Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

How spatial frequencies and color drive object search in real-world scenes: A new eye-movement corpus

ABSTRACT: When studying how people search for objects in scenes, the inhomogeneity of the visual field is often ignored. Due to physiological limitations, peripheral vision is blurred and mainly uses coarse-grained information (i.e., low spatial frequencies) for selecting saccade targets, whereas high-acuity central vision uses fine-grained information (i.e., high spatial frequencies) for analysis of details. Here we investigated how spatial frequencies and color affect object search in real-world scenes. Using gaze-contingent filters, we attenuated high or low frequencies in central or peripheral vision while viewers searched color or grayscale scenes. Results showed that peripheral filters and central high-pass filters hardly affected search accuracy, whereas accuracy dropped drastically with central low-pass filters. Peripheral filtering increased the time to localize the target by decreasing saccade amplitudes and increasing number and duration of fixations. The use of coarse-grained information in the periphery was limited to color scenes. Central filtering increased the time to verify target identity instead, especially with low-pass filters. We conclude that peripheral vision is critical for object localization and central vision is critical for object identification. Visual guidance during peripheral object localization is dominated by low-frequency color information, whereas high-frequency information, relatively independent of color, is most important for object identification in central vision.

SUBMITTER: Cajar A

PROVIDER: S-EPMC7424126 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Characteristic Sounds Facilitate Object Search in Real-Life Scenes.

Project description:Real-world events do not only provide temporally and spatially correlated information across the senses, but also semantic correspondences about object identity. Prior research has shown that object sounds can enhance detection, identification, and search performance of semantically consistent visual targets. However, these effects are always demonstrated in simple and stereotyped displays that lack ecological validity. In order to address identity-based cross-modal relationships in real-world scenarios, we designed a visual search task using complex, dynamic scenes. Participants searched for objects in video clips recorded from real-life scenes. Auditory cues, embedded in the background sounds, could be target-consistent, distracter-consistent, neutral, or just absent. We found that, in these naturalistic scenes, characteristic sounds improve visual search for task-relevant objects but fail to increase the salience of irrelevant distracters. Our findings generalize previous results on object-based cross-modal interactions with simple stimuli and shed light upon how audio-visual semantically congruent relationships play out in real-life contexts.

| S-EPMC6848886 | biostudies-literature

Emotional real-world scenes impact visual search.

Project description:Research shows that emotional stimuli can capture attention, and this can benefit or impair performance, depending on the characteristics of a task. Additionally, whilst some findings show that attention expands under positive conditions, others show that emotion has no influence on the broadening of attention. The current study investigated whether emotional real-world scenes influence attention in a visual search task. Participants were asked to identify a target letter embedded in the centre or periphery of emotional images. Identification accuracy was lower in positive images compared to neutral images, and response times were slower in negative images. This suggests that real-world emotional stimuli have a distracting effect on visual attention and search. There was no evidence that emotional images influenced the spatial spread of attention. Instead, it is suggested that findings may provide support for the argument that positive emotion encourages a global processing style and negative emotion promotes local processing.

| S-EPMC6647383 | biostudies-literature

Flipping the world upside down: Using eye tracking in virtual reality to study visual search in inverted scenes.

Project description:Image inversion is a powerful tool for investigating cognitive mechanisms of visual perception. However, studies have mainly used inversion in paradigms presented on twodimensional computer screens. It remains open whether disruptive effects of inversion also hold true in more naturalistic scenarios. In our study, we used scene inversion in virtual reality in combination with eye tracking to investigate the mechanisms of repeated visual search through three-dimensional immersive indoor scenes. Scene inversion affected all gaze and head measures except fixation durations and saccade amplitudes. Our behavioral results, surprisingly, did not entirely follow as hypothesized: While search efficiency dropped significantly in inverted scenes, participants did not utilize more memory as measured by search time slopes. This indicates that despite the disruption, participants did not try to compensate the increased difficulty by using more memory. Our study highlights the importance of investigating classical experimental paradigms in more naturalistic scenarios to advance research on daily human behavior.

| S-EPMC10195094 | biostudies-literature

Predicting eye movement patterns from fMRI responses to natural scenes.

