Deriving an appropriate baseline for describing fixation behaviour

Retinal eccentricity modulates saliency-driven but not relevance-driven visual selection

Where we move our eyes during visual search is controlled by the relative saliency and relevance of stimuli in the visual field. However, the visual field is not homogeneous, as both sensory representations and attention change with eccentricity. Here we present an experiment investigating how eccentricity differences between competing stimuli affect saliency- and relevance-driven selection. Participants made a single eye movement to a predefined orientation singleton target that was simultaneously presented with an orientation singleton distractor in a background of multiple homogenously oriented other items. The target was either more or less salient than the distractor. Moreover, each of the two singletons could be presented at one of three different retinal eccentricities, such that both were presented at the same eccentricity, one eccentricity value apart, or two eccentricity values apart. The results showed that selection was initially determined by saliency, followed after about 300 ms by relevance. In addition, observers preferred to select the closer over the more distant singleton, and this central selection bias increased with increasing eccentricity difference. Importantly, it largely emerged within the same time window as the saliency effect, thereby resulting in a net reduction of the influence of saliency on the selection outcome. In contrast, the relevance effect remained unaffected by eccentricity. Together, these findings demonstrate that eccentricity is a major determinant of selection behavior, even to the extent that it modifies the relative contribution of saliency in determining where people move their eyes.

Objects guide human gaze behavior in dynamic real-world scenes

The complexity of natural scenes makes it challenging to experimentally study the mechanisms behind human gaze behavior when viewing dynamic environments. Historically, eye movements were believed to be driven primarily by space-based attention towards locations with salient features. Increasing evidence suggests, however, that visual attention does not select locations with high saliency but operates on attentional units given by the objects in the scene. We present a new computational framework to investigate the importance of objects for attentional guidance. This framework is designed to simulate realistic scanpaths for dynamic real-world scenes, including saccade timing and smooth pursuit behavior. Individual model components are based on psychophysically uncovered mechanisms of visual attention and saccadic decision-making. All mechanisms are implemented in a modular fashion with a small number of well-interpretable parameters. To systematically analyze the importance of objects in guiding gaze behavior, we implemented five different models within this framework: two purely spatial models, where one is based on low-level saliency and one on high-level saliency, two object-based models, with one incorporating low-level saliency for each object and the other one not using any saliency information, and a mixed model with object-based attention and selection but space-based inhibition of return. We optimized each model's parameters to reproduce the saccade amplitude and fixation duration distributions of human scanpaths using evolutionary algorithms. We compared model performance with respect to spatial and temporal fixation behavior, including the proportion of fixations exploring the background, as well as detecting, inspecting, and returning to objects. A model with object-based attention and inhibition, which uses saliency information to prioritize between objects for saccadic selection, leads to scanpath statistics with the highest similarity to the human data. This demonstrates that scanpath models benefit from object-based attention and selection, suggesting that object-level attentional units play an important role in guiding attentional processing.

Impact of neovascular age-related macular degeneration on eye-movement control during scene viewing: Viewing biases and guidance by visual salience

Human vision requires us to analyze the visual periphery to decide where to fixate next. In the present study, we investigated this process in people with age-related macular degeneration (AMD). In particular, we examined viewing biases and the extent to which visual salience guides fixation selection during free-viewing of naturalistic scenes. We used an approach combining generalized linear mixed modeling (GLMM) with a-priori scene parcellation. This method allows one to investigate group differences in terms of scene coverage and observers' well-known tendency to look at the center of scene images. Moreover, it allows for testing whether image salience influences fixation probability above and beyond what can be accounted for by the central bias. Compared with age-matched normally sighted control subjects (and young subjects), AMD patients' viewing behavior was less exploratory, with a stronger central fixation bias. All three subject groups showed a salience effect on fixation selection-higher-salience scene patches were more likely to be fixated. Importantly, the salience effect for the AMD group was of similar size as the salience effect for the control group, suggesting that guidance by visual salience was still intact. The variances for by-subject random effects in the GLMM indicated substantial individual differences. A separate model exclusively considered the AMD data and included fixation stability as a covariate, with the results suggesting that reduced fixation stability was associated with a reduced impact of visual salience on fixation selection.

