Semantic guidance of eye movements in real-world scenes

Finding the meaning in meaning maps: Quantifying the roles of semantic and non-semantic scene information in guiding visual attention

In real-world vision, people prioritise the most informative scene regions via eye-movements. According to the cognitive guidance theory of visual attention, viewers allocate visual attention to those parts of the scene that are expected to be the most informative. The expected information of a scene region is coded in the semantic distribution of that scene. Meaning maps have been proposed to capture the spatial distribution of local scene semantics in order to test cognitive guidance theories of attention. Notwithstanding the success of meaning maps, the reason for their success has been contested. This has led to at least two possible explanations for the success of meaning maps in predicting visual attention. On the one hand, meaning maps might measure scene semantics. On the other hand, meaning maps might measure scene features, overlapping with, but distinct from, scene semantics. This study aims to disentangle these two sources of information by considering both conceptual information and non-semantic scene entropy simultaneously. We found that both semantic and non-semantic information is captured by meaning maps, but scene entropy accounted for more unique variance in the success of meaning maps than conceptual information. Additionally, some explained variance was unaccounted for by either source of information. Thus, although meaning maps may index some aspect of semantic information, their success seems to be better explained by non-semantic information. We conclude that meaning maps may not yet be a good tool to test cognitive guidance theories of attention in general, since they capture non-semantic aspects of local semantic density and only a small portion of conceptual information. Rather, we suggest that researchers should better define the exact aspect of cognitive guidance theories they wish to test and then use the tool that best captures that desired semantic information. As it stands, the semantic information contained in meaning maps seems too ambiguous to draw strong conclusions about how and when semantic information guides visual attention.

Exploring the Semantic-Inconsistency Effect in Scenes Using a Continuous Measure of Linguistic-Semantic Similarity

Viewers use contextual information to visually explore complex scenes. Object recognition is facilitated by exploiting object-scene relations (which objects are expected in a given scene) and object-object relations (which objects are expected because of the occurrence of other objects). Semantically inconsistent objects deviate from these expectations, so they tend to capture viewers' attention (the semantic-inconsistency effect). Some objects fit the identity of a scene more or less than others, yet semantic inconsistencies have hitherto been operationalized as binary (consistent vs. inconsistent). In an eye-tracking experiment (N = 21 adults), we study the semantic-inconsistency effect in a continuous manner by using the linguistic-semantic similarity of an object to the scene category and to other objects in the scene. We found that both highly consistent and highly inconsistent objects are viewed more than other objects (U-shaped relationship), revealing that the (in)consistency effect is more than a simple binary classification.

Foreground bias: Semantic consistency effects modulated when searching across depth

When processing visual scenes, we tend to prioritize information in the foreground, often at the expense of background information. The foreground bias has been supported by data demonstrating that there are more fixations to foreground, and faster and more accurate detection of targets embedded in foreground. However, it is also known that semantic consistency is associated with more efficient search. Here, we examined whether semantic context interacts with foreground prioritization, either amplifying or mitigating the effect of target semantic consistency. For each scene, targets were placed in the foreground or background and were either semantically consistent or inconsistent with the context of immediately surrounding depth region. Results indicated faster response times (RTs) for foreground and semantically consistent targets, replicating established effects. More importantly, we found the magnitude of the semantic consistency effect was significantly smaller in the foreground than background region. To examine the robustness of this effect, in Experiment 2, we strengthened the reliability of semantics by increasing the proportion of targets consistent with the scene region to 80%. We found the overall results pattern to replicate the incongruous effect of semantic consistency across depth observed in Experiment 1. This suggests foreground bias modulates the effects of semantics so that performance is less impacted in near space.

Predicting attentional allocation in real-world environments: The need to investigate crossmodal semantic guidance

Real-world environments are multisensory, meaningful, and highly complex. To parse these environments in a highly efficient manner, a subset of this information must be selected both within and across modalities. However, the bulk of attention research has been conducted within sensory modalities, with a particular focus on vision. Visual attention research has made great strides, with over a century of research methodically identifying the underlying mechanisms that allow us to select critical visual information. Spatial attention, attention to features, and object-based attention have all been studied extensively. More recently, research has established semantics (meaning) as a key component to allocating attention in real-world scenes, with the meaning of an item or environment affecting visual attentional selection. However, a full understanding of how semantic information modulates real-world attention requires studying more than vision in isolation. The world provides semantic information across all senses, but with this extra information comes greater complexity. Here, we summarize visual attention (including semantic-based visual attention), crossmodal attention, and argue for the importance of studying crossmodal semantic guidance of attention. This article is categorized under: Psychology > Attention Psychology > Perception and Psychophysics.

Quantifying task-related gaze

Competing theories attempt to explain what guides eye movements when exploring natural scenes: bottom-up image salience and top-down semantic salience. In one study, we apply language-based analyses to quantify the well-known observation that task influences gaze in natural scenes. Subjects viewed ten scenes as if they were performing one of two tasks. We found that the semantic similarity between the task and the labels of objects in the scenes captured the task-dependence of gaze (t(39) = 13.083; p < 0.001). In another study, we examined whether image salience or semantic salience better predicts gaze during a search task, and if viewing strategies are affected by searching for targets of high or low semantic relevance to the scene. Subjects searched 100 scenes for a high- or low-relevance object. We found that image salience becomes a worse predictor of gaze across successive fixations, while semantic salience remains a consistent predictor (X2(1, N=40) = 75.148, p < .001). Furthermore, we found that semantic salience decreased as object relevance decreased (t(39) = 2.304; p = .027). These results suggest that semantic salience is a useful predictor of gaze during task-related scene viewing, and that even in target-absent trials, gaze is modulated by the relevance of a search target to the scene in which it might be located.

