Christoph Hoelscher

Cognitive maps are mental representations of space essential for guiding spatial behavior. To assess the properties of these cognitive maps, sketch mapping has been widely used as a research tool in spatial cognition research. This scoping review aims to provide a comprehensive overview of the methodologies and the cognitive processes concerning the externalization of cognitive maps through sketch mapping. Following the PRISMA-ScR checklist (Tricco et al. in Ann Intern Med 169(7):467–473, 2018, https://doi.org/10.7326/M18-0850), a comprehensive search from five electronic databases was performed with predefined combinations of keywords. Twenty-four articles were selected and analyzed, covering a wide range of methods: traditional pen-and-paper sketching (n = 18 studies); combination of pen-and-paper and digital sketching (n = 1); exclusively digital sketching (n = 4); and digital VR sketching (n = 1). With regard to the formation of cognitive maps in environmental scale spaces, studies employed either direct experience or virtual experience of unfamiliar environments, videos, maps, or retrieval from own memory. This review highlights the inherent conflict between sketch maps’ advantages in capturing knowledge in less structured experimental protocols and researchers’ need for structured quantification of their quality, as well as the underused diversity of media through which sketch maps can be produced for appropriate scenarios. We encourage researchers to (a) increase the precision in reporting the cognitive processes being investigated with sketch maps (e.g., short-term vs. long-term memory), (b) rely on established data analysis methods instead of developing custom measures for each study, and (c) consider alternative media beyond pen and paper when more suitable to the experimental context.

Background: Continuously and unobtrusively monitoring work-related stress may help combat its detrimental effects on mental and physical health. For work in offices specifically, mouse and keyboard data have been suggested as highly suitable data sources for stress detection, in addition to physiological data such as heart rate variability (HRV). However, previous studies have yielded mixed results regarding the potential of mouse and keyboard data to detect stress, and very few works have examined the connection in real work environments. Moreover, concerns regarding the robustness and validity of existing stress detection approaches have been raised, emphasising the need for more rigorous investigations in the field. Methods: We conducted an 8-week observational field study with office employees ( N = 36) where we collected mouse, keyboard, and cardiac data as well as self-reported stress during working hours. We derived mouse movement, keystroke dynamics, and HRV features, and trained regression models to detect self-reported stress levels using machine learning algorithms including Elastic Net, Random Forest, eXtreme Gradient Boosting (XGBoost), and Recurrent Neural Networks in two distinct modelling approaches: (1) one-fits-all, and (2) personalised. The first approach aims to detect the stress levels of a participant using training data from other participants. In the second, individual models are trained per participant. Results: The one-fits-all modelling approach yields modest correlations with true labels (Spear-man’s ρ = 0.078) under leave-one-subject-out cross-validation, with a slight improvement when incorporating time series of feature values (Spearman’s ρ = 0.096). In the personalised approach, XGBoost models trained on mouse and keyboard features reach an average Spearman’s ρ of 0.188 under blocked cross-validation. When optimised across machine learning models and feature sets, performance of the personalised approach further improves, reaching an average Spearman’s ρ of 0.296. Conclusion: Our results suggest that that developing robust and valid stress detection models from in-field data remains challenging, reflecting the complexity of affective computing in naturalistic settings. Personalised modelling approaches show encouraging potential and warrant further exploration. We offer actionable recommendations to advance research on automated stress detection in real-world settings and openly share our dataset to promote innovation and collaboration within the research community.

In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in complex buildings and incorporates robotic navigation and intelligent path planning. A novel simulated human behavior model was integrated, capable of complex human--robot interaction, independent escape route searching, and exhibiting herd mentality and memory mechanisms. We also proposed a multiagent framework that combines MARL and ARL to enhance overall evacuation efficiency and robustness. Additionally, we developed a new ARL evaluation framework that provides a novel method for quantifying agents' performance. Various experiments of differing difficulty levels were conducted, and the results demonstrate that the proposed framework exhibits advantages in emergency evacuation scenarios. Specifically, our ARLR approach increased survival rates by 1.8% points in low-difficulty evacuation tasks compared to the RLR approach using only MARL algorithms. In high-difficulty evacuation tasks, the ARLR approach raised survival rates from 46.7% without robots to 64.4%, exceeding the RLR approach by 1.7% points. This study aims to enhance the efficiency and safety of human--robot collaborative fire evacuations and provides theoretical support for evaluating and improving the performance and robustness of ARL agents.

Architects and designers must consider issues of sustainability, building regulations, as well as a multitude of subjective questions such as style and taste. They are asked to create spaces not just in consideration of expected user needs, but in anticipation of them, whilst simultaneously defending and substantiating the value of their design decisions. By contrast, there is a growing body of research examining person-environment interactions and how experiences are immediately affected by designed properties of spaces. The research approach of Architectural Cognition in Practice seeks to develop a robust bridge to link learnings about user cognition from academic disciplines such as spatial cognition, neuroarchitecture and environmental psychology into real-world design processes. Toward this, we propose a framework addressing three distinct streams of issues: (1) Fundamental research to increase our understanding of person–environment interactions and their impact on cognition, decision-making and experience; (2) Reflective research considering how (and how well) designers currently conceptualize their end-users in the design process; and (3) Translational research to equip design teams with knowledge and tools to be able to apply learnings from architectural cognition into their practice. We briefly overview each pillar of research and provide examples from our own pool of research to demonstrate how our framework and methodologies aim to tackle this stated divide between knowledge and practice.

INTRODUCTION Alzheimer’s disease affects spatial abilities that are often overlooked in standard cognitive screening tools. We assessed whether the spatial navigation tasks in the Spatial Performance Assessment for Cognitive Evaluation (SPACE) can complement existing tools such as the Montreal Cognitive Assessment (MoCA). METHODS 348 participants aged 21-76 completed the MoCA, SPACE, and sociodemographic- health questionnaires. Regressions were used to predict MoCA scores with risk factors and SPACE tasks as predictors. We also conducted a factor analysis to investigate the relationships among SPACE tasks and the MoCA. RESULTS Regressions revealed significant effects of age, gender, and SPACE tasks. No risk factors for dementia predicted MoCA scores. The factor analysis revealed that MoCA and perspective taking contributed to a separate factor from other navigation tasks in SPACE. Normative data for SPACE are provided. DISCUSSION Our findings highlight the importance of navigation tasks for cognitive assessment and the early detection of cognitive impairment.

As larger parts of our lives are determined in the digital realm, it is critical to reflect on how democratic values can be preserved and cultivated by technology. At the city-scale, this is studied in the field of ‘digital civics’; however, there seems to be no corresponding focus at the level of buildings/building inhabitants. The majority of our lives are spent indoors and therefore the impact that ‘indoor digital civics’ may have, might exceed that of city-scale, digital civics. The digitization of building design and building management creates an opportunity to better identify, protect, and cultivate civic values that, until now, were centralized in the hands of building designers and building owners. By bringing together leading architecture/HCI academics and commercial stakeholders, this workshop builds on previous workshops at CHI. The workshop will provide a forum where a new agenda for research in ‘HabiTech’ can be defined and new research collaborations formed.

Gender differences in navigation performance are a recurrent and controversial topic. Previous research suggests that men outperform women in navigation tasks and that men and women exhibit different navigation strategies. Here, we investigate whether motivation to complete the task moderates the relationship between navigation performance and gender. Participants learned the locations of landmarks in a novel virtual city. During learning, participants could trigger a top-down map that depicted their current position and the locations of the landmarks. During testing, participants were divided into control and treatment groups and were not allowed to consult the map. All participants were given 16 minutes to navigate to the landmarks, but those in the treatment group were monetarily penalized for every second they spent completing the task. Results revealed a negative relationship between physiological arousal and the time required to locate the landmarks. In addition, gender differences in strategy were found during learning, with women spending more time with the map and taking 40% longer than men to locate the landmarks. Interestingly, an interaction between gender and treatment group revealed that women in the control group required more time than men and women in the treatment group to retrieve the landmarks. During testing, women in the control group also took more circuitous routes compared to men in the control group and women in the treatment group. These results suggest that a concurrent and relevant stressor can motivate women to perform similarly to men, helping to diminish pervasive gender differences found in the navigation literature.

There is an active debate concerning the association of handedness and spatial ability. Past studies used small sample sizes within a single country. Determining the effect of handedness on spatial ability requires a large, cross-cultural sample of participants, and a navigation task with real-world validity. Here, we overcome these challenges via the mobile app Sea Hero Quest. We analysed the navigation performance from 422,772 participants from 41 countries and found no reliable evidence for any difference in spatial ability between left- and right-handers across all countries. Using 749,037 participants from the larger sample, we replicate previous findings that age, gender, and country of residence have an impact on the prevalence of left-handedness, and found an effect of education on left-handedness prevalence in China, Indonesia, India, Taiwan, and Hong Kong. Our study clarifies the factors associated with spatial ability and outlines new ways in which cultural patterns influence handedness. Statement of Relevance What is the relation between handedness and navigation ability? Evidence so far has been mixed, and findings from small-scale and large-scale tasks seem to point in opposite directions. Part of the reason is that cultural and sociodemographic differences have a significant impact on both spatial ability and handedness. Tackling the question requires a large, cross-cultural sample of participants performing an ecologically valid navigation task. Here, we employ a mobile app, Sea Hero Quest, to test the navigation ability of a large number of participants across many different countries. What we find is that there is no reliable connection between handedness and navigation ability. Then, we use our data to explore the prevalence of left-handedness across countries, and find that age, gender, and country of residency all have an effect on the ratio of left-handedness. Moreover, we find an effect of education on left-handedness in China, Indonesia, India, Taiwan, and Hong Kong.

Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.