A Bayesian Statistical Model Is Able to Predict Target-by-Target Selection Behaviour in a Human Foraging Task

Foraging refers to search involving multiple targets or multiple types of targets, and as a model task has a long history in animal behaviour and human cognition research. Foraging behaviour is usually operationalized using summary statistics, such as average distance covered during target collection (the path length) and the frequency of switching between target types. We recently introduced an alternative approach, which is to model each instance of target selection as random selection without replacement. Our model produces estimates of a set of foraging biases, such as a bias to select closer targets or targets of a particular category. Here we apply this model to predict individual target selection events. We add a new start position bias to the model, and generate foraging paths using the parameters estimated from individual participants' pre-existing data. The model predicts which target the participant will select next with a range of accuracy from 43% to 69% across participants (chance is 11%). The model therefore explains a substantial proportion of foraging behaviour in this paradigm. The situations where the model makes errors reveal useful information to guide future research on those aspects of foraging that we have not yet explained.

Scene saliencies in egocentric vision and their creation by parents and infants

Across the lifespan, humans are biased to look first at what is easy to see, with a handful of well-documented visual saliences shaping our attention (e.g., Itti & Koch, 2001). These attentional biases may emerge from the contexts in which moment-tomoment attention occurs, where perceivers and their social partners actively shape bottom-up saliences, moving their bodies and objects to make targets of interest more salient. The goal of the present study was to determine the bottom-up saliences present in infant egocentric images and to provide evidence on the role that infants and their mature social partners play in highlighting targets of interest via these saliences. We examined 968 unique scenes in which an object had purposefully been placed in the infant's egocentric view, drawn from videos created by one-year-old infants wearing a head camera during toy-play with a parent. To understand which saliences mattered in these scenes, we conducted a visual search task, asking participants (n = 156) to find objects in the egocentric images. To connect this to the behaviors of perceivers, we then characterized the saliences of objects placed by infants or parents compared to objects that were otherwise present in the scenes. Our results show that body-centric properties, such as increases in the centering and visual size of the object, as well as decreases in the number of competing objects immediately surrounding it, both predicted faster search time and distinguished placed and unplaced objects. The present results suggest that the bottom-up saliences that can be readily controlled by perceivers and their social partners may most strongly impact our attention. This finding has implications for the functional role of saliences in human vision, their origin, the social structure of perceptual environments, and how the relation between bottom-up and top-down control of attention in these environments may support infant learning.

Visual search habits and the spatial structure of scenes

Some spatial layouts may suit our visual search habits better than others. We compared eye movements during search across three spatial configurations. Participants searched for a line segment oriented 45^∘ to the right. Variation in the orientation of distractor line segments determines the extent to which this target would be visible in peripheral vision: a target among homogeneous distractors is highly visible, while a target among heterogeneous distractors requires central vision. When the search array is split into homogeneous and heterogeneous left and right halves, a large proportion of fixations are “wasted” on the homogeneous half, leading to slower search times. We compared this pattern to two new configurations. In the first, the array was split into upper and lower halves. During a passive viewing baseline condition, we observed biases to look both at the top half and also at the hetergeneous region first. Both of these biases were weaker during active search, despite the fact that the heterogeneous bias would have led to improvements in efficiency if it had been retained. In the second experiment, patches of more or less heterogeneous line segments were scattered across the search space. This configuration allows for more natural, spatially distributed scanpaths. Participants were more efficient and less variable relative to the left/right configuration. The results are consistent with the idea that visual search is associated with a distributed sequence of fixations, guided only loosely by the potential visibility of the target in different regions of the scene.

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.