Representational structures as a unifying framework for attention

Our visual system consciously processes only a subset of the incoming information. Selective attention allows us to prioritize relevant inputs, and can be allocated to features, locations, and objects. Recent advances in feature-based attention suggest that several selection principles are shared across these domains and that many differences between the effects of attention on perceptual processing can be explained by differences in the underlying representational structures. Moving forward, it can thus be useful to assess how attention changes the structure of the representational spaces over which it operates, which include the spatial organization, feature maps, and object-based coding in visual cortex. This will ultimately add to our understanding of how attention changes the flow of visual information processing more broadly.

A Comparison of Head Movement Classification Methods

To understand human behavior, it is essential to study it in the context of natural movement in immersive, three-dimensional environments. Virtual reality (VR), with head-mounted displays, offers an unprecedented compromise between ecological validity and experimental control. However, such technological advancements mean that new data streams will become more widely available, and therefore, a need arises to standardize methodologies by which these streams are analyzed. One such data stream is that of head position and rotation tracking, now made easily available from head-mounted systems. The current study presents five candidate algorithms of varying complexity for classifying head movements. Each algorithm is compared against human rater classifications and graded based on the overall agreement as well as biases in metrics such as movement onset/offset time and movement amplitude. Finally, we conclude this article by offering recommendations for the best practices and considerations for VR researchers looking to incorporate head movement analysis in their future studies.

Visual search patterns during exploration of naturalistic scenes are driven by saliency cues in individuals with cerebral visual impairment

We investigated the relative influence of image salience and image semantics during the visual search of naturalistic scenes, comparing performance in individuals with cerebral visual impairment (CVI) and controls with neurotypical development. Participants searched for a prompted target presented as either an image or text cue. Success rate and reaction time were collected, and gaze behavior was recorded with an eye tracker. A receiver operating characteristic (ROC) analysis compared the distribution of individual gaze landings based on predictions of image salience (using Graph-Based Visual Saliency) and image semantics (using Global Vectors for Word Representations combined with Linguistic Analysis of Semantic Salience) models. CVI participants were less likely and were slower in finding the target. Their visual search behavior was also associated with a larger visual search area and greater number of fixations. ROC scores were also lower in CVI compared to controls for both model predictions. Furthermore, search strategies in the CVI group were not affected by cue type, although search times and accuracy showed a significant correlation with verbal IQ scores for text-cued searches. These results suggest that visual search patterns in CVI are driven mainly by image salience and provide further characterization of higher-order processing deficits observed in this population.

Efficient quantization of painting images by relevant colors

Realistic images often contain complex variations in color, which can make economical descriptions difficult. Yet human observers can readily reduce the number of colors in paintings to a small proportion they judge as relevant. These relevant colors provide a way to simplify images by effectively quantizing them. The aim here was to estimate the information captured by this process and to compare it with algorithmic estimates of the maximum information possible by colorimetric and general optimization methods. The images tested were of 20 conventionally representational paintings. Information was quantified by Shannon's mutual information. It was found that the estimated mutual information in observers' choices reached about 90% of the algorithmic maxima. For comparison, JPEG compression delivered somewhat less. Observers seem to be efficient at effectively quantizing colored images, an ability that may have applications in the real world.

Attention and platypuses

This perspective piece discusses a set of attentional phenomena that are not easily accommodated within current theories of attentional selection. We call these phenomena attentional platypuses, as they allude to an observation that within biological taxonomies the platypus does not fit into either mammal or bird categories. Similarly, attentional phenomena that do not fit neatly within current attentional models suggest that current models are in need of a revision. We list a few instances of the "attentional platypuses" and then offer a new approach, that we term dynamically weighted prioritization, stipulating that multiple factors impinge onto the attentional priority map, each with a corresponding weight. The interaction between factors and their corresponding weights determines the current state of the priority map which subsequently constrains/guides attentional allocation. We propose that this new approach should be considered as a supplement to existing models of attention, especially those that emphasize categorical organizations. This article is categorized under: Psychology > Attention Psychology > Perception and Psychophysics Neuroscience > Cognition.

Low-level factors increase gaze-guidance under cognitive load: A comparison of image-salience and semantic-salience models

