Increasing number of objects impairs binding in visual working memory

Kleij, Gwendid T. van der Voort van der; Kamps, Marc de; Velde, Frