The relative contribution of scene context and target features to visual search in scenes

Castelhano, Monica S.; Heaven, Chelsea

doi:10.3758/app.72.5.1283

Cited by 102 publications

(130 citation statements)

References 46 publications

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“…This familiarity allows the eye movements of expert radiologists to be drawn to the sites of lesions, with less time spent on irrelevant sites. A similar pattern of efficient eye movements can be observed when humans search real scenes for a prespecified target (51)(52)(53)(54). Told to search for a toaster, a person would not bother to search the bathroom and would be unlikely to search on the floor of the kitchen.…”

Section: Development Of Expertise In Nonselective Searchmentioning

confidence: 72%

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

et al. 2013

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Diagnostic accuracy for radiologists is above that expected by chance when they are exposed to a chest radiograph for only one-fifth of a second, a period too brief for more than a single voluntary eye movement. How do radiologists glean information from a first glance at an image? It is thought that this expert impression of the gestalt of an image is related to the everyday, immediate visual understanding of the gist of a scene. Several high-speed mechanisms guide our search of complex images. Guidance by basic features (such as color) requires no learning, whereas guidance by complex scene properties is learned. It is probable that both hardwired guidance by basic features and learned guidance by scene structure become part of radiologists' expertise. Search in scenes may be best explained by a two-pathway model: Object recognition is performed via a selective pathway in which candidate targets must be individually selected for recognition. A second, nonselective pathway extracts information from global or statistical information without selecting specific objects. An appreciation of the role of nonselective processing may be particularly useful for understanding what separates novice from expert radiologists and could help establish new methods of physician training based on medical image perception.

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Section: Development Of Expertise In Nonselective Searchmentioning

confidence: 72%

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

et al. 2013

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“…First, a major component of how the scene-context guides search is that observers learn the spatial relationships between scenes and the objects within (Castelhano & Henderson, 2007;Castelhano & Heaven, 2010). As a result, the scene-context can guide search towards likely target locations, even when targets were not present in previews.…”

Section: Discussionmentioning

confidence: 99%

Worth a quick look? Initial scene previews can guide eye movements as a function of domain-specific expertise but can also have unforeseen costs.

Litchfield¹,

Donovan²

2016

Journal of Experimental Psychology: Human Perception and Perfor

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Rapid scene recognition is a global visual process we can all exploit to guide search. This ability is thought to underpin expertise in medical image perception yet there is no direct evidence that isolates the expertise-specific contribution of processing scene previews on subsequent eye movement performance. We used the flash-preview moving window paradigm (Castelhano & Henderson, 2007) to investigate this issue. Expert radiologists and novice observers underwent 2 experiments whereby participants viewed a 250-ms scene preview or a mask before searching for a target. Observers looked for everyday objects from real-world scenes (Experiment 1), and searched for lung nodules from medical images (Experiment 2). Both expertise groups exploited the brief preview of the upcoming scene to more efficiently guide windowed search in Experiment 1, but there was only a weak effect of domain-specific expertise in Experiment 2, with experts showing small improvements in search metrics with scene previews. Expert diagnostic performance was better than novices in all conditions but was not contingent on seeing the scene preview, and scene preview actually impaired novice diagnostic performance. Experiment 3 required novice and experienced observers to search for a variety of abnormalities from different medical images. Rather than maximizing the expertise-specific advantage of processing scene previews, both novices and experienced radiographers were worse at detecting abnormalities with scene previews. We discuss how restricting access to the initial glimpse can be compensated for by subsequent search and discovery processing, but there can still be costs in integrating a fleeting glimpse of a medical scene.

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“…Visual search is ubiquitous in routine tasks: finding one's car in a parking lot, house keys on a cluttered desk, or the button you wish to click on a computer interface. When searching common scene contexts for a target object, individuals rapidly glean information about where targets are typically located (1)(2)(3)(4)(5)(6)(7)(8)(9). This ability to use the "gist" of an image (3,4) enables individuals to perform flexibly and efficiently in familiar environments.…”

mentioning

confidence: 99%

Learning where to look for a hidden target

Chukoskie

Snider

Mozer

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Survival depends on successfully foraging for food, for which evolution has selected diverse behaviors in different species. Humans forage not only for food, but also for information. We decide where to look over 170,000 times per day, approximately three times per wakeful second. The frequency of these saccadic eye movements belies the complexity underlying each individual choice. Experience factors into the choice of where to look and can be invoked to rapidly redirect gaze in a context and task-appropriate manner. However, remarkably little is known about how individuals learn to direct their gaze given the current context and task. We designed a task in which participants search a novel scene for a target whose location was drawn stochastically on each trial from a fixed prior distribution. The target was invisible on a blank screen, and the participants were rewarded when they fixated the hidden target location. In just a few trials, participants rapidly found the hidden targets by looking near previously rewarded locations and avoiding previously unrewarded locations. Learning trajectories were well characterized by a simple reinforcement-learning (RL) model that maintained and continually updated a reward map of locations. The RL model made further predictions concerning sensitivity to recent experience that were confirmed by the data. The asymptotic performance of both the participants and the RL model approached optimal performance characterized by an ideal-observer theory. These two complementary levels of explanation show how experience in a novel environment drives visual search in humans and may extend to other forms of search such as animal foraging.ideal observer | oculomotor | reinforcement learning | saccades T he influence of evolution can be seen in foraging behaviors, which have been studied in behavioral ecology. Economic models of foraging assume that decisions are made to maximize payoff and minimize energy expenditure. For example, a bee setting off in search of flowers that are in bloom may travel kilometers to find food sources. Seeking information about an environment is an important part of foraging. Bees need to identify objects at a distance that are associated with food sources. Humans are also experts at searching for items in the world, and in learning how to find them. This study explores the problem of how humans learn where to look in the context of animal foraging.Our daily activities depend on successful search strategies for finding objects in our environment. Visual search is ubiquitous in routine tasks: finding one's car in a parking lot, house keys on a cluttered desk, or the button you wish to click on a computer interface. When searching common scene contexts for a target object, individuals rapidly glean information about where targets are typically located (1-9). This ability to use the "gist" of an image (3, 4) enables individuals to perform flexibly and efficiently in familiar environments. Add to that the predictable sequence of eye movements that occurs when some...

show abstract

The relative contribution of scene context and target features to visual search in scenes

Cited by 102 publications

References 46 publications

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

Informatics in Radiology: What Can You See in a Single Glance and How Might This Guide Visual Search in Medical Images?

Worth a quick look? Initial scene previews can guide eye movements as a function of domain-specific expertise but can also have unforeseen costs.

Learning where to look for a hidden target

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