Growing evidence links eye movements and cognitive functioning, however there is debate concerning what image content is fixated in natural scenes. Competing approaches have argued that low-level/feedforward and high-level/feedback factors contribute to gaze-guidance. We used one low-level model (Graph Based Visual Salience, GBVS) and a novel language-based high-level model (Global Vectors for Word Representation, GloVe) to predict gaze locations in a natural image search task, and we examined how fixated locations during this task vary under increasing levels of cognitive load. Participants (N = 30) freely viewed a series of 100 natural scenes for 10 seconds each. Between scenes, subjects identified a target object from the scene a specified number of trials (N) back among three distracter objects of the same type but from alternate scenes. The N-back was adaptive: N-back increased following two correct trials and decreased following one incorrect trial. Receiver operating characteristic (ROC) analysis of gaze locations showed that as cognitive load increased, there was a significant increase in prediction power for GBVS, but not for GloVe. Similarly, there was no significant difference in the area under the ROC between the minimum and maximum N-back achieved across subjects for GloVe (t(29) = -1.062, p = 0.297), while there was a cohesive upwards trend for GBVS (t(29) = -1.975, p = .058), although not significant. A permutation analysis showed that gaze locations were correlated with GBVS indicating that salient features were more likely to be fixated. However, gaze locations were anti-correlated with GloVe, indicating that objects with low semantic consistency with the scene were more likely to be fixated. These results suggest that fixations are drawn towards salient low-level image features and this bias increases with cognitive load. Additionally, there is a bias towards fixating improbable objects that does not vary under increasing levels of cognitive load.

Hierarchical organization of objects in scenes is reflected in mental representations of objects

The arrangement of objects in scenes follows certain rules ("Scene Grammar"), which we exploit to perceive and interact efficiently with our environment. We have proposed that Scene Grammar is hierarchically organized: scenes are divided into clusters of objects ("phrases", e.g., the sink phrase); within every phrase, one object ("anchor", e.g., the sink) holds strong predictions about identity and position of other objects ("local objects", e.g., a toothbrush). To investigate if this hierarchy is reflected in the mental representations of objects, we collected pairwise similarity judgments for everyday object pictures and for the corresponding words. Similarity judgments were stronger not only for object pairs appearing in the same scene, but also object pairs appearing within the same phrase of the same scene as opposed to appearing in different phrases of the same scene. Besides, object pairs with the same status in the scenes (i.e., being both anchors or both local objects) were judged as more similar than pairs of different status. Comparing effects between pictures and words, we found similar, significant impact of scene hierarchy on the organization of mental representation of objects, independent of stimulus modality. We conclude that the hierarchical structure of visual environment is incorporated into abstract, domain general mental representations of the world.

How much is a cow like a meow? A novel database of human judgements of audiovisual semantic relatedness

Semantic information about objects, events, and scenes influences how humans perceive, interact with, and navigate the world. The semantic information about any object or event can be highly complex and frequently draws on multiple sensory modalities, which makes it difficult to quantify. Past studies have primarily relied on either a simplified binary classification of semantic relatedness based on category or on algorithmic values based on text corpora rather than human perceptual experience and judgement. With the aim to further accelerate research into multisensory semantics, we created a constrained audiovisual stimulus set and derived similarity ratings between items within three categories (animals, instruments, household items). A set of 140 participants provided similarity judgments between sounds and images. Participants either heard a sound (e.g., a meow) and judged which of two pictures of objects (e.g., a picture of a dog and a duck) it was more similar to, or saw a picture (e.g., a picture of a duck) and selected which of two sounds it was more similar to (e.g., a bark or a meow). Judgements were then used to calculate similarity values of any given cross-modal pair. An additional 140 participants provided word judgement to calculate similarity of word-word pairs. The derived and reported similarity judgements reflect a range of semantic similarities across three categories and items, and highlight similarities and differences among similarity judgments between modalities. We make the derived similarity values available in a database format to the research community to be used as a measure of semantic relatedness in cognitive psychology experiments, enabling more robust studies of semantics in audiovisual environments.

Meaning maps detect the removal of local semantic scene content but deep saliency models do not

Meaning mapping uses human raters to estimate different semantic features in scenes, and has been a useful tool in demonstrating the important role semantics play in guiding attention. However, recent work has argued that meaning maps do not capture semantic content, but like deep learning models of scene attention, represent only semantically-neutral image features. In the present study, we directly tested this hypothesis using a diffeomorphic image transformation that is designed to remove the meaning of an image region while preserving its image features. Specifically, we tested whether meaning maps and three state-of-the-art deep learning models were sensitive to the loss of semantic content in this critical diffeomorphed scene region. The results were clear: meaning maps generated by human raters showed a large decrease in the diffeomorphed scene regions, while all three deep saliency models showed a moderate increase in the diffeomorphed scene regions. These results demonstrate that meaning maps reflect local semantic content in scenes while deep saliency models do something else. We conclude the meaning mapping approach is an effective tool for estimating semantic content in scenes.

Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps

Semantic information is important in eye movement control. An important semantic influence on gaze guidance relates to object-scene relationships: objects that are semantically inconsistent with the scene attract more fixations than consistent objects. One interpretation of this effect is that fixations are driven toward inconsistent objects because they are semantically more informative. We tested this explanation using contextualized meaning maps, a method that is based on crowd-sourced ratings to quantify the spatial distribution of context-sensitive “meaning” in images. In Experiment 1, we compared gaze data and contextualized meaning maps for images, in which objects-scene consistency was manipulated. Observers fixated more on inconsistent versus consistent objects. However, contextualized meaning maps did not assign higher meaning to image regions that contained semantic inconsistencies. In Experiment 2, a large number of raters evaluated image-regions, which were deliberately selected for their content and expected meaningfulness. The results suggest that the same scene locations were experienced as slightly less meaningful when they contained inconsistent compared to consistent objects. In summary, we demonstrated that — in the context of our rating task — semantically inconsistent objects are experienced as less meaningful than their consistent counterparts and that contextualized meaning maps do not capture prototypical influences of image meaning on gaze guidance.

