Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models.

Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, they cannot accumulate knowledge relevant to the upcoming visual input. Consequently, the influence of such knowledge on how this input is processed remains underexplored. Here, we investigated this influence in the context of gaze control. We used sequences of static film frames arranged in a way that allowed us to compare eye movements to identical frames between two groups: a group that accumulated prior knowledge relevant to the situations depicted in these frames and a group that did not. We used a machine learning approach based on hidden Markov models fitted to individual scanpaths to demonstrate that the gaze patterns from the two groups differed systematically and, thereby, showed that recently accumulated prior knowledge contributes to gaze control. Next, we leveraged the interpretability of hidden Markov models to characterize these differences. Additionally, we report two unexpected and interesting caveats of our approach. Overall, our results highlight the importance of recently acquired prior knowledge for oculomotor control and the potential of hidden Markov models as a tool for investigating it

Computer models of saliency alone fail to predict subjective visual attention to landmarks during observed navigation

This study aimed to understand whether or not computer models of saliency could explain landmark saliency. An online survey was conducted and participants were asked to watch videos from a spatial navigation video game (Sea Hero Quest). Participants were asked to pay attention to the environments within which the boat was moving and to rate the perceived saliency of each landmark. In addition, state-of-the-art computer saliency models were used to objectively quantify landmark saliency. No significant relationship was found between objective and subjective saliency measures. This indicates that during passive observation of an environment while being navigated, current automated models of saliency fail to predict subjective reports of visual attention to landmarks

Personality Traits Do Not Predict How We Look at Faces

While personality has typically been considered to influence gaze behaviour, literature relating to the topic is mixed. Previously, we found no evidence of self-reported personality traits on preferred gaze duration between a participant and a person looking at them via a video. In this study, 77 of the original participants answered an in-depth follow-up survey containing a more comprehensive assessment of personality traits (Big Five Inventory) than was initially used, to check whether earlier findings were caused by the personality measure being too coarse. In addition to preferred mutual gaze duration, we also examined two other factors linked to personality traits: number of blinks and total fixation duration in the eye region of observed faces. No significant correlations were found between any of these measures and participant personality traits. We suggest that effects previously reported in the literature may stem from contextual differences or modulation of arousal

Entropy and a Sub-Group of Geometric Measures of Paths Predict the Navigability of an Environment

Despite extensive research on navigation, it remains unclear which features of an environment predict how difficult it will be to navigate. We analysed 478,170 trajectories from 10,626 participants who navigated 45 virtual environments in the research app-based game Sea Hero Quest. Virtual environments were designed to vary in a range of properties such as their layout, number of goals, visibility (varying fog) and map condition. We calculated 58 spatial measures grouped into four families: task-specific metrics, space syntax configurational metrics, space syntax geometric metrics, and general geometric metrics. We used Lasso, a variable selection method, to select the most predictive measures of navigation difficulty. Geometric features such as entropy, area of navigable space, number of rings and closeness centrality of path networks were among the most significant factors determining the navigational difficulty. By contrast a range of other measures did not predict difficulty, including measures of intelligibility. Unsurprisingly, other task-specific features (e.g. number of destinations) and fog also predicted navigation difficulty. These findings have implications for the study of spatial behaviour in ecological settings, as well as predicting human movements in different settings, such as complex buildings and transport networks and may aid the design of more navigable environments

Cultural determinants of the gap between self-estimated navigation ability and wayfinding performance: evidence from 46 countries

Cognitive abilities can vary widely. Some people excel in certain skills, others struggle. However, not all those who describe themselves as gifted are. One possible influence on self-estimates is the surrounding culture. Some cultures may amplify self-assurance and others cultivate humility. Past research has shown that people in different countries can be grouped into a set of consistent cultural clusters with similar values and tendencies, such as attitudes to masculinity or individualism. Here we explored whether such cultural dimensions might relate to the extent to which populations in 46 countries overestimate or underestimate their cognitive abilities in the domain of spatial navigation. Using the Sea Hero Quest navigation test and a large sample (N = 383,187) we found cultural clusters of countries tend to be similar in how they self-rate ability relative to their actual performance. Across the world population sampled, higher self-ratings were associated with better performance. However, at the national level, higher self-ratings as a nation were not associated with better performance as a nation. Germanic and Near East countries were found to be most overconfident in their abilities and Nordic countries to be most under-confident in their abilities. Gender stereotypes may play a role in mediating this pattern, with larger national positive attitudes to male stereotyped roles (Hofstede's masculinity dimension) associated with a greater overconfidence in performance at the national level. We also replicate, with higher precision than prior studies, evidence that older men tend to overestimate their navigation skill more than other groups. These findings give insight into how culture and demographics may impact self-estimates of our abilities

No link between handedness and spatial navigation: evidence from over 400 000 participants in 41 countries

There is an active debate concerning the association of handedness and spatial ability. Past studies used small sample sizes. 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 leftand right-handers across all countries. A small but growing gap in performance appears for participants over 64 years old, with left-handers outperforming right-handers. Further analysis, however, suggests that this gap is most likely due to selection bias. Overall, our study clarifies the factors associated with spatial ability and shows that left-handedness is not associated with either a benefit or a deficit in spatial ability

Entropy of city street networks linked to future spatial navigation ability

The cultural and geographical properties of the environment have been shown to deeply influence cognition and mental health1-6. Living near green spaces has been found to be strongly beneficial7-11, and urban residence has been associated with a higher risk of some psychiatric disorders12-14-although some studies suggest that dense socioeconomic networks found in larger cities provide a buffer against depression15. However, how the environment in which one grew up affects later cognitive abilities remains poorly understood. Here we used a cognitive task embedded in a video game16 to measure non-verbal spatial navigation ability in 397,162 people from 38 countries across the world. Overall, we found that people who grew up outside cities were better at navigation. More specifically, people were better at navigating in environments that were topologically similar to where they grew up. Growing up in cities with a low street network entropy (for example, Chicago) led to better results at video game levels with a regular layout, whereas growing up outside cities or in cities with a higher street network entropy (for example, Prague) led to better results at more entropic video game levels. This provides evidence of the effect of the environment on human cognition on a global scale, and highlights the importance of urban design in human cognition and brain function

Plasmonic Metasurface for Directional and Frequency-Selective Thermal Emission

International audienceIncandescent filaments and membranes are often used as infrared sources despite their low efficiency, broad angular emission, and lack of spectral selectivity. Here, we introduce a metasurface to control simultaneously the spectrum and the directivity of blackbody radiation. The plasmonic metasurface operates reliably at 600 °C with an emissivity higher than 0.85 in a narrow frequency band and in a narrow solid angle. This emitter paves the way for the development of compact, efficient, and cheap IR sources and gas detection systems

Réseau de neurones récurrent à attention pour la détection de lésions intestinales

International audienceCrohn's disease (CD) is a chronic inflammatory bowel disease , affecting young subjects, causing mucosal damage to the small intestine : erosions, ulcerations, edema and stenosis. The wireless capsule endosopy (WCE) is the best examination for their detection. The WCE generates approximately 50,000 images that are time-consuming for gastroenterologists to analyse. The objective of our work is therefore to develop a tool for the automatic recognition of mucosal lesions of CD in the small intestine. The algorithm is based on a network of convolutional neurons with attention. This was trained on a public database, GIANA, containing images of normal WCEs and with inflammatory and vascular lesions. Another database was used separately , CROHN-IPI, consisting of normal images and MC le-sions annotated by gastroenterologists from Nantes University Hospital. Preliminary results show that the algorithm trained on the 1800 GIANA images, is able to detect with an accuracy of 99,77% the pathological images. Concerning CROHN-IPI, the accuracy obtained on the 2500 images from 39 patients is 80.36%. This difference can be explained by the way the images in the database were selected (images of more obvious lesions in GIANA) or by the under-representation of certain pathologies in CROHN-IPI. In the future, a larger scale WCE annotation application will be developed to enrich CROHN-IPI.La maladie de Crohn (MC) est une maladie inflammatoire chronique intestinale, atteignant les sujets jeunes, provo-quant des lésions de l'intestin grêle : des érosions, des ul-cérations, de l'oedème et des sténoses. La vidéo-capsule endoscopique (VCE) est le meilleur examen permettant leur détection. La VCE génère environ 50000 images dont l'analyse par les gastro-entérologues est consommatrice de temps. L'objectif de notre travail est donc de dévelop-per un outil permettant la reconnaissance automatique des lésions de MC dans l'intestin grêle. L'algorithme est basé sur un réseau de neurones convolutifs à attention. Celui-ci a été entraîné sur une base de données publique, GIANA, contenant des images de VCE normales et avec des lésions inflammatoires et vasculaires. Une autre base a été utilisée séparément, CROHN-IPI, constituée d'images normales et de lésions de MC annotées par des gastro-entérologues du CHU de Nantes. Les résultats préliminaires montrent que l'algorithme entraîné sur les 1800 images de GIANA, est capable de distinguer avec une précision de 99,77% les images pathologiques des images non pathologiques. Concernant CROHN-IPI, la précision obtenue sur les 2500 images provenant de 39 patients est de 80,36%. Cet écart peut s'expliquer par la façon dont ont été sélectionnées les images de la base (images de lésions plus évidentes dans GIANA) ou encore par la sous-représentation de certaines pathologies dans CROHN-IPI. À l'avenir, une application d'annotation de VCE à plus grande échelle sera dévelop-pée pour enrichir CROHN-IPI. Mots Clef Apprentissage profond, réseau récurrent à attention, mala-die de Crohn, classification d'images médicales