Scanpath modeling and classification with Hidden Markov Models

How people look at visual information reveals fundamental information about them; their interests and their states of mind. Previous studies showed that scanpath, i.e., the sequence of eye movements made by an observer exploring a visual stimulus, can be used to infer observer-related (e.g., task at hand) and stimuli-related (e.g., image semantic category) information. However, eye movements are complex signals and many of these studies rely on limited gaze descriptors and bespoke datasets. Here, we provide a turnkey method for scanpath modeling and classification. This method relies on variational hidden Markov models (HMMs) and discriminant analysis (DA). HMMs encapsulate the dynamic and individualistic dimensions of gaze behavior, allowing DA to capture systematic patterns diagnostic of a given class of observers and/or stimuli. We test our approach on two very different datasets. Firstly, we use fixations recorded while viewing 800 static natural scene images, and infer an observer-related characteristic: the task at hand. We achieve an average of 55.9% correct classification rate (chance = 33%). We show that correct classification rates positively correlate with the number of salient regions present in the stimuli. Secondly, we use eye positions recorded while viewing 15 conversational videos, and infer a stimulus-related characteristic: the presence or absence of original soundtrack. We achieve an average 81.2% correct classification rate (chance = 50%). HMMs allow to integrate bottom-up, top-down, and oculomotor influences into a single model of gaze behavior. This synergistic approach between behavior and machine learning will open new avenues for simple quantification of gazing behavior. We release SMAC with HMM, a Matlab toolbox freely available to the community under an open-source license agreement.published_or_final_versio

Prior knowledge about events depicted in scenes decreases oculomotor exploration.

The visual input that the eyes receive usually contains temporally continuous information about unfolding events. Therefore, humans can accumulate knowledge about their current environment. Typical studies on scene perception, however, involve presenting multiple unrelated images and thereby render this accumulation unnecessary. Our study, instead, facilitated it and explored its effects. Specifically, we investigated how recently-accumulated prior knowledge affects gaze behavior. Participants viewed sequences of static film frames that contained several 'context frames' followed by a 'critical frame'. The context frames showed either events from which the situation depicted in the critical frame naturally followed, or events unrelated to this situation. Therefore, participants viewed identical critical frames while possessing prior knowledge that was either relevant or irrelevant to the frames' content. In the former case, participants' gaze behavior was slightly more exploratory, as revealed by seven gaze characteristics we analyzed. This result demonstrates that recently-gained prior knowledge reduces exploratory eye movements

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

La surveillance phytosanitaire de la forêt en région méditerranéenne

La stratégie de lutte est améliorée par une surveillance permanente dépendant d'un réseau en 3 éléments : observation, technique, administration. L'action des principaux insectes ravageurs et agents pathogènes est évoquée

In the eye of the beholder? Oxytocin effects on eye movements in schizophrenia

Background Individuals with schizophrenia have difficulty in extracting salient information from faces. Eye-tracking studies have reported that these individuals demonstrate reduced exploratory viewing behaviour (i.e. reduced number of fixations and shorter scan paths) compared to healthy controls. Oxytocin has previously been demonstrated to exert pro-social effects and modulate eye gaze during face exploration. In this study, we tested whether oxytocin has an effect on visual attention in patients with schizophrenia. Methods Nineteen male participants with schizophrenia received intranasal oxytocin 40UI or placebo in a double-blind, placebo-controlled, crossover fashion during two visits separated by seven days. They engaged in a free-viewing eye-tracking task, exploring images of Caucasian men displaying angry, happy, and neutral emotional expressions; and control images of animate and inanimate stimuli. Eye-tracking parameters included: total number of fixations, mean duration of fixations, dispersion, and saccade amplitudes. Results We found a main effect of treatment, whereby oxytocin increased the total number of fixations, dispersion, and saccade amplitudes, while decreasing the duration of fixations compared to placebo. This effect, however, was non-specific to facial stimuli. When restricting the analysis to facial images only, we found the same effect. In addition, oxytocin modulated fixation rates in the eye and nasion regions. Discussion This is the first study to explore the effects of oxytocin on eye gaze in schizophrenia. Oxytocin had enhanced exploratory viewing behaviour in response to both facial and inanimate control stimuli. We suggest that the acute administration of intranasal oxytocin may have the potential to enhance visual attention in schizophrenia

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