Project description:Eye tracking has long been used to measure overt spatial attention, and computational models of spatial attention reliably predict eye movements to natural images. However, researchers lack techniques to noninvasively access spatial representations in the human brain that guide eye movements. Here, we use functional magnetic resonance imaging (fMRI) to predict eye movement patterns from reconstructed spatial representations evoked by natural scenes. First, we reconstruct fixation maps to directly predict eye movement patterns from fMRI activity. Next, we use a model-based decoding pipeline that aligns fMRI activity to deep convolutional neural network activity to reconstruct spatial priority maps and predict eye movements in a zero-shot fashion. We predict human eye movement patterns from fMRI responses to natural scenes, provide evidence that visual representations of scenes and objects map onto neural representations that predict eye movements, and find a novel three-way link between brain activity, deep neural network models, and behavior.

| S-EPMC6279768 | biostudies-literature

Persistence in eye movement during visual search.

Project description:As any cognitive task, visual search involves a number of underlying processes that cannot be directly observed and measured. In this way, the movement of the eyes certainly represents the most explicit and closest connection we can get to the inner mechanisms governing this cognitive activity. Here we show that the process of eye movement during visual search, consisting of sequences of fixations intercalated by saccades, exhibits distinctive persistent behaviors. Initially, by focusing on saccadic directions and intersaccadic angles, we disclose that the probability distributions of these measures show a clear preference of participants towards a reading-like mechanism (geometrical persistence), whose features and potential advantages for searching/foraging are discussed. We then perform a Multifractal Detrended Fluctuation Analysis (MF-DFA) over the time series of jump magnitudes in the eye trajectory and find that it exhibits a typical multifractal behavior arising from the sequential combination of saccades and fixations. By inspecting the time series composed of only fixational movements, our results reveal instead a monofractal behavior with a Hurst exponent , which indicates the presence of long-range power-law positive correlations (statistical persistence). We expect that our methodological approach can be adopted as a way to understand persistence and strategy-planning during visual search.

| S-EPMC4807769 | biostudies-literature

Understanding Events by Eye and Ear: Agent and Verb Drive Non-anticipatory Eye Movements in Dynamic Scenes.

Project description:As Macnamara (1978) once asked, how can we talk about what we see? We report on a study manipulating realistic dynamic scenes and sentences aiming to understand the interaction between linguistic and visual representations in real-world situations. Specifically, we monitored participants' eye movements as they watched video clips of everyday scenes while listening to sentences describing these scenes. We manipulated two main variables. The first was the semantic class of the verb in the sentence and the second was the action/motion of the agent in the unfolding event. The sentences employed two verb classes-causatives (e.g., break) and perception/psychological (e.g., notice)-which impose different constraints on the nouns that serve as their grammatical complements. The scenes depicted events in which agents either moved toward a target object (always the referent of the verb-complement noun), away from it, or remained neutral performing a given activity (such as cooking). Scenes and sentences were synchronized such that the verb onset corresponded to the first video frame of the agent motion toward or away from the object. Results show effects of agent motion but weak verb-semantic restrictions: causatives draw more attention to potential referents of their grammatical complements than perception verbs only when the agent moves toward the target object. Crucially, we found no anticipatory verb-driven eye movements toward the target object, contrary to studies using non-naturalistic and static scenes. We propose a model in which linguistic and visual computations in real-world situations occur largely independent of each other during the early moments of perceptual input, but rapidly interact at a central, conceptual system using a common, propositional code. Implications for language use in real world contexts are discussed.

| S-EPMC6795699 | biostudies-literature

Eye movement feedback fails to improve visual search performance.

