Visual Attention

Chun, Marvin M.; Wolfe, Jeremy M.

doi:10.1002/9780470753477.ch9

Cited by 90 publications

(91 citation statements)

References 213 publications

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“…Visual search is an essential component of our lives and is an extremely well-researched field in human psychology and neuroscience (Chun and Wolfe, 2000;Verghese, 2001;Wolfe, 1998;Wolfe, 2003). We know comparatively little, however, about visual search in other animals despite the fact that several animals also use visual search for vital biological functions like foraging, searching for mates or avoiding predators (Bond and Kamil, 2002;Ings and Chittka, 2008;Spaethe et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Bumblebee visual search for multiple learned target types

Nityananda

Pattrick

2013

Journal of Experimental Biology

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SUMMARYVisual search is well studied in human psychology, but we know comparatively little about similar capacities in non-human animals. It is sometimes assumed that animal visual search is restricted to a single target at a time. In bees, for example, this limitation has been evoked to explain flower constancy, the tendency of bees to specialise on a single flower type. Few studies, however, have investigated bee visual search for multiple target types after extended learning and controlling for prior visual experience. We trained colour-naive bumblebees (Bombus terrestris) extensively in separate discrimination tasks to recognise two rewarding colours in interspersed block training sessions. We then tested them with the two colours simultaneously in the presence of distracting colours to examine whether and how quickly they were able to switch between the target colours. We found that bees switched between visual targets quickly and often. The median time taken to switch between targets was shorter than known estimates of how long traces last in bees' working memory, suggesting that their capacity to recall more than one learned target was not restricted by working memory limitations. Following our results, we propose a model of memory and learning that integrates our findings with those of previous studies investigating flower constancy.

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Section: Introductionmentioning

confidence: 99%

Bumblebee visual search for multiple learned target types

Nityananda

Pattrick

2013

Journal of Experimental Biology

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“…We assume that, if we could successfully describe the rules that govern human search behavior, we would be able to improve performance in critical applied search tasks and to offer suggestions to those trying to build machines that might do our search tasks for us. Visual search is also an experimentally tractable way to study selective attention, and it is increasingly clear that any useful theory of visual perception will require an understanding of the role of attention.Treisman's feature integration theory Visual search and the role of attention in search has been much discussed in recent literature (see [1][2][3][4][5] for reviews). This article will concentrate on several issues growing out of Anne Treisman's seminal Feature Integration Theory (FIT) [6].…”

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confidence: 99%

“…Treisman's feature integration theory Visual search and the role of attention in search has been much discussed in recent literature (see [1][2][3][4][5] for reviews). This article will concentrate on several issues growing out of Anne Treisman's seminal Feature Integration Theory (FIT) [6].…”

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confidence: 99%

Moving towards solutions to some enduring controversies in visual search

Wolfe

2003

Trends in Cognitive Sciences

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How do we find a target item in a visual world filled with distractors? A quarter of a century ago, in her influential 'Feature Integration Theory (FIT)', Treisman proposed a two-stage solution to the problem of visual search: a preattentive stage that could process a limited number of basic features in parallel and an attentive stage that could perform more complex acts of recognition, one object at a time. The theory posed a series of problems. What is the nature of that preattentive stage? How do serial and parallel processes interact? How does a search unfold over time? Recent work has shed new light on these issues.Visual search is one of those things we do all day, every day, from finding milk in the refrigerator to locating our car in the car-park. We pay others to do it at airport security checks and in radiology laboratories and, in the past quarter of a century, we have done a great deal of it in our research laboratories. Laboratory search tasks ask the observer to find and/or identify a target item among some number of distractor items. The core empirical fact that needs explanation is that some search tasks are easy and others are difficult (see Fig. 1). We assume that, if we could successfully describe the rules that govern human search behavior, we would be able to improve performance in critical applied search tasks and to offer suggestions to those trying to build machines that might do our search tasks for us. Visual search is also an experimentally tractable way to study selective attention, and it is increasingly clear that any useful theory of visual perception will require an understanding of the role of attention.Treisman's feature integration theory Visual search and the role of attention in search has been much discussed in recent literature (see [1][2][3][4][5] for reviews). This article will concentrate on several issues growing out of Anne Treisman's seminal Feature Integration Theory (FIT) [6]. It would be a great disservice to many other researchers to label FIT as the sole 'big bang' of the visual search universe. However, it does serve well as an organizing principle for a brief review of some interesting and long-running controversies.The original FIT proposed that visual search tasks could be dichotomized into 'preattentive' and 'attentive' categories. Preattentive processing was held to occur in parallel across most or all of the visual field in a single step and to be limited to a small set of basic features like color, size, motion, and orientation. Thus, you could preattentively find a red item among green. Operationally, preattentive search for a target defined by a single basic feature would produce reaction times (RTs) independent of the number of items in the display (set size). Thus, the slope of the function relating RT to set size would be near zero. Other tasks, like a search for a randomly oriented 'T' among 'L's could not be performed preattentively. Attentive processing was presumed to marshal the more extensive perceptual capabilities required to 'bind' features ...