Developmental changes in natural scene viewing in infancy

We extend decades of research on infants' visual processing by examining their eye gaze during viewing of natural scenes. We examined the eye movements of a racially diverse group of 4- to 12-month-old infants (N = 54; 27 boys; 24 infants were White and not Hispanic, 30 infants were African American, Asian American, mixed race and/or Hispanic) as they viewed images selected from the MIT Saliency Benchmark Project. In general, across this age range infants' fixation distributions became more consistent and more adult-like, suggesting that infants' fixations in natural scenes become increasingly more systematic. Evaluation of infants' fixation patterns with saliency maps generated by different models of physical salience revealed that although over this age range there was an increase in the correlations between infants' fixations and saliency, the amount of variance accounted for by salience actually decreased. At the youngest age, the amount of variance accounted for by salience was very similar to the consistency between infants' fixations, suggesting that the systematicity in these youngest infants' fixations was explained by their attention to physically salient regions. By 12 months, in contrast, the consistency between infants was greater than the variance accounted for by salience, suggesting that the systematicity in older infants' fixations reflected more than their attention to physically salient regions. Together these results show that infants' fixations when viewing natural scenes becomes more systematic and predictable, and that predictability is due to their attention to features other than physical salience. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Introducing Point-of-Interest as an alternative to Area-of-Interest for fixation duration analysis

Many eye-tracking data analyses rely on the Area-of-Interest (AOI) methodology, which utilizes AOIs to analyze metrics such as fixations. However, AOI-based methods have some inherent limitations including variability and subjectivity in shape, size, and location of AOIs. In this article, we propose an alternative approach to the traditional AOI dwell time analysis: Weighted Sum Durations (WSD). This approach decreases the subjectivity of AOI definitions by using Points-of-Interest (POI) while maintaining interpretability. In WSD, the durations of fixations toward each POI is weighted by the distance from the POI and summed together to generate a metric comparable to AOI dwell time. To validate WSD, we reanalyzed data from a previously published eye-tracking study (n = 90). The re-analysis replicated the original findings that people gaze less towards faces and more toward points of contact when viewing violent social interactions.

Salience-based object prioritization during active viewing of naturalistic scenes in young and older adults

Whether fixation selection in real-world scenes is guided by image salience or by objects has been a matter of scientific debate. To contrast the two views, we compared effects of location-based and object-based visual salience in young and older (65 + years) adults. Generalized linear mixed models were used to assess the unique contribution of salience to fixation selection in scenes. When analysing fixation guidance without recurrence to objects, visual salience predicted whether image patches were fixated or not. This effect was reduced for the elderly, replicating an earlier finding. When using objects as the unit of analysis, we found that highly salient objects were more frequently selected for fixation than objects with low visual salience. Interestingly, this effect was larger for older adults. We also analysed where viewers fixate within objects, once they are selected. A preferred viewing location close to the centre of the object was found for both age groups. The results support the view that objects are important units of saccadic selection. Reconciling the salience view with the object view, we suggest that visual salience contributes to prioritization among objects. Moreover, the data point towards an increasing relevance of object-bound information with increasing age.

Predicting artificial visual field losses: A gaze-based inference study

Visual field defects are a world-wide concern, and the proportion of the population experiencing vision loss is ever increasing. Macular degeneration and glaucoma are among the four leading causes of permanent vision loss. Identifying and characterizing visual field losses from gaze alone could prove crucial in the future for screening tests, rehabilitation therapies, and monitoring. In this experiment, 54 participants took part in a free-viewing task of visual scenes while experiencing artificial scotomas (central and peripheral) of varying radii in a gaze-contingent paradigm. We studied the importance of a set of gaze features as predictors to best differentiate between artificial scotoma conditions. Linear mixed models were utilized to measure differences between scotoma conditions. Correlation and factorial analyses revealed redundancies in our data. Finally, hidden Markov models and recurrent neural networks were implemented as classifiers in order to measure the predictive usefulness of gaze features. The results show separate saccade direction biases depending on scotoma type. We demonstrate that the saccade relative angle, amplitude, and peak velocity of saccades are the best features on the basis of which to distinguish between artificial scotomas in a free-viewing task. Finally, we discuss the usefulness of our protocol and analyses as a gaze-feature identifier tool that discriminates between artificial scotomas of different types and sizes.

