Modelling divided visual attention with a winner-take-all network

Standage, Dominic; Trappenberg, Thomas; Klein, Raymond M.

doi:10.1016/j.neunet.2005.06.015

Cited by 32 publications

(29 citation statements)

References 19 publications

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“…We use the terminology MAPS here to emphasize the role of both bottom-up salience and task relevance in determining the “attentional priority” of each item [27]. For example, Standage and colleagues [28] developed a model in which top-down factors such as task relevance are also taken into account and in which several areas may achieve equal priority in the map. One key prediction of models based on winner-take-all selection is the existence of competitive interactions, such as lateral inhibition, between items [21], [22], [29], [30].…”

Section: Introductionmentioning

confidence: 99%

The Role of Attentional Priority and Saliency in Determining Capacity Limits in Enumeration and Visual Working Memory

Melcher

Piazza

2011

PLoS ONE

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Many common tasks require us to individuate in parallel two or more objects out of a complex scene. Although the mechanisms underlying our abilities to count the number of items, remember the visual properties of objects and to make saccadic eye movements towards targets have been studied separately, each of these tasks require selection of individual objects and shows a capacity limit. Here we show that a common factor—salience—determines the capacity limit in the various tasks. We manipulated bottom-up salience (visual contrast) and top-down salience (task relevance) in enumeration and visual memory tasks. As one item became increasingly salient, the subitizing range was reduced and memory performance for all other less-salient items was decreased. Overall, the pattern of results suggests that our abilities to enumerate and remember small groups of stimuli are grounded in an attentional priority or salience map which represents the location of important items.

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Section: Introductionmentioning

confidence: 99%

The Role of Attentional Priority and Saliency in Determining Capacity Limits in Enumeration and Visual Working Memory

Melcher

Piazza

2011

PLoS ONE

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“…Standage et al (2005), Trappenberg (in press, 2008b) and Trappenberg et al (2001)). Neural field models of this sort were first introduced by Amari (1977).…”

Section: Centre-surround Neural Field (Csnf) Model Architecturementioning

confidence: 99%

“…CentreSurround Neural Field (CSNF) models work in a similar way with activation at one region exciting near-by locations, but suppressing distant locations. The CSNF models we will describe in this paper are also called Continuous Attractor Neural Networks (Standage, Trappenberg, & Klein, 2005;Stringer, Trappenberg, Rolls, & Araujo, 2002) and Competitive Integration Models (Godijn & Theeuwes, 2002). The centre-surround mechanisms of CSNF models have been under investigation for a number of years (e.g.…”

Section: Centre-surround Neural Field (Csnf) Modelsmentioning

confidence: 99%

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Modeling the integration of expectations in visual search with centre-surround neural fields

Salmon

Trappenberg

2008

Neural Networks

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a b s t r a c tCentre-Surround Neural Field (CSNF) models were used to explain a possible mechanism by which information from different sources may be integrated into target likelihood maps that are then used to direct eye saccades. The CSNF model is a dynamic model in which each region in network excites nearby location and inhibits distant locations, thereby modeling competition for eye movements (saccades). The CSNF model was tested in a number of conditions analogous to a naturalistic search task in which the target was either (1) present in the expected location, (2) and Shimozaki (2006) found no significant differences. These discrepancies between model predictions and behavioural results are discussed. Additional simulations indicated that these models can also capture the qualitative flavor of eye movements in conditions with multiple targets as compared to [Findlay, J. M. (1997). Saccade target selection during visual search. Vision Research, 37(5),[617][618][619][620][621][622][623][624][625][626][627][628][629][630][631].

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“…A "winner-take-all" (WTA) network is commonly used to allocate attention [6,7,10,11] . In the network that receives input from a saliency map, only one unit is allowed to be active at a given time for serial processing, and the others are suppressed.…”

Section: Processing Stages In Selective Attentionmentioning

confidence: 99%

Guiding Attention by Cooperative Cues

Lee

2008

J. Comput. Sci. Technol.

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A common assumption in visual attention is based on the rationale of "limited capacity of information processing". From this view point there is little consideration of how different information channels or modules are cooperating because cells in processing stages are forced to compete for the limited resource. To examine the mechanism behind the cooperative behavior of information channels, a computational model of selective attention is implemented based on two hypotheses. Unlike the traditional view of visual attention, the cooperative behavior is assumed to be a dynamic integration process between the bottom-up and top-down information. Furthermore, top-down information is assumed to provide a contextual cue during selection process and to guide the attentional allocation among many bottom-up candidates. The result from a series of simulation with still and video images showed some interesting properties that could not be explained by the competitive aspect of selective attention alone.

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Modelling divided visual attention with a winner-take-all network

Cited by 32 publications

References 19 publications

The Role of Attentional Priority and Saliency in Determining Capacity Limits in Enumeration and Visual Working Memory

The Role of Attentional Priority and Saliency in Determining Capacity Limits in Enumeration and Visual Working Memory

Modeling the integration of expectations in visual search with centre-surround neural fields

Guiding Attention by Cooperative Cues

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