Human visual attention with context-specific top-down saliency

In the present study, we examined the eye movement behaviour of children and adults looking at five Van Gogh paintings in the Van Gogh Museum, Amsterdam. The goal of the study was to determine the role of top-down and bottom-up attentional processes in the first stages of participants’ aesthetic experience. Bottom-up processes were quantified by determining a salience map for each painting. Top-down processing was manipulated by first allowing participants to view the paintings freely, then providing background information about each painting, and then allowing them to view the paintings a second time. The salience analysis showed differences between the eye movement behaviour of children and adults, and differences between the two phases. In the children, the first five fixations during the free viewing phase were strongly related to visually salient features of the paintings—indicating a strong role for bottom-up factors. In the second phase, after children had received background information, top-down factors played a more prominent role. By contrast, adults’ observed patterns were similar in both phases, indicating that bottom-up processes did not play a major role when they viewed the paintings. In the second phase, children and adults both spent more time looking at regions that were mentioned in the background information. This effect was greater for adults than for children, confirming the notion that adults, when viewing paintings, rely much more on top-down processing than children.

Like many dual process theories in the psychological sciences, existing models of prospective memory (i.e., remembering to execute future intentions) emphasize the role of singular top-down or bottom-up processes that act in isolation. We argue that top-down and bottom-up processes are interconnected and dynamically interact to support prospective memory. We elaborate on this dynamic multiprocess framework by focusing on recent behavioral, neuroimaging, and eye-tracking research that demonstrated the dynamic nature of monitoring (top-down) and spontaneous retrieval (bottom-up) processes in relation to contextual factors, metacognition, and individual differences. We conclude that identifying how dual processes interact with environmental and individual difference factors is crucial for advancing understanding of cognition and behavior.

Wetland habitats are changing under multiple anthropogenic pressures. Nutrient leakage and pollution modify physico-chemical state of wetlands and affect the ecosystem through bottom-up processes, while alien predators affect the ecosystems in a top-down manner. Boreal wetlands are important breeding areas for several waterbird species, the abundances of which potentially reflect both bottom-up and top-down ecosystem processes. Here, we use long-term national monitoring data gathered from c. 130 waterbird breeding sites in Finland from the 1980s to the 2020s. We hypothesised that the physico-chemical state of the waters and increasing alien predator abundance both play a role in steering the waterbird population trends. We set out to test this hypothesis by relating population changes of 17 waterbird species to changes in water chemistry and to regional alien predator indices while allowing species-specific effects to vary with foraging niche (dabblers, invertivore divers, piscivorous divers, herbivores), nesting site, female mass and habitat (oligotrophic, eutrophic). We found niche and nesting site-specific, habitat-dependent changes in waterbird numbers. While the associations with higher phosphorus levels and browning water were in overall positive at the oligotrophic lakes, the numbers of invertivore and piscivore diving ducks were most strongly negatively associated with higher phosphorus levels and browning water at the eutrophic lakes. Furthermore, increased pH levels benefitted piscivores. Invertivore diving duck species nesting on the wetlands had declined most on sites with high alien predator indices. Large herbivorous species and species preferring oligotrophic lakes seem to be successful. We conclude that the large-scale breeding waterbird decline in Finland is closely connected to both bottom-up and top-down processes, where negative associations are emphasised especially at eutrophic lakes. Niche-, nest site- and habitat-specific management actions are required to conserve declining waterbird populations. Managing wetlands on catchments level together with alien predator control may provide important approaches to future wetland management.

Human visual attention is strongly influenced by rewards, affecting both top-down and bottom-up attentional processes. The value-driven attentional capture (VDAC) paradigm, introduced by Anderson et al. (2011b), has had a significant impact on the field of visual attention. In particular, it has facilitated scientific understanding of how a previously task-irrelevant stimulus (e.g., a red circle) paired with monetary or social rewards can capture attention when presented as a distractor (e.g., red) in subsequent non-reward shape-search tasks (e.g., diamond). However, recent work using the traditional VDAC paradigm has produced controversial research findings. The present systematic review, including 23 publications (52 experiments), investigated whether a) reward-associated features might influence attentional capture subsequently, and b) the VDAC paradigm provides a reliable experimental approach to examine the reward-attention relationship. The analysis of these VDAC experiments has revealed methodological concerns, including history-selection confounds, issues with color-reward associations, color-luminance effects and preferences, and low statistical power. Reward-associated features influence attentional capture, however, the current VDAC paradigm provides weak experimental effects. Critically, the literature review provides a concise evaluation of the VDAC paradigm and offers potential directions for further experimental research.

Understanding human visual attention in immersive virtual reality (VR) is crucial for many important applications, including gaze prediction, gaze guidance, and gaze-contingent rendering. However, previous works on visual attention analysis typically only explored one specific VR task and paid less attention to the differences between different tasks. Moreover, existing task recognition methods typically focused on 2D viewing conditions and only explored the effectiveness of human eye movements. We first collect eye and head movements of 30 participants performing four tasks, i.e., Free viewing, Visual search, Saliency, and Track, in 15 360-degree VR videos. Using this dataset, we analyze the patterns of human eye and head movements and reveal significant differences across different tasks in terms of fixation duration, saccade amplitude, head rotation velocity, and eye-head coordination. We then propose EHTask - a novel learning-based method that employs eye and head movements to recognize user tasks in VR. We show that our method significantly outperforms the state-of-the-art methods derived from 2D viewing conditions both on our dataset (accuracy of 84.4% versus 62.8%) and on a real-world dataset ( 61.9% versus 44.1%). As such, our work provides meaningful insights into human visual attention under different VR tasks and guides future work on recognizing user tasks in VR.