Crossmodal Semantic Congruence Interacts with Object Contextual Consistency in Complex Visual Scenes to Enhance Short-Term Memory Performance

Object sounds can enhance the attentional selection and perceptual processing of semantically-related visual stimuli. However, it is currently unknown whether crossmodal semantic congruence also affects the post-perceptual stages of information processing, such as short-term memory (STM), and whether this effect is modulated by the object consistency with the background visual scene. In two experiments, participants viewed everyday visual scenes for 500 ms while listening to an object sound, which could either be semantically related to the object that served as the STM target at retrieval or not. This defined crossmodal semantically cued vs. uncued targets. The target was either in- or out-of-context with respect to the background visual scene. After a maintenance period of 2000 ms, the target was presented in isolation against a neutral background, in either the same or different spatial position as in the original scene. The participants judged the same vs. different position of the object and then provided a confidence judgment concerning the certainty of their response. The results revealed greater accuracy when judging the spatial position of targets paired with a semantically congruent object sound at encoding. This crossmodal facilitatory effect was modulated by whether the target object was in- or out-of-context with respect to the background scene, with out-of-context targets reducing the facilitatory effect of object sounds. Overall, these findings suggest that the presence of the object sound at encoding facilitated the selection and processing of the semantically related visual stimuli, but this effect depends on the semantic configuration of the visual scene.

Saliency-Based Gaze Visualization for Eye Movement Analysis

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.

The importance of peripheral vision when searching 3D real-world scenes: A gaze-contingent study in virtual reality

Visual search in natural scenes is a complex task relying on peripheral vision to detect potential targets and central vision to verify them. The segregation of the visual fields has been particularly established by on-screen experiments. We conducted a gaze-contingent experiment in virtual reality in order to test how the perceived roles of central and peripheral visions translated to more natural settings. The use of everyday scenes in virtual reality allowed us to study visual attention by implementing a fairly ecological protocol that cannot be implemented in the real world. Central or peripheral vision was masked during visual search, with target objects selected according to scene semantic rules. Analyzing the resulting search behavior, we found that target objects that were not spatially constrained to a probable location within the scene impacted search measures negatively. Our results diverge from on-screen studies in that search performances were only slightly affected by central vision loss. In particular, a central mask did not impact verification times when the target was grammatically constrained to an anchor object. Our findings demonstrates that the role of central vision (up to 6 degrees of eccentricities) in identifying objects in natural scenes seems to be minor, while the role of peripheral preprocessing of targets in immersive real-world searches may have been underestimated by on-screen experiments.

Cognitive load influences oculomotor behavior in natural scenes

Cognitive neuroscience researchers have identified relationships between cognitive load and eye movement behavior that are consistent with oculomotor biomarkers for neurological disorders. We develop an adaptive visual search paradigm that manipulates task difficulty and examine the effect of cognitive load on oculomotor behavior in healthy young adults. Participants (N = 30) free-viewed a sequence of 100 natural scenes for 10 s each, while their eye movements were recorded. After each image, participants completed a 4 alternative forced choice task in which they selected a target object from one of the previously viewed scenes, among 3 distracters of the same object type but from alternate scenes. Following two correct responses, the target object was selected from an image increasingly farther back (N-back) in the image stream; following an incorrect response, N decreased by 1. N-back thus quantifies and individualizes cognitive load. The results show that response latencies increased as N-back increased, and pupil diameter increased with N-back, before decreasing at very high N-back. These findings are consistent with previous studies and confirm that this paradigm was successful in actively engaging working memory, and successfully adapts task difficulty to individual subject's skill levels. We hypothesized that oculomotor behavior would covary with cognitive load. We found that as cognitive load increased, there was a significant decrease in the number of fixations and saccades. Furthermore, the total duration of saccades decreased with the number of events, while the total duration of fixations remained constant, suggesting that as cognitive load increased, subjects made fewer, longer fixations. These results suggest that cognitive load can be tracked with an adaptive visual search task, and that oculomotor strategies are affected as a result of greater cognitive demand in healthy adults.

The Linguistic Analysis of Scene Semantics: LASS

In this paper, we define a new method for analyzing object-scene contextual relationships using computational linguistics: Linguistic Analysis of Scene Semantics, or LASS. LASS uses linguistic semantic similarity relationships between scene object and context labels embedded in a vector-space language model: Facebook Research's fastText. Importantly, the use of fastText permits semantic similarity score calculation between any set of strings and thus elements of any set of image data for which labels are available. Scene semantic similarity scores are then embedded in object segmentation mask locations in the image, creating a semantic similarity map. LASS can also be fully automated by generating context and object labels, as well as object segmentation masks, using deep learning. We compare semantic similarity maps between human- and neural network-generated annotations on a corpus of images taken from the LabelMe database. Semantic similarity maps produced by the fully automated LASS have a number of desirable properties, while maintaining a high degree of spatial and semantic similarity to them. Finally, we use LASS to evaluate the distribution of semantically consistent scene elements in space. Both show relatively uniform distributions of semantic relatedness to scene context, suggesting that contextually appropriate objects are likely to be found in all image regions. Taken together, these results suggest that LASS is accurate, automatic, flexible, and useful in a number of research contexts such as scene grammar and novelty detection.