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“…Consequently, a process has evolved that allows us to rapidly select what is important and reduces the information present in order to operate within the limited capacity of our visual system (Chum and Wolfe 2001 ). This selective process, which prioritizes what visual information is relevant in relation to the situation, is what we term attention.…”

Section: Models Of Visual Attention Based On Lowlevel Perceptual Cuesmentioning

confidence: 99%

“…GISbased approaches have dominated calculationdriven research in this area to date, with most gains over the past decade coming in the size of the area that could be modeled and or the detail in which it may be analyzed. AQ3 Now, advances in cognitive neurosciences and a suite of improved computational modeling tools (Pashler 2016; Petersen and Posner 2012; Parks, Borji, and Itti 20132015 ; Borji and Itti 2015; Borji, Sihite, and Itti 2013; Julien and Riche 2016; Li et al 2013; Dickinson and Pizlo 2015, combined with the proliferation of detailed 3D models of archaeological complexes and landscapes, offer an opportunity for new approaches to this topic, based on models of lowlevel perceptual cues and visual attention (see Chum and Wolfe 2001 for a nice overview of the principles and models of visual attention and a thorough bibliography). The new approach described here takes aim at the question of where people will look, rather than simply what is visible.…”

Section: Introductionmentioning

confidence: 99%

An Experiment in Using Visual Attention Metrics to Think About Experience and Design Choices in Past Places

Opitz

2017

J Archaeol Method Theory

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An experiment in using visual attention metrics to think about experience and design choices in past places. Journal of Archaeological Method and Theory, 24(4), pp. 1203-1226. (doi:10.1007 This is the author's final accepted version.There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.http://eprints.gla.ac.uk/142948/ AbstractThe influence of the visual properties of a built space or landscape on the behaviors of people within them and the manipulation of these visual properties to cue or constrain behaviors are subjects of longstanding archaeological interest. Advances in cognitive neurosciences and a suite of improved computational modeling tools, combined with the proliferation of detailed 3D models of archaeological complexes and landscapes, offer an opportunity for new approaches to these topics based on models of lowlevel perceptual cues and visual attention. The approach described here takes aim at the question of where people will look, rather than simply what is visible, with the goal of investigating the intentions of designers of spaces and visual aspects of the experience of a place. In simple terms, our approach involves placing detailed 3D models of built spaces or landscapes into a digital environment. An individual then virtually walks through the space and what is visible at each moment is recorded in the form of a video stream, which may be broken down into a sequence of scenes. This set of scenes is then analyzed using software that calculates and maps the visual saliency of each scene and the path of focuses of attention (FOAs) over time. This set of saliency maps, raw images, and FOA paths provide the basis for further interpretation. This paper presents an initial experiment to illustrate the approach, carried out in the eastern passage at Knowth, one of the main mounds in the Brú na Bóinne in Ireland.

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Visual Attention

Cited by 90 publications

References 213 publications

Bumblebee visual search for multiple learned target types

Bumblebee visual search for multiple learned target types

Moving towards solutions to some enduring controversies in visual search

An Experiment in Using Visual Attention Metrics to Think About Experience and Design Choices in Past Places

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