Both excessive sitting and excessive standing have been shown to be detrimental for performance, productivity and health. In the present study, our objective was specifically to determine the effect of alternating the body position (between standing and sitting) on task performance and visual attention in the Attention Network Task (ANT), relative to a sitting-only condition. Twenty-four participants (aged 18–35) performed the ANT six times in both conditions (5 min 35 per ANT). The proportion of blinks was significantly lower in the alternating condition than in the sitting-only condition. In both between-condition and within-condition analyses, the reaction times were significantly shorter when standing than when sitting. Humans may be more effective (i.e. a shorter reaction time) and have greater visual attention (i.e. less frequent proportion of blinking) in an alternating condition than in a sitting-only condition. In practice, the use of sit-stand desks might usefully help to both reduce the time spent sitting and improve task performance.

Reading assistants provide users with additional information through pop-ups or other interactive events which might interrupt the flow of reading. We propose that unnoticeable changes can be made in a given text during blinks while the vision is obscured for a short period of time. Reading assistants could make use of such change blindness to adapt text in real time and without infringing on the reading experience. We developed a system to study blink-induced change blindness. In two preliminary experiments, we asked five participants to read six short texts each. Once per text and during a blink, our system changed a predetermined part of each text. In each trial, the intensity and distance of the change were systematically varied. Our results show that text changes — although obvious to bystanders — were difficult to detect for participants. Concretely, while changes that affected the appearance of large text parts were detected in 80% of the occurrences, no line-contained changes were detected.

Controlled studies in safety-critical domains such as Air Traffic Control (ATC) are inherently difficult, making high-fidelity simulators essential for research. However, existing simulation environments are often complex and expensive facilities that are only available at selected locations (e.g. flight simulators) or lack necessary realism, limiting their use in Human-Computer Interaction (HCI) research. This paper presents a framework that addresses this gap, demonstrating how a more realistic, sensor-enhanced simulation environment can be developed in a comparatively low-cost manner. Following the Design Science Research (DSR) methodology, we integrated the open-source BlueSky ATC engine with a custom frontend and multiple sensor modalities (e.g., eye-tracking, PPG, respiration). Our preliminary evaluation in a landing scenario case study confirms the framework’s effectiveness in capturing rich physiological and behavioral data corresponding to cognitive load. We present the system architecture, assess the DSR process, and release the framework as an open source tool to foster further research.

Predicting surgical time completion helps streamline surgical workflow and OR utilization, enhancing hospital efficacy. When time prediction is based on interventional video of the surgical site, time predictions may correlate with technical proficiency of the surgeon because skill is a useful proxy of completion time. To understand features that are predictive of surgical time in surgical site video, we develop prototype-like visual explanations, making them applicable to video sequences.

The heart does not beat like a metronome: varying parasympathetic input to the heart leads to constant heart rate variability. Vagal cardiomotor neuron activity is coupled to the respiratory cycle, leading to Respiratory Sinus Arrhythmia (RSA), a permanent oscillation of heart rate synchronized to respiration. Heart rate also temporarily decelerates in specific conditions such as in freezing due to perceived threat, or anticipation of a salient stimulus. Anticipatory Cardiac Deceleration (ACD) is observed consistently in anticipation of a stimulus in perceptual tasks, but its relationship with perceptual performance is debated. Previous quantifications of ACD neglect ongoing heart rate oscillations due to RSA, which may have led to inconsistencies in the ACD-related analyses across studies. Here, we suggest a novel approach to estimate trial-averaged RSA amplitude and respiratory phase-independent cardiac deceleration simultaneously, and apply it to an EEG-ECG dataset from a visual detection task. While the total ACD was not associated with perception, dissociating RSA-based and non-respiratory cardiac modulations revealed that they show opposing effects on perceptual performance. Additionally, we found that participants with higher ACD amplitudes also displayed larger Visual Awareness Negativity potentials, further supporting a contribution of ACD to visual perception. Impact Statement We present a novel analysis method to quantify task-related, anticipatory cardiac deceleration which takes tonic heart rate oscillations due to respiratory sinus arrhythmia into account. Our results add to previous research on the relationship between cardiac deceleration and perception by simultaneously characterizing and dissociating respiratory and non-respiratory heart rate modulations during stimulus anticipation.

Trust, mental workload, and situation awareness (TWSA) are cognitive states important to human performance and human-autonomy teaming. Individual and team performance may be improved if operators can maintain ideal levels of TWSA. Predictions of operator TWSA can inform adaptive autonomy and resource allocation in teams, helping achieve this goal. Current approaches of estimating TWSA, such as questionnaires or behavioral measures, are obtrusive, task-specific, or cannot be used in real-time. Psychophysiological modeling has the potential to overcome these limitations, but prior work is limited in operational feasibility. To help address this gap, we develop psychophysiological models that can be used in real time and that do not rely on operator-specific background information. We assess the impacts of these constraints on the models' performance. Participants ( n = 10) performed a human-autonomy teaming task in which they monitored a simulated spacecraft habitat. Regression models using LASSO-based feature selection were fit with an emphasis on model stability and generalizability. We demonstrate functional model fit (Adjusted R 2 : T = 0.67, W = 0.60, SA = 0.85). Furthermore, model performance extends to predictive ability, assessed via leave-one-participant-out cross validation ( Q 2 : T = 0.58, W = 0.46, SA = 0.74). This study evaluates model performance to help establish the viability of real-time, operator-agnostic models of TWSA.

Humans regularly navigate an overwhelming amount of information via text media, whether reading articles, browsing social media, or interacting with chatbots. Confusion naturally arises when new information conflicts with or exceeds a reader's comprehension or prior knowledge, posing a challenge for learning. In this study, we present a multimodal investigation of reading-induced confusion using EEG and eye tracking. We collected neural and gaze data from 11 adult participants as they read short paragraphs sampled from diverse, real-world sources. By isolating the N400 event-related potential (ERP), a well-established neural marker of semantic incongruence, and integrating behavioral markers from eye tracking, we provide a detailed analysis of the neural and behavioral correlates of confusion during naturalistic reading. Using machine learning, we show that multimodal (EEG + eye tracking) models improve classification accuracy by 4-22% over unimodal baselines, reaching an average weighted participant accuracy of 77.3% and a best accuracy of 89.6%. Our results highlight the dominance of the brain's temporal regions in these neural signatures of confusion, suggesting avenues for wearable, low-electrode brain-computer interfaces (BCI) for real-time monitoring. These findings lay the foundation for developing adaptive systems that dynamically detect and respond to user confusion, with potential applications in personalized learning, human-computer interaction, and accessibility.

In this uncontrolled, open-label exploratory clinical study, the authors explore the potential of blink data as a digital biomarker for estimating clinical indices of Parkinson’s disease (PD) using a machine learning approach. Blink data were collected from 20 patients with PD before and after (up to 4 h) L-dopa/decarboxylase inhibitor administration. Concurrent assessments of patient diary-based ON/OFF and dyskinesia, L-dopa plasma concentration, and MDS-UPDRS Part III scores were conducted at 30 min intervals. The models were developed to predict clinical symptoms based on blink data collected at 3 min intervals. The most effective post-processing models accurately predicted the ON/OFF states (mean area under the receiver operating characteristic curve (AUCROC) = 0.87) and the presence of dyskinesia (mean AUCROC = 0.84). They also moderately predicted MDS-UPDRS Part III scores (mean Spearman’s correlation ρ = 0.54) and plasma L-dopa concentrations (ρ = 0.57). Our findings highlight the potential of the spontaneous eye blink as a noninvasive, real-time digital biomarker for PD.

This study firstly aims to understand the impacts of map representations of coastal erosion on risk perception and affects of lay citizens. Secondly, it aims to study the effect of differing design of cartographic features on observation and interpretation of the message conveyed by different maps. Seven maps were presented to the participants (N = 50), varying according endogenous (abstraction, regalian cartridge) and exogenous characteristics (background, colours). A questionnaire interrogated risk perception and affect before and after observation, while eye-tracking data were recorded during the observation of each map. This experiment shows that communicating erosion risk by maps reduces perceived knowledge of risk but also reduces fear of the risk. The abstraction level significantly impacts observation patterns: correlation maps seem to guide visual attention in a more relevant way and to convey the message more clearly and effectively than other types of maps. But there is no influence of the other characteristics even if interviews show that colours seem to influence the message interpretation

As research on human behavior, such as spatial navigation, increasingly adopts naturalistic settings, establishing best practices for such experiments becomes essential. While virtual reality (VR) offers a bridge between laboratory control and real-world complexity, it does not fully capture the experiential richness of real-world environments. Here, we present a demonstration of a mobile eye-tracking study conducted in a large-scale, outdoor urban environment, featuring unconstrained, long-duration free exploration and outside-pointing tasks. Using the city of Limassol, Cyprus as our testbed, we showcase the feasibility of collecting high-quality mobile eye-tracking, head orientation, and GPS data “in the wild,” capturing a wide range of natural behavior with minimal experimental constraints. Based on this experience, we provide a set of best practices tailored to the logistical and methodological challenges posed by complex, real-world urban settings, challenges unlikely to arise in traditional indoor or highly controlled environments. While these recommendations have general relevance, we exemplify them in the context of spatial navigation research. By establishing methodological standards for studies at this scale, we aim to encourage and inform future research into naturalistic human behavior outside the laboratory.