The meaning and structure of scenes

We live in a rich, three dimensional world with complex arrangements of meaningful objects. For decades, however, theories of visual attention and perception have been based on findings generated from lines and color patches. While these theories have been indispensable for our field, the time has come to move on from this rather impoverished view of the world and (at least try to) get closer to the real thing. After all, our visual environment consists of objects that we not only look at, but constantly interact with. Having incorporated the meaning and structure of scenes, i.e. its "grammar", then allows us to easily understand objects and scenes we have never encountered before. Studying this grammar provides us with the fascinating opportunity to gain new insights into the complex workings of attention, perception, and cognition. In this review, I will discuss how the meaning and the complex, yet predictive structure of real-world scenes influence attention allocation, search, and object identification.

Assessing visual search performance using a novel dynamic naturalistic scene

Daily activities require the constant searching and tracking of visual targets in dynamic and complex scenes. Classic work assessing visual search performance has been dominated by the use of simple geometric shapes, patterns, and static backgrounds. Recently, there has been a shift toward investigating visual search in more naturalistic dynamic scenes using virtual reality (VR)-based paradigms. In this direction, we have developed a first-person perspective VR environment combined with eye tracking for the capture of a variety of objective measures. Participants were instructed to search for a preselected human target walking in a crowded hallway setting. Performance was quantified based on saccade and smooth pursuit ocular motor behavior. To assess the effect of task difficulty, we manipulated factors of the visual scene, including crowd density (i.e., number of surrounding distractors) and the presence of environmental clutter. In general, results showed a pattern of worsening performance with increasing crowd density. In contrast, the presence of visual clutter had no effect. These results demonstrate how visual search performance can be investigated using VR-based naturalistic dynamic scenes and with high behavioral relevance. This engaging platform may also have utility in assessing visual search in a variety of clinical populations of interest.

Gaze Information Channel in Van Gogh's Paintings

This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all.

Fixation-related Brain Potentials during Semantic Integration of Object–Scene Information

In vision science, a particularly controversial topic is whether and how quickly the semantic information about objects is available outside foveal vision. Here, we aimed at contributing to this debate by coregistering eye movements and EEG while participants viewed photographs of indoor scenes that contained a semantically consistent or inconsistent target object. Linear deconvolution modeling was used to analyze the ERPs evoked by scene onset as well as the fixation-related potentials (FRPs) elicited by the fixation on the target object (t) and by the preceding fixation (t − 1). Object–scene consistency did not influence the probability of immediate target fixation or the ERP evoked by scene onset, which suggests that object–scene semantics was not accessed immediately. However, during the subsequent scene exploration, inconsistent objects were prioritized over consistent objects in extrafoveal vision (i.e., looked at earlier) and were more effortful to process in foveal vision (i.e., looked at longer). In FRPs, we demonstrate a fixation-related N300/N400 effect, whereby inconsistent objects elicit a larger frontocentral negativity than consistent objects. In line with the behavioral findings, this effect was already seen in FRPs aligned to the pretarget fixation t − 1 and persisted throughout fixation t, indicating that the extraction of object semantics can already begin in extrafoveal vision. Taken together, the results emphasize the usefulness of combined EEG/eye movement recordings for understanding the mechanisms of object–scene integration during natural viewing.

What eye movements reveal about strategy encoding of words in younger and older adults

The main goal of this study was to explore the organizational strategies used by younger and older adults when encoding words, using eye-tracking. Participants had to learn a set of organizable words and then a set of non-organizable words, each presented on a single display. Participants were then asked to recall the words of each set in the order in which they came to their mind. Hence, the participants' encoding strategies revealed by eye-tracking could be directly related to their subsequent memory performance. The results confirmed the detrimental impact of aging on memory and the weaker use of organizational strategies by older adults during the recall phase. The eye-tracking data showed that when they encode the words, older adults do not look at them for as long as younger adults, probably because of slower eye movements. They also revealed that compared to younger adults, older adults were much less able to adapt their word scanning strategy according to whether the words to encode were organizable or not. Finally, the relationships that were found between the recall scores and the eye-tracking data suggest that the eye movement pattern at learning can predict how people will recall the words.

Extra-foveal Processing of Object Semantics Guides Early Overt Attention During Visual Search

Eye-tracking studies using arrays of objects have demonstrated that some high-level processing of object semantics can occur in extra-foveal vision, but its role on the allocation of early overt attention is still unclear. This eye-tracking visual search study contributes novel findings by examining the role of object-to-object semantic relatedness and visual saliency on search responses and eye-movement behaviour across arrays of increasing size (3, 5, 7). Our data show that a critical object was looked at earlier and for longer when it was semantically unrelated than related to the other objects in the display, both when it was the search target (target-present trials) and when it was a target's semantically related competitor (target-absent trials). Semantic relatedness effects manifested already during the very first fixation after array onset, were consistently found for increasing set sizes, and were independent of low-level visual saliency, which did not play any role. We conclude that object semantics can be extracted early in extra-foveal vision and capture overt attention from the very first fixation. These findings pose a challenge to models of visual attention which assume that overt attention is guided by the visual appearance of stimuli, rather than by their semantics.

