intended to provide a solution to provide systems‐level common operating picture and an initial basis for increased communication and understanding across complex crisis events.

Decision-making in emergency situations, such as those faced by fire, police, and health service personnel, presents unique challenges due to the high-stakes and time-pressured environment. Here we aim to better understand what emergency responders regard as constituting ‘good’ and ‘bad’ decisions in emergency situations. We administered a modified Delphi study, eliciting opinions from decision-makers across all these sectors towards consensus around the key elements of good and bad decision-making. Participants were first asked to define what makes a ‘good’ and a ‘bad’ decision, and subsequently to identify the top five most important elements of each. While consensus was not found, important insights were identified that can assist improve the standard of decision making at all levels of emergency response and management. We observed (i) a lack of a common understanding between participants of what a decision is, and how a decision differs from pre-decision and postdecision components; (ii) responses varied according to whether a free text description or the identification of separate elements was requested; (iii) respondents valued ‘goodness’ across different and at times unrelated components a scaled measure of decision quality as opposed to a binary evaluation of ‘rightness’; and (iv) pre- and postdecision elements are considered more important than the decision itself when determining the quality of a decision. To address the issues highlighted by the study we recommend improvements in training, and improvements to organization doctrine related to decision making, risk tolerance, assessment of decision quality and development of intent-based or principle-based operational guidance. Ultimately, incident controllers from all services must remain cognizant that they will face considerable scrutiny if they cannot explain how they arrived at the decision they made.

Expert fingerprint examiners demonstrate impressive feats of memory that may support their accuracy when making high-stakes identification decisions. Understanding the interplay between expertise and memory is therefore critical. Across two experiments, we tested fingerprint examiners and novices on their visual short-term memory for fingerprints. In Experiment 1, experts showed substantially higher memory performance compared to novices for fingerprints from their domain of expertise. In Experiment 2, we manipulated print distinctiveness and found that while both groups benefited from distinctive prints, experts still outperformed novices. This indicates that beyond stimulus qualities, expertise itself enhances short-term memory, likely through more effective organisational processing and sensitivity to meaningful patterns. Taken together, these findings shed light on the cognitive mechanisms that may explain fingerprint examiners’ superior memory performance within their domain of expertise. They further suggest that training to improve memory for diverse fingerprints could practically boost examiner performance. Given the high-stakes nature of forensic identification, characterising psychological processes like memory that potentially contribute to examiner accuracy has important theoretical and practical implications.

Decisions in forensic science are often binary. A firearms expert must decide whether a bullet was fired from a particular gun or not. A face comparison expert must decide whether a photograph matches a suspect or not. A fingerprint examiner must decide whether a crime scene fingerprint belongs to a suspect or not. Researchers who study these decisions have therefore quantified expert performance using measurement models derived largely from signal detection theory. Here we demonstrate that the design and measurement choices researchers make can have a dramatic effect on the conclusions drawn about the performance of forensic examiners. We introduce several performance models – proportion correct, diagnosticity ratio, and parametric and non-parametric signal detection measures – and apply them to forensic decisions. We use data from expert and novice fingerprint comparison decisions along with a resampling method to demonstrate how experimental results can change as a function of the task, case materials, and measurement model chosen. We also graphically show how response bias, prevalence, inconclusive responses, floor and ceiling effects, case sampling, and number of trials might affect one’s interpretation of expert performance in forensics. Finally, we discuss several considerations for experimental and diagnostic accuracy studies: (1) include an equal number of same-source and different-source trials; (2) record inconclusive responses separately from forced choices; (3) include a control comparison group; (4) counterbalance or randomly sample trials for each participant; and (5) present as many trials to participants as is practical.

With dementia incidence projected to escalate significantly within the next 25 years, the United Nations declared 2021-2030 the Decade of Healthy Ageing, emphasising cognition as a crucial element. As a leading discipline in cognition and ageing research, psychology is well-equipped to offer insights for translational research, clinical practice, and policy-making. In this comprehensive review, we discuss the current state of knowledge on age-related changes in cognition and psychological health. We discuss cognitive changes during ageing, including (a) heterogeneity in the rate, trajectory, and characteristics of decline experienced by older adults, (b) the role of cognitive reserve in age-related cognitive decline, and (c) the potential for cognitive training to slow this decline. We also examine ageing and cognition through multiple theoretical perspectives. We highlight critical unresolved issues, such as the disparate implications of subjective versus objective measures of cognitive decline and the insufficient evaluation of cognitive training programs. We suggest future research directions, and emphasise interdisciplinary collaboration to create a more comprehensive understanding of the factors that modulate cognitive ageing.

While co-design methods are crucial for developing digital educational interventions that are user-centred, contextually relevant, inclusive, and effective in meeting the diverse needs of learners and educators, little attention has been paid to the potential value of co-design processes for digital application development in the Defence context. This research gauged the efficacy of combining a generative co-design framework making use of agile and iterative co-design principles in an applied research and development project. The project produced an immersive virtual reality based digital solution in collaboration with the Australian Defence Force Special Operations Command (SOCOMD) Army. Specifically, the ParaVerse project sought to develop a solution considering the advanced Tactics, Techniques and Procedures (TTPs) relevant to special operations soldiers for advanced parachute training. A Defence advisory group consisting of a series of subject matter experts was formulated to consult with the research and development team over the course of the co-design process. End-user testing with 35 SOCOMD personnel demonstrated the value of the ParaVerse application for SOCOMD personnel, speaking to the success of the leveraged generative co-design model. End-users rated ParaVerse as having greater capacity to influence education and training practices for SOCOMD and Defence generally in comparison to a pre-existing virtual parachute simulator. ParaVerse was also rated higher for satisfaction and useability and was associated with fewer instances of motion sickness. The Generative Co-Design Framework leveraged for this research provides one roadmap on how to integrate end-users in innovation design, particularly for projects working across the nexus of Defence and academia.

This paper distils seven key lessons about ‘error’ from a collaborative webinar series between practitioners at [redacted] and academics. It aims to provide the common understanding of error necessary to foster interdisciplinary dialogue, collaboration and research. The lessons underscore the inevitability, complexity and subjectivity of error, as well as opportunities for learning and growth. Ultimately, we argue that error can be a potent tool for continuous improvement and accountability, enhancing the reliability of forensic sciences and public trust. It is hoped the shared understanding provided by this paper will support future initiatives and funding for collaborative developments in this vital domain.