Looking (for) patterns: Similarities and differences between infant and adult free scene-viewing patterns

Systematic tendencies such as the center and horizontal bias are known to have a large influence on how and where we move our eyes during static onscreen free scene viewing. However, it is unknown whether these tendencies are learned viewing strategies or are more default tendencies in the way we move our eyes. To gain insight into the origin of these tendencies we explore the systematic tendencies of infants (3 - 20-month-olds, N = 157) and adults (N = 88) in three different scene viewing data sets. We replicated com-mon findings, such as longer fixation durations and shorter saccade amplitudes in infants compared to adults. The leftward bias was never studied in infants, and our results indi-cate that it is not present, while we did replicate the leftward bias in adults. The general pattern of the results highlights the similarity between infant and adult eye movements. Similar to adults, infants' fixation durations increase with viewing time and the depend-encies between successive fixations and saccades show very similar patterns. A straight-forward conclusion to draw from this set of studies is that infant and adult eye movements are mainly driven by similar underlying basic processes.

Seeing Beyond Salience and Guidance: The Role of Bias and Decision in Visual Search

Visual search is a popular tool for studying a range of questions about perception and attention, thanks to the ease with which the basic paradigm can be controlled and manipulated. While often thought of as a sub-field of vision science, search tasks are significantly more complex than most other perceptual tasks, with strategy and decision playing an essential, but neglected, role. In this review, we briefly describe some of the important theoretical advances about perception and attention that have been gained from studying visual search within the signal detection and guided search frameworks. Under most circumstances, search also involves executing a series of eye movements. We argue that understanding the contribution of biases, routines and strategies to visual search performance over multiple fixations will lead to new insights about these decision-related processes and how they interact with perception and attention. We also highlight the neglected potential for variability, both within and between searchers, to contribute to our understanding of visual search. The exciting challenge will be to account for variations in search performance caused by these numerous factors and their interactions. We conclude the review with some recommendations for ways future research can tackle these challenges to move the field forward.

Disentangling bottom-up versus top-down and low-level versus high-level influences on eye movements over time

Bottom-up and top-down as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how they interact remains a matter of debate. Here, we disentangle these factors by analyzing their influence over time. For this purpose, we develop a saliency model that is based on the internal representation of a recent early spatial vision model to measure the low-level, bottom-up factor. To measure the influence of high-level, bottom-up features, we use a recent deep neural network-based saliency model. To account for top-down influences, we evaluate the models on two large data sets with different tasks: first, a memorization task and, second, a search task. Our results lend support to a separation of visual scene exploration into three phases: the first saccade, an initial guided exploration characterized by a gradual broadening of the fixation density, and a steady state that is reached after roughly 10 fixations. Saccade-target selection during the initial exploration and in the steady state is related to similar areas of interest, which are better predicted when including high-level features. In the search data set, fixation locations are determined predominantly by top-down processes. In contrast, the first fixation follows a different fixation density and contains a strong central fixation bias. Nonetheless, first fixations are guided strongly by image properties, and as early as 200 ms after image onset, fixations are better predicted by high-level information. We conclude that any low-level, bottom-up factors are mainly limited to the generation of the first saccade. All saccades are better explained when high-level features are considered, and later, this high-level, bottom-up control can be overruled by top-down influences.

The role of meaning in attentional guidance during free viewing of real-world scenes

In real-world vision, humans prioritize the most relevant visual information at the expense of other information via attentional selection. The current study sought to understand the role of semantic features and image features on attentional selection during free viewing of real-world scenes. We compared the ability of meaning maps generated from ratings of isolated, context-free image patches and saliency maps generated from the Graph-Based Visual Saliency model to predict the spatial distribution of attention in scenes as measured by eye movements. Additionally, we introduce new contextualized meaning maps in which scene patches were rated based upon how informative or recognizable they were in the context of the scene from which they derived. We found that both context-free and contextualized meaning explained significantly more of the overall variance in the spatial distribution of attention than image salience. Furthermore, meaning explained early attention to a significantly greater extent than image salience, contrary to predictions of the 'saliency first' hypothesis. Finally, both context-free and contextualized meaning predicted attention equivalently. These results support theories in which meaning plays a dominant role in attentional guidance during free viewing of real-world scenes.

Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing

Scene viewing is used to study attentional selection in complex but still controlled environments. One of the main observations on eye movements during scene viewing is the inhomogeneous distribution of fixation locations: While some parts of an image are fixated by almost all observers and are inspected repeatedly by the same observer, other image parts remain unfixated by observers even after long exploration intervals. Here, we apply spatial point process methods to investigate the relationship between pairs of fixations. More precisely, we use the pair correlation function, a powerful statistical tool, to evaluate dependencies between fixation locations along individual scanpaths. We demonstrate that aggregation of fixation locations within 4° is stronger than expected from chance. Furthermore, the pair correlation function reveals stronger aggregation of fixations when the same image is presented a second time. We use simulations of a dynamical model to show that a narrower spatial attentional span may explain differences in pair correlations between the first and the second inspection of the same image.

SGaze: A Data-Driven Eye-Head Coordination Model for Realtime Gaze Prediction

We present a novel, data-driven eye-head coordination model that can be used for realtime gaze prediction for immersive HMD-based applications without any external hardware or eye tracker. Our model (SGaze) is computed by generating a large dataset that corresponds to different users navigating in virtual worlds with different lighting conditions. We perform statistical analysis on the recorded data and observe a linear correlation between gaze positions and head rotation angular velocities. We also find that there exists a latency between eye movements and head movements. SGaze can work as a software-based realtime gaze predictor and we formulate a time related function between head movement and eye movement and use that for realtime gaze position prediction. We demonstrate the benefits of SGaze for gaze-contingent rendering and evaluate the results with a user study.

What Do Different Evaluation Metrics Tell Us About Saliency Models?

How best to evaluate a saliency model's ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whether spatial deviations are factored in, and how the saliency maps are pre-processed. In this paper, we provide an analysis of 8 different evaluation metrics and their properties. With the help of systematic experiments and visualizations of metric computations, we add interpretability to saliency scores and more transparency to the evaluation of saliency models. Building off the differences in metric properties and behaviors, we make recommendations for metric selections under specific assumptions and for specific applications.

Free-choice saccades and their underlying determinants: Explorations of high-level voluntary oculomotor control

Models of eye-movement control distinguish between different control levels, ranging from automatic (bottom-up, stimulus-driven selection) and automatized (based on well-learned routines) to voluntary (top-down, goal-driven selection, e.g., based on instructions). However, one type of voluntary control has yet only been examined in the manual and not in the oculomotor domain, namely free-choice selection among arbitrary targets, that is, targets that are of equal interest from both a bottom-up and top-down processing perspective. Here, we ask which features of targets (identity- or location-related) are used to determine such oculomotor free-choice behavior. In two experiments, participants executed a saccade to one of four peripheral targets in three different choice conditions: unconstrained free choice, constrained free choice based on target identity (color), and constrained free choice based on target location. The analysis of choice frequencies revealed that unconstrained free-choice selection closely resembled constrained choice based on target location. The results suggest that free-choice oculomotor control is mainly guided by spatial (location-based) target characteristics. We explain these results by assuming that participants tend to avoid less parsimonious recoding of target-identity representations into spatial codes, the latter being a necessary prerequisite to configure oculomotor commands.

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.

A Generative Model of Cognitive State from Task and Eye Movements

The early eye tracking studies of Yarbus provided descriptive evidence that an observer's task influences patterns of eye movements, leading to the tantalizing prospect that an observer's intentions could be inferred from their saccade behavior. We investigate the predictive value of task and eye movement properties by creating a computational cognitive model of saccade selection based on instructed task and internal cognitive state using a Dynamic Bayesian Network (DBN). Understanding how humans generate saccades under different conditions and cognitive sets links recent work on salience models of low-level vision with higher level cognitive goals. This model provides a Bayesian, cognitive approach to top-down transitions in attentional set in pre-frontal areas along with vector-based saccade generation from the superior colliculus. Our approach is to begin with eye movement data that has previously been shown to differ across task. We first present an analysis of the extent to which individual saccadic features are diagnostic of an observer's task. Second, we use those features to infer an underlying cognitive state that potentially differs from the instructed task. Finally, we demonstrate how changes of cognitive state over time can be incorporated into a generative model of eye movement vectors without resorting to an external decision homunculus. Internal cognitive state frees the model from the assumption that instructed task is the only factor influencing observers' saccadic behavior. While the inclusion of hidden temporal state does not improve the classification accuracy of the model, it does allow accurate prediction of saccadic sequence results observed in search paradigms. Given the generative nature of this model, it is capable of saccadic simulation in real time. We demonstrated that the properties from its generated saccadic vectors closely match those of human observers given a particular task and cognitive state. Many current models of vision focus entirely on bottom-up salience to produce estimates of spatial "areas of interest" within a visual scene. While a few recent models do add top-down knowledge and task information, we believe our contribution is important in three key ways. First, we incorporate task as learned attentional sets that are capable of self-transition given only information available to the visual system. This matches influential theories of bias signals by (Miller and Cohen Annu Rev Neurosci 24:167-202, 2001) and implements selection of state without simply shifting the decision to an external homunculus. Second, our model is generative and capable of predicting sequence artifacts in saccade generation like those found in visual search. Third, our model generates relative saccadic vector information as opposed to absolute spatial coordinates. This matches more closely the internal saccadic representations as they are generated in the superior colliculus.

How Well Can Saliency Models Predict Fixation Selection in Scenes Beyond Central Bias? A New Approach to Model Evaluation Using Generalized Linear Mixed Models

Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.

Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience

In addition to low-level stimulus characteristics and current goals, our previous experience with stimuli can also guide attentional deployment. It remains unclear, however, if such effects act independently or whether they interact in guiding attention. In the current study, we presented natural scenes including every-day objects that differed in affective-motivational impact. In the first free-viewing experiment, we presented visually-matched triads of scenes in which one critical object was replaced that varied mainly in terms of motivational value, but also in terms of valence and arousal, as confirmed by ratings by a large set of observers. Treating motivation as a categorical factor, we found that it affected gaze. A linear-effect model showed that arousal, valence, and motivation predicted fixations above and beyond visual characteristics, like object size, eccentricity, or visual salience. In a second experiment, we experimentally investigated whether the effects of emotion and motivation could be modulated by visual salience. In a medium-salience condition, we presented the same unmodified scenes as in the first experiment. In a high-salience condition, we retained the saturation of the critical object in the scene, and decreased the saturation of the background, and in a low-salience condition, we desaturated the critical object while retaining the original saturation of the background. We found that highly salient objects guided gaze, but still found additional additive effects of arousal, valence and motivation, confirming that higher-level factors can also guide attention, as measured by fixations towards objects in natural scenes.

Do infants have the horizontal bias?

A robust set of studies show that adults make more horizontal than vertical and oblique saccades, while scanning real-world scenes. In this paper we study the horizontal bias in infants. The directions of eye movements were calculated for 41 infants (M=8.40 months, SD=3.74, range=3.48-15.47) and 47 adults (M=21.74 years, SD=4.54, range=17.89-39.84) while viewing 28 real-world scenes. Saccade directions were binned to study the proportion of saccades in the horizontal, vertical and oblique directions. In addition, saccade directions were also modeled using a mixture of Von Mises distributions, to account for the relatively large amount of variance in infants data. Horizontal bias was replicated in adults and also found in infants, using both the binning and Von Mises approach. Moreover, a developmental pattern was observed in which older infants are more precise in targeting their saccades than younger infants. That infants have a horizontal bias is important in understanding infants' eye movements. Future studies should account for the horizontal bias in their designs and analyses.

A new approach to modeling the influence of image features on fixation selection in scenes

Which image characteristics predict where people fixate when memorizing natural images? To answer this question, we introduce a new analysis approach that combines a novel scene-patch analysis with generalized linear mixed models (GLMMs). Our method allows for (1) directly describing the relationship between continuous feature value and fixation probability, and (2) assessing each feature's unique contribution to fixation selection. To demonstrate this method, we estimated the relative contribution of various image features to fixation selection: luminance and luminance contrast (low-level features); edge density (a mid-level feature); visual clutter and image segmentation to approximate local object density in the scene (higher-level features). An additional predictor captured the central bias of fixation. The GLMM results revealed that edge density, clutter, and the number of homogenous segments in a patch can independently predict whether image patches are fixated or not. Importantly, neither luminance nor contrast had an independent effect above and beyond what could be accounted for by the other predictors. Since the parcellation of the scene and the selection of features can be tailored to the specific research question, our approach allows for assessing the interplay of various factors relevant for fixation selection in scenes in a powerful and flexible manner.