Functional Imaging of Visuospatial Attention in Complex and Naturalistic Conditions

One of the ultimate goals of cognitive neuroscience is to understand how the brain works in the real world. Functional imaging with naturalistic stimuli provides us with the opportunity to study the brain in situations similar to the everyday life. This includes the processing of complex stimuli that can trigger many types of signals related both to the physical characteristics of the external input and to the internal knowledge that we have about natural objects and environments. In this chapter, I will first outline different types of stimuli that have been used in naturalistic imaging studies. These include static pictures, short video clips, full-length movies, and virtual reality, each comprising specific advantages and disadvantages. Next, I will turn to the main issue of visual-spatial orienting in naturalistic conditions and its neural substrates. I will discuss different classes of internal signals, related to objects, scene structure, and long-term memory. All of these, together with external signals about stimulus salience, have been found to modulate the activity and the connectivity of the frontoparietal attention networks. I will conclude by pointing out some promising future directions for functional imaging with naturalistic stimuli. Despite this field of research is still in its early days, I consider that it will play a major role in bridging the gap between standard laboratory paradigms and mechanisms of brain functioning in the real world.

The functional arrangement of objects biases gaze direction

A growing number of studies suggest that semantic knowledge can influence the control of gaze in scenes. For example, observers are more likely to look toward objects that are semantically related to the currently fixated object. Recent evidence also suggests that an object's functional orientation can bias gaze direction. However, it is unknown whether these semantic and functional relationships can interact to determine gaze control. To address this issue, the present study assessed whether the functional arrangement of multiple objects can influence gaze control. Participants fixated a central object (e.g., a key) flanked by two peripheral objects. After a brief delay, participants were free to shift their gaze toward the peripheral object of their choice. One of the peripheral objects was semantically related to the central object (e.g., a lock), and the objects were arranged to depict a functional or non-functional interaction (e.g., a key pointing toward or away from a lock). When the orientation of the central object was manipulated, participants were more likely to look in the direction this object was pointing. Moreover, the functional arrangement of objects modulated this central orienting bias. However, when the orientation of the peripheral objects was manipulated, only the peripheral objects' semantic relationships influenced gaze control. Together, these findings suggest that functional relationships play an important role in the allocation of gaze, and can interact with semantic relationships to determine gaze control.

The Changing Landscape: High-Level Influences on Eye Movement Guidance in Scenes

The use of eye movements to explore scene processing has exploded over the last decade. Eye movements provide distinct advantages when examining scene processing because they are both fast and spatially measurable. By using eye movements, researchers have investigated many questions about scene processing. Our review will focus on research performed in the last decade examining: (1) attention and eye movements; (2) where you look; (3) influence of task; (4) memory and scene representations; and (5) dynamic scenes and eye movements. Although typically addressed as separate issues, we argue that these distinctions are now holding back research progress. Instead, it is time to examine the intersections of these seemingly separate influences and examine the intersectionality of how these influences interact to more completely understand what eye movements can tell us about scene processing.

Gaze Information Channel in Cognitive Comprehension of Poster Reading

Today, eye trackers are extensively used in studying human cognition. However, it is hard to analyze and interpret eye movement data from the cognitive comprehension perspective of poster reading. To find quantitative links between eye movements and cognitive comprehension, we tracked observers’ eye movement for reading scientific poster publications. We model in this paper eye tracking fixation sequences between content-dependent Areas of Interests (AOIs) as a Markov chain. Furthermore, we use the fact that a Markov chain is a special case of information or communication channel. Then, the gaze transition can be modeled as a discrete information channel, the gaze information channel. Next, some traditional eye tracking metrics, together with the gaze entropy and mutual information of the gaze information channel are calculated to quantify cognitive comprehension for every participant. The analysis of the results demonstrate that the gaze entropy and mutual information from individual gaze information channel are related to participants’ individual differences. This is the first study that eye tracking technology has been used to assess the cognitive comprehension of poster reading. The present work provides insights into human cognitive comprehension by using the novel gaze information channel methodology.

Intrusive effects of task-irrelevant information on visual selective attention: semantics and size

Attentional selection is a mechanism by which incoming sensory information is prioritized for further, detailed, and more effective, processing. Given that attended information is privileged by the sensory system, understanding and predicting what information is granted prioritization becomes an important endeavor. It has been argued that salient events as well as information that is related to the current goal of the organism (i.e., task-relevant) receive such a priority. Here, we propose that attentional prioritization is not limited to task-relevance, and discuss evidence showing that task-irrelevant, non-salient, high-level properties of unattended objects, namely object meaning and size, influence attentional allocation. Such an intrusion of non-salient, task-irrelevant, high-level information points to the need to re-conceptualize and formally modify current models of attentional guidance.

Developing attentional control in naturalistic dynamic road crossing situations

In the last 20 years, there has been increasing interest in studying visual attentional processes under more natural conditions. In the present study, we propose to determine the critical age at which children show similar to adult performance and attentional control in a visually guided task; in a naturalistic dynamic and socially relevant context: road crossing. We monitored visual exploration and crossing decisions in adults and children aged between 5 and 15 while they watched road traffic videos containing a range of traffic densities with or without pedestrians. 5–10 year old (y/o) children showed less systematic gaze patterns. More specifically, adults and 11–15 y/o children look mainly at the vehicles’ appearing point, which is an optimal location to sample diagnostic information for the task. In contrast, 5–10 y/os look more at socially relevant stimuli and attend to moving vehicles further down the trajectory when the traffic density is high. Critically, 5-10 y/o children also make an increased number of crossing decisions compared to 11–15 y/os and adults. Our findings reveal a critical shift around 10 y/o in attentional control and crossing decisions in a road crossing task.

