SLAM: A connectionist model for attention in visual selection tasks

Phaf, R. Hans; Heijden, A. H. C. van der; Hudson, Peter

doi:10.1016/0010-0285(90)90006-p

Cited by 253 publications

(288 citation statements)

References 68 publications

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“…In the case of controlled search this ability has not been learned, and the positions to look at are chosen in some random order. Similar to SLAM (Phaf et al, 1990), our model makes no strict distinction between controlled search and automatic detection, but these are considered to be two extremes on a continuum.…”

Section: Discussionmentioning

confidence: 99%

“…For example, feature-integration theory (Treisman and Gelade, 1980), SLAM (Phaf et al, 1990), VISIT (Ahmad, 1992), the neocognitron (Fukushima and Imagawa, 1993), dynamic routing circuits (Olshausen et al, 1993), SCAN (Postma, 1994), SERR (Humphreys and Miiller, 1993) and guided search (Wolfe, 1994). Whereas humans learn and use attention in multiple tasks simultaneously, these models are either hard-wired without learning properties or perform just one specific task.…”

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confidence: 99%

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Task-Dependent Learning of Attention

Laar¹,

Heskes²,

Gielen³

1997

Neural Networks

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Task-Dependent Learning of Attention

Laar¹,

Heskes²,

Gielen³

1997

Neural Networks

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“…[4] and [5]) emphasizing the influence of neuron connection pathways (weights) in response processing, ours considers the influence of attention. In our model, the modulation of attention in a recall process is simulated through a part-pattern (<Attention> bits) completion.…”

Section: Methodsmentioning

confidence: 99%

“…[2], [4] and [5]). The conjectured explanation is that the asymmetric processing is due to automaticity of the word stimulus that is always processed first prior to the intended task stimulus, the colour.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Stroop Effect: Dynamics in Inhibition of Automatic Stimuli Processing

Yusoff

Grüning

Browne

2010

Advances in Cognitive Neurodynamics (II)

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Abstract. In this study, we simulate the dynamics of suppressing an automatic stimulus processing which interferes with a different non-automatic target task. The dynamics can be observed in terms of interference and facilitation effects that influence target response processing time. For this purpose, we use Hopfield neural network with varying attention modulation in a colour-word Stroop stimuli processing paradigm. With the biologically realistic features of the network, our model is able to model the Stroop effect in comparison to the human performance.

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“…Hence, it is not possible to focus on a particular location in the image. Phaf and coworkers [8] present SLAM, which include spatial attention and models the earliest stages of visual processing. Subsequently, these biologicallybased models were extended to eye movement control.…”

Section: Introductionmentioning

confidence: 99%

Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network

Saifullah

Kovordanyi

2011

Adaptive and Natural Computing Algorithms

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Abstract:We present a computational model for visual processing where attentional focus emerges fundamental mechanisms inherent to human vision. Through detailed analysis of activation development in the network we demonstrate how normal interaction between top-down and bottom-up processing and intrinsic mutual competition within processing units can give rise to attentional focus. The model includes both spatial and object-based attention, which are computed simultaneously, and can mutually reinforce each other. We show how a non-salient location and a corresponding non-salient feature set that are at first weakly activated by visual input can be reinforced by top-down feedback signals (centrally controlled attention), and instigate a change in attentional focus to the weak object. One application of this model is highlight a task-relevant object in a cluttered visual environment, even when this object is non-salient (non-conspicuous).

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SLAM: A connectionist model for attention in visual selection tasks

Cited by 253 publications

References 68 publications

Task-Dependent Learning of Attention

Task-Dependent Learning of Attention

Modelling the Stroop Effect: Dynamics in Inhibition of Automatic Stimuli Processing

Emergence of Attention Focus in a Biologically-Based Bidirectionally-Connected Hierarchical Network

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