Project description:Many real-world searches (e.g., radiology and baggage screening) have rare targets. When targets are rare, observers perform rapid, incomplete searches, leading to higher miss rates. To improve search for rare (10% prevalence) targets, we provided eye movement feedback (EMF) to observers during their searches. Although the nature of the EMF varied across experiments, each method informed observers about the regions of the display that had not yet been inspected. We hypothesized that feedback would help guide attention to unsearched areas and increase the proportion of the display searched before making a target-absent response, thereby increasing accuracy. An eye tracker was used to mark fixated areas by either removing a semiopaque gray overlay (Experiments 1 and 4) as portions of the display were fixated or by adding the overlay once the eye left a segment of the image (Experiments 2 and 4). Experiment 3 provided automated EMF, such that a new region was uncovered every 540 milliseconds. Across experiments, we varied whether people searched for "Waldo" in images from "Where's Waldo?" search books or searched for a T among offset Ls. We found weak evidence that EMF improves accuracy in Experiment 1. However, in the remaining experiments, EMF had no effect (Experiment 4), or even reduced accuracy (Experiments 2 and 3). We conclude that the one positive result we found is likely a Type I error and that the EMF method that we used is unlikely to improve visual search performance.

| S-EPMC5698387 | biostudies-literature

Modeling Search for People in 900 Scenes: A combined source model of eye guidance.

Project description:How predictable are human eye movements during search in real world scenes? We recorded 14 observers' eye movements as they performed a search task (person detection) in 912 outdoor scenes. Observers were highly consistent in the regions fixated during search, even when the target was absent from the scene. These eye movements were used to evaluate computational models of search guidance from three sources: saliency, target features, and scene context. Each of these models independently outperformed a cross-image control in predicting human fixations. Models that combined sources of guidance ultimately predicted 94% of human agreement, with the scene context component providing the most explanatory power. None of the models, however, could reach the precision and fidelity of an attentional map defined by human fixations. This work puts forth a benchmark for computational models of search in real world scenes. Further improvements in modeling should capture mechanisms underlying the selectivity of observer's fixations during search.

| S-EPMC2790194 | biostudies-literature

The categories, frequencies, and stability of idiosyncratic eye-movement patterns to faces.

Project description:The spatial pattern of eye-movements to faces considered typical for neurologically healthy individuals is a roughly T-shaped distribution over the internal facial features with peak fixation density tending toward the left eye (observer's perspective). However, recent studies indicate that striking deviations from this classic pattern are common within the population and are highly stable over time. The classic pattern actually reflects the average of these various idiosyncratic eye-movement patterns across individuals. The natural categories and respective frequencies of different types of idiosyncratic eye-movement patterns have not been specifically investigated before, so here we analyzed the spatial patterns of eye-movements for 48 participants to estimate the frequency of different kinds of individual eye-movement patterns to faces in the normal healthy population. Four natural clusters were discovered such that approximately 25% of our participants' fixation density peaks clustered over the left eye region (observer's perspective), 23% over the right eye-region, 31% over the nasion/bridge region of the nose, and 20% over the region spanning the nose, philthrum, and upper lips. We did not find any relationship between particular idiosyncratic eye-movement patterns and recognition performance. Individuals' eye-movement patterns early in a trial were more stereotyped than later ones and idiosyncratic fixation patterns evolved with time into a trial. Finally, while face inversion strongly modulated eye-movement patterns, individual patterns did not become less distinct for inverted compared to upright faces. Group-averaged fixation patterns do not represent individual patterns well, so exploration of such individual patterns is of value for future studies of visual cognition.

| S-EPMC5474224 | biostudies-literature

Drawings of real-world scenes during free recall reveal detailed object and spatial information in memory.

Project description:Understanding the content of memory is essential to teasing apart its underlying mechanisms. While recognition tests have commonly been used to probe memory, it is difficult to establish what specific content is driving performance. Here, we instead focus on free recall of real-world scenes, and quantify the content of memory using a drawing task. Participants studied 30 scenes and, after a distractor task, drew as many images in as much detail as possible from memory. The resulting memory-based drawings were scored by thousands of online observers, revealing numerous objects, few memory intrusions, and precise spatial information. Further, we find that visual saliency and meaning maps can explain aspects of memory performance and observe no relationship between recall and recognition for individual images. Our findings show that not only is it possible to quantify the content of memory during free recall, but those memories contain detailed representations of our visual experiences.

| S-EPMC6315028 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data