Left hemispatial neglect and overt orienting in naturalistic conditions: Role of high-level and stimulus-driven signals

Deficits of visuospatial orienting in brain-damaged patients affected by hemispatial neglect have been extensively investigated. Nonetheless, spontaneous spatial orienting in naturalistic conditions is still poorly understood. Here, we investigated the role played by top-down and stimulus-driven signals in overt spatial orienting of neglect patients during free-viewing of short videos portraying everyday life situations. In Experiment 1, we assessed orienting when meaningful visual events competed on the left and right side of space, and tested whether sensory salience on the two sides biased orienting. In Experiment 2, we examined whether the spatial alignment of visual and auditory signals modulates orienting. The results of Experiment 1 showed that in neglect patients severe deficits in contralesional orienting were restricted to viewing conditions with bilateral visual events competing for attentional capture. In contrast, orienting towards the contralesional side was largely spared when the videos contained a single event on the left side. In neglect patients the processing of stimulus-driven salience was relatively spared and helped orienting towards the left side when multiple events were present. Experiment 2 showed that sounds spatially aligned with visual events on the left side improved orienting towards the otherwise neglected hemispace. Anatomical scans indicated that neglect patients suffered grey and white matter damages primarily in the ventral frontoparietal cortex. This suggests that the improvement of contralesional orienting associated with visual salience and audiovisual spatial alignment may be due to processing in the relatively intact dorsal frontoparietal areas. Our data show that in naturalistic environments, the presence of multiple meaningful events is a major determinant of spatial orienting deficits in neglect patients, whereas the salience of visual signals and the spatial alignment between auditory and visual signals can counteract spatial orienting deficits. These results open new perspectives to develop novel rehabilitation strategies based on the use of naturalistic stimuli.

Semantic associations do not modulate the visual awareness of objects

When observers adopt a category-level attentional set, objects that belong to the same category as this attentional set are more likely to enter awareness. For example, a driver who is monitoring the road for cars may be more likely to notice an oncoming car than a pedestrian who is crossing the road. Semantic associations between categories are also known to influence the deployment of attention, but it is unclear whether these associative relationships can influence the visual awareness of objects. To address this issue, we conducted four experiments using an inattentional blindness task. Participants tracked moving images of animals (e.g., monkeys or rabbits). On the last trial, an unexpected object that could belong to the same category as the tracked objects (i.e., a monkey or rabbit) or a semantically associated category (i.e., a banana or carrot) moved across the display. Participants were more likely to notice this object when it was visually salient or belonged to the same category as the tracked objects. However, they were no more likely to notice objects that shared a semantic association with the tracked objects. Thus, although categorical relationships play an important role in the visual awareness of objects, this effect does not extend to associative relationships among objects.

Psychophysical evaluation of individual low-level feature influences on visual attention

In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images.

Automatic Optimization of Wayfinding Design

Wayfinding signs play an important role in guiding users to navigate in a virtual environment and in helping pedestrians to find their ways in a real-world architectural site. Conventionally, the wayfinding design of a virtual environment is created manually, so as the wayfinding design of a real-world architectural site. The many possible navigation scenarios, as well as the interplay between signs and human navigation, can make the manual design process overwhelming and non-trivial. As a result, creating a wayfinding design for a typical layout can take months to several years. In this paper, we introduce the Way to Go! approach for automatically generating a wayfinding design for a given layout. The designer simply has to specify some navigation scenarios; our approach will automatically generate an optimized wayfinding design with signs properly placed considering human agents' visibility and possibility of making mistakes during a navigation. We demonstrate the effectiveness of our approach in generating wayfinding designs for different layouts such as a train station, a downtown and a canyon. We evaluate our results by comparing different wayfinding designs and show that our optimized wayfinding design can guide pedestrians to their destinations effectively and efficiently. Our approach can also help the designer visualize the accessibility of a destination from different locations, and correct any "blind zone" with additional signs.

Short-term visual memory for location in depth: A U-shaped function of time

Short-term visual memory was studied by displaying arrays of four or five numerals, each numeral in its own depth plane, followed after various delays by an arrow cue shown in one of the depth planes. Subjects reported the numeral at the depth cued by the arrow. Accuracy fell with increasing cue delay for the first 500 ms or so, and then recovered almost fully. This dipping pattern contrasts with the usual iconic decay observed for memory traces. The dip occurred with or without a verbal or color-shape retention load on working memory. In contrast, accuracy did not change with delay when a tonal cue replaced the arrow cue. We hypothesized that information concerning the depths of the numerals decays over time in sensory memory, but that cued recall is aided later on by transfer to a visual memory specialized for depth. This transfer is sufficiently rapid with a tonal cue to compensate for the sensory decay, but it is slowed by the need to tag the arrow cue's depth relative to the depths of the numerals, exposing a dip when sensation has decayed and transfer is not yet complete. A model with a fixed rate of sensory decay and varied transfer rates across individuals captures the dip as well as the cue modality effect.

Intrusive effects of semantic information on visual selective attention

Every object is represented by semantic information in extension to its low-level properties. It is well documented that such information biases attention when it is necessary for an ongoing task. However, whether semantic relationships influence attentional selection when they are irrelevant to the ongoing task remains an open question. The ubiquitous nature of semantic information suggests that it could bias attention even when these properties are irrelevant. In the present study, three objects appeared on screen, two of which were semantically related. After a varying time interval, a target or distractor appeared on top of each object. The objects’ semantic relationships never predicted the target location. Despite this, a semantic bias on attentional allocation was observed, with an initial, transient bias to semantically related objects. Further experiments demonstrated that this effect was contingent on the objects being attended: if an object never contained the target, it no longer exerted a semantic influence. In a final set of experiments, we demonstrated that the semantic bias is robust and appears even in the presence of more predictive cues (spatial probability). These results suggest that as long as an object is attended, its semantic properties bias attention, even if it is irrelevant to an ongoing task and if more predictive factors are available.

Five Factors that Guide Attention in Visual Search

How do we find what we are looking for? Fundamental limits on visual processing mean that even when the desired target is in our field of view, we often need to search, because it is impossible to recognize everything at once. Searching involves directing attention to objects that might be the target. This deployment of attention is not random. It is guided to the most promising items and locations by five factors discussed here: Bottom-up salience, top-down feature guidance, scene structure and meaning, the previous history of search over time scales from msec to years, and the relative value of the targets and distractors. Modern theories of search need to specify how all five factors combine to shape search behavior. An understanding of the rules of guidance can be used to improve the accuracy and efficiency of socially-important search tasks, from security screening to medical image perception.

Making Sense of Real-World Scenes

To interact with the world, we have to make sense of the continuous sensory input conveying information about our environment. A recent surge of studies has investigated the processes enabling scene understanding, using increasingly complex stimuli and sophisticated analyses to highlight the visual features and brain regions involved. However, there are two major challenges to producing a comprehensive framework for scene understanding. First, scene perception is highly dynamic, subserving multiple behavioral goals. Second, a multitude of different visual properties co-occur across scenes and may be correlated or independent. We synthesize the recent literature and argue that for a complete view of scene understanding, it is necessary to account for both differing observer goals and the contribution of diverse scene properties.

Gaze and visual search strategies of children with Asperger syndrome/high functioning autism viewing a magic trick

OBJECTIVE

To examine visual search patterns and strategies used by children with and without Asperger syndrome/high functioning autism (AS/HFA) while watching a magic trick. Limited responsivity to gaze cues is hypothesised to contribute to social deficits in children with AS/HFA.

METHODS

Twenty-one children with AS/HFA and 31 matched peers viewed a video of a gaze-cued magic trick twice. Between the viewings, they were informed about how the trick was performed. Participants' eye movements were recorded using a head-mounted eye-tracker.

RESULTS

Children with AS/HFA looked less frequently and had shorter fixation on the magician's direct and averted gazes during both viewings and more frequently at not gaze-cued objects and on areas outside the magician's face. After being informed of how the trick was conducted, both groups made fewer fixations on gaze-cued objects and direct gaze.

CONCLUSIONS

Information may enhance effective visual strategies in children with and without AS/HFA.

Target templates specify visual, not semantic, features to guide search: A marked asymmetry between seeking and ignoring

Top-down search templates specify targets' properties, either to guide attention toward the target or, independently, to accelerate the recognition of individual search items. Some previous studies have concluded that target templates can specify semantic categories to guide attention, though dissociating the effects of semantic versus visual features has proven difficult. In the present experiments, we examined the roles of target templates in search performance, by measuring the "two-template costs" incurred when observers did not know which of two types of targets would be presented. For target templates, these costs only varied with set size when a template could specify a target's features. Any semantic influences did not affect the guidance of attention, only the recognition of individual items. In contrast, templates for rejection-specifying the properties of irrelevant nontargets-do appear to specify semantic properties to guide attention away from those items, without affecting recognition. These qualitative differences between the two types of templates suggest that the processes of seeking and ignoring are fundamentally different.

Reference Production as Search: The Impact of Domain Size on the Production of Distinguishing Descriptions

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic findings in visual search; however, unlike models of reference production, visual search models also predict that search can become very efficient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression-as reflected by speech onset latencies-is influenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search.

Reconciling Saliency and Object Center-Bias Hypotheses in Explaining Free-Viewing Fixations

Predicting where people look in natural scenes has attracted a lot of interest in computer vision and computational neuroscience over the past two decades. Two seemingly contrasting categories of cues have been proposed to influence where people look: 1) low-level image saliency and 2) high-level semantic information. Our first contribution is to take a detailed look at these cues to confirm the hypothesis proposed by Henderson and Nuthmann and Henderson that observers tend to look at the center of objects. We analyzed fixation data for scene free-viewing over 17 observers on 60 object-annotated images with various types of objects. Images contained different types of scenes, such as natural scenes, line drawings, and 3-D rendered scenes. Our second contribution is to propose a simple combined model of low-level saliency and object center bias that outperforms each individual component significantly over our data, as well as on the Object and Semantic Images and Eye-tracking data set by Xu et al. The results reconcile saliency with object center-bias hypotheses and highlight that both types of cues are important in guiding fixations. Our work opens new directions to understand strategies that humans use in observing scenes and objects, and demonstrates the construction of combined models of low-level saliency and high-level object-based information.