Apex predators perform important functions that regulate ecosystems worldwide. However, little is known about how ecosystem regulation by predators is influenced by human activities. In particular, how important are top-down effects of predators relative to direct and indirect human-mediated bottom-up and top-down processes? Combining data on species' occurrence from camera traps and hunting records, we aimed to quantify the relative effects of top-down and bottom-up processes in shaping predator and prey distributions in a human-dominated landscape in Transylvania, Romania. By global standards this system is diverse, including apex predators (brown bear and wolf), mesopredators (red fox) and large herbivores (roe and red deer). Humans and free-ranging dogs represent additional predators in the system. Using structural equation modelling, we found that apex predators suppress lower trophic levels, especially herbivores. However, direct and indirect top-down effects of humans affected the ecosystem more strongly, influencing species at all trophic levels. Our study highlights the need to explicitly embed humans and their influences within trophic cascade theory. This will greatly expand our understanding of species interactions in human-modified landscapes, which compose the majority of the Earth's terrestrial surface.

Travelling Educational Change and Cultural Diversity in the Czech Republic

Neurophysiological and behavioural research has linked observational practice (OP) to a mirroring mechanism encompassing the action-observation network (AON). Although, the original findings indicate that biological stimuli alone activate the AON, recent evidence has shown sensitivity to non-biological stimuli. Thus, the AON is suggested to be influenced by interacting bottom-up and top-down processes. In this review, we describe the multi-functional properties of the AON and discuss the implications for OP and subsequent motor learning.

Hierarchical GAN-Tree and Bi-Directional Capsules for multi-label image classification

In order to perform cooperative tasks smoothly, a robot needs to behave properly with the other robot or human by observing them and recognizing their action. In order for such recognition, visual information captured by a camera which is mounted on the robot should be processed efficiently. The authors consider that the detection of regions-of-interest like human visual attention is important and it is effective to investigate properties of gazing regions observed in human eye movement toward the detection of valid regions by the robot. In this study, the gaze points of subject are measured in observation of the movies in which a robot moves toward a target object in some objects or moves its hand to manipulate an object. The regions-of-interest were extracted from the gaze points and characteristics of the regions were analyzed. The result suggested that the regions-of-interest were repeatedly concentrated on the moving robot as well as target object based on a proactive anticipation by a subject for the robot motion and selected object.

We present a method for automatically determining the perceptual importance of different regions in an image. The algorithm is based on human visual attention and eye movement characteristics. Several features known to influence human visual attention are evaluated for each region of a segmented image to produce an importance value for each factor and region. These are combined to produce an Importance Map, which classifies each region of the image in relation to its perceptual importance. Results shown indicate that the calculated Importance Maps correlate well with human perception of visually important regions. The Importance Maps can be used in a variety of applications, including compression, machine vision, and image databases. Our technique is computationally efficient and flexible, and can easily be extended to specific applications.

We humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation.

Humans can easily find a specific category of objects in a complex scene, simulating this mechanism has both scientific and economic impact. The existing research usually focuses on where humans will look, overlooking the effects of the category of objects on visual attention. In this paper, we proposed a computational visual model aimed at mimicking human visual attention while they were looking for a specific category of objects. First, we proposed a machine learning-based Probabilistic Latent Semantic Analysis (PLSA) model to recognize the generic category of objects and find their locations. The PLSA model parameters are learned in a supervised manner. Then, the category and location information of objects are used as high-level information, together with low-level features of the same objects, to train a Support Vector Regression (SVR) model. With this SVR model, we can predict the visual attention for a specific category. To demonstrate the effectiveness and applicability of the proposed method, we conducted comprehensive experiments. The results show that the model can provide a reasonable estimate for human visual attention under the visual search task for a specific category. Besides, we evaluated the proposed model's ability to discover a specific category of objects and their locations on the natural image dataset. The results show that besides facilitating the human visual attention mechanism research, the proposed model can also be applied to find a specific category of objects from a set of images.

We propose a method of predicting human egocentric visual attention using bottom-up visual saliency and egomotion information. Computational models of visual saliency are often employed to predict human attention; however, its mechanism and effectiveness have not been fully explored in egocentric vision. The purpose of our framework is to compute attention maps from an egocentric video that can be used to infer a person’s visual attention. In addition to a standard visual saliency model, two kinds of attention maps are computed based on a camera’s rotation velocity and direction of movement. These rotation-based and translation-based attention maps are aggregated with a bottom-up saliency map to enhance the accuracy with which the person’s gaze positions can be predicted. The efficiency of the proposed framework was examined in real environments by using a head-mounted gaze tracker, and we found that the egomotion-based attention maps contributed to accurately predicting human visual attention.

The mobile robots are equipped with sensitive audio visual sensors, usually microphone arrays and video cameras. They are the main sources of audio visual information to perform suitable mobile robots navigation tasks, modeling the human audio visual perception. The results from the audio and visual perception algorithms are widely used, separate or in conjunction (audio visual perception) in the mobile robots navigation, for example to control mobile robots motion in applications like people and objects tracking, surveillance systems, etc. The effectiveness and precision of the audio visual perception methods in the mobile robots navigation can be enhanced combining audio visual perception with audio visual attention. Sufficient relative knowledge exists, describing the phenomena of human audio and visual attention. Such approaches are usually based on a lot of physiological, psychological, medical and technical experimental investigations relating the human audio and visual attention, with the human audio and visual perception with the leading role of the brain activity. Of course, the results from these investigations are very important, but not sufficient for the mobile robots audio visual attention modeling, mainly because of brain missing in mobile robots audio visual perception systems. Therefore, in this chapter is proposed to use the existing definitions and models for human audio and visual attention, adapting them to the models of mobile robots audio and visual attention and combining with the results from the mobile robots audio and visual perception in the mobile robots navigation tasks.