Music can evoke both positive and negative moods, which may, in turn, differently affect the processing of food cues. This preregistered eye-tracking study investigated whether self-selected liked versus disliked music affects desire to eat, visual attention to foods of varying sugar content, and subsequent food choice in a buffet-like context. A total of 106 participants (mean age = 25 years; mean body mass index = 22 kg/m2) viewed a buffet with high-sugar foods, low-sugar alternatives, and non-foods while eye movements were recorded. Participants were randomly assigned to a liked music, disliked music, or no music condition. Self-reported desire to eat and food choice were assessed. Disliked music decreased general desire to eat but increased the specific desire to eat high-sugar food. Furthermore, it increased the likelihood of selecting high-sugar foods from the buffet. Liked music and no music were associated with a preference for low-sugar foods. Music did not significantly influence visual attention. Participants consistently looked longer at food than non-food items regardless of their music condition. These findings suggest that music can bias food-related decision-making independently of attentional processes: liked music may encourage healthier choices, whereas disliked music increases susceptibility to high-sugar comfort foods despite reduced general appetite. The results highlight the potential of music as a subtle, non-caloric intervention for promoting low-sugar eating behaviour. They also point towards risks of being exposed to disliked music in contexts in which food decisions are being made like in restaurants or supermarkets.

The progressive introduction of driving automation is profoundly transforming driver-vehicle interaction. Although automated driving systems are designed to improve vehicle stability and reduce driver workload, their influence on driver behavior especially during transitions between manual and automated control remains insufficiently understood.

Eye tracking has emerged as a powerful tool for examining visual perception and search strategies in various domains, including medicine. While it is relatively straightforward to apply in 2D settings, its use in 3D medical imaging remains challenging and not yet well explored. This gap is particularly relevant for radiology, where volumetric images such as computed tomography (CT) scans are routinely read by medical experts. Radiologists typically interpret these images by navigating through hundreds of 2D slices, most often viewed in the axial projection. A taxonomy of eye movement data during navigation through a CT volume could be valuable to understand how radiologists approach diagnostic tasks. As an example of the derived taxonomy, we asked two radiologists to search abdominal CTs of the pancreas. We collect eye tracking data and align eye gaze movements with slice navigation to visualize the representation of the pancreas through volume and analyze clinicians’ gaze behavior in both space and time.

Vergence is widely used as a proxy for depth perception and spatial attention in immersive and real-world eye-tracking studies. In this paper, we investigate how pupil size artefacts affect vergence estimates during real physical depth viewing with a head-mounted eye tracker. Using a beamsplitter setup with physically near and far targets, we elicited controlled convergent and divergent eye movements under static, luminance-modulated, and blockwise fixation conditions. Near and far targets were reliably separable in vergence angle across participants. However, pupil-vergence coupling varied substantially across individuals and conditions. Static illumination produced large inter-participant variability, while luminance modulation reduced this spread, yielding more clustered estimates. Blockwise and audio-cued recordings further showed that pupil-vergence coupling persists even without visual depth onsets. These results suggest that pupil size fluctuations can systematically influence vergence estimates, and that controlled viewing conditions can reduce--but not eliminate--this effect.

This study investigated how perceptual workload in driving situations is captured by subjective ratings versus eye-tracking metrics. Fifty participants completed low- and high-complexity conditions while fixation behavior, blinks, and pupil diameter were recorded, and workload was assessed using the DALI scale. High-load scenes elicited longer fixations, fewer fixations per minute, reduced blinking, and increased pupil dilation, indicating elevated attentional demand. DALI scores increased with scene complexity and were negatively associated with fixation duration, demonstrating that participants’ subjective ratings were driven primarily by perceptual strain rather than cognitive effort. Eye-tracking patterns supported this interpretation: fixation-based indicators tent to reflect the cognitive component of demand, whereas DALI selectively tracked perceptual overload. Together, these results show that DALI is highly sensitive to visual density, and that eye-movement measures provide converging evidence for its specificity as a perceptual load instrument.

Pupillometry is increasingly adopted to infer drivers’ psychophysiological states, as pupil diameter is sensitive to variations in cognitive workload, visual demand, and arousal. However, in automated driving contexts, pupillary responses may be simultaneously influenced by road geometry and by changes in the driver’s control role, making interpretation sharp.,This study investigates whether pupil diameter is modulated by road geometry and by the level of vehicle automation during simulated driving. A controlled experiment was conducted using a driving simulator, in which 39 drivers completed two traversals of the same rural road alignment. The first traversal was entirely manual, while the second included a manual-automated-manual sequence across three consecutive road sections. Horizontal alignment complexity was characterized by curve type (easy vs. sharp).,Pupil diameter was analyzed using linear mixed-effects models to account for repeated measurements and inter-individual variability. To properly reflect the sequential experimental design, both a global analysis and targeted within-subject comparisons were performed.,The results showed significant effects of curve type and road section on pupil diameter, as well as a significant interaction between geometry and section. In contrast, no significant main or interaction effects involving the level of automation were observed, even when directly comparing the same road section driven under manual and automated conditions.,Overall, the findings indicate that pupillometry is highly sensitive to infrastructure-induced visual and cognitive demand, while steady-state supervised automation does not produce a robust pupillary signature.

In Western societies, many people are unfamiliar with insect-based foods and reject them, despite their promise as a sustainable alternative to conventional animal protein. This mobile eye-tracking study examined how people view and evaluate insect-based foods in a buffet setting. Thirty-seven participants (mean age = 26 years) freely viewed a buffet containing 12 items from four categories: insect-based snacks, novel non-insect snacks, familiar snacks, and non-food objects. Mobile eye-tracking measured total and mean fixation durations for each item. Participants also rated each food item on disgust and desire to eat. The findings show that insect-based and novel snacks were viewed significantly longer than familiar snacks and non-foods, indicating increased visual engagement rather than oculomotor avoidance. Mean fixation duration did not differ across categories. Insect-based snacks elicited significantly higher disgust and lower desire to eat than both novel and familiar snacks. In conclusion, despite high disgust and low desire to eat, insect-based snacks attracted more visual attention than familiar foods and non-foods. This suggests that food disgust is not associated with oculomotor avoidance which is commonly observed when disgust is elicited by non-food.

This study was conducted to identify the environmental and human factors affecting wayfinding as part of fundamental research into children’s wayfinding, with the aim of preventing children from becoming lost. To this end, an eye-tracking-based wayfinding experiment was conducted in a complex commercial facility in South Korea with 51 children and 37 adults; the results were compared between the two groups. The eye-tracking data were analyzed using heatmaps and scanpaths, and the experimental environments were evaluated through visibility graph analysis (VGA). Key factors hindering children’s wayfinding include: The complexity of plan configuration can increase the difficulty of wayfinding when paths are irregular and have many forks. Children are shorter than adults and can be obstructed by shelves. Children have a greater attention span and memory for attractive elements like toys and landmarks, using diverse differentiation strategies than adults. Differentiation may be ineffective if passages or stores are very similar. Children may struggle to locate or recognize signage if it is poorly positioned, unclear, or illegible. Children under nine years of age may have difficulty with wayfinding due to underdeveloped spatial cognition. Thus, spaces for children should have simple layouts, high visibility, attractive architectural differentiation, and clear signage.

This study characterized the autonomic nervous system impulses underlying the iris spontaneous cyclic activity, which is characterized by hippus occurrence, by taking into account also its viscoelastic properties using the Kelvin-Voigt model. Hippus recordings were carried out in young active individuals, in the supine and standing position to test the model sensitivity, simultaneously with cardiac activity. This was followed by the recording of a photomotor reflex sequence. A clustering analysis allowed the selection of relatively low-noise hippus, where the model showed a high degree of fit (mean error = 3.5 ± 1.1%). Despite the modest reproducibility of autonomic nervous system impulses, ranging from 0.35 ± 0.33 to 0.88 ± 30 in each participant, hippus showed strong similarities, suggesting the existence of an autonomic activity signature. Parasympathetic impulses were significantly (p = 3.4 × 10− 3) higher in the supine than in the standing position. The amount of expended energy was 7.5 times lower (p = 1.7 × 10− 7) during hippus than the photomotor reflex, indicating a less costly activity. The application of the model to hippus and the photomotor reflex provided information on the autonomic nervous system basal state and adaptive reserve, respectively. These analyses allow better understanding of the autonomic nervous system activity in both clinical and athletic contexts.

Steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) have been widely studied because they provide high classification accuracy and information transfer rate (ITR) without requiring user training. To further enhance BCI performance, this study proposes a novel hybrid BCI that integrates single-channel SSVEP with the pupillary light reflex (PLR). Twelve healthy subjects participated in experiments involving three paradigms: SSVEP, PLR, and hybrid. Each subject completed a total of 90 trials (three blocks) for each of the four classes in every paradigm. The experimental protocols were identical across paradigms, except for differences in visual stimuli. The hybrid paradigm achieved an accuracy (ITR) of 95.70% (25.54 bpm) for four-class classification using a data length of 4 s across all subjects, representing increases of 8.54% (6.34 bpm) and 12.92% (8.91 bpm) compared with the SSVEP and PLR paradigms, respectively. In the poor performer group (SSVEP accuracy below 90%), the hybrid paradigm achieved an average accuracy (ITR) of 93.06% (23.03 bpm), showing an improvement of 11.67% (7.54 bpm) compared with the SSVEP paradigm (81.39% and 15.49 bpm). As the number of EEG channels increased from one to ten electrodes, the classification accuracy of the SSVEP paradigm improved from 87.15% to 91.28% (an increase of 4.13%). In contrast, the hybrid paradigm exhibited minimal change, with only a 0.55% increase (from 95.70% to 96.25%). These results suggest that the proposed hybrid BCI can serve as an effective approach to enhance overall BCI performance, which may contribute to the practical and widespread adoption of BCIs by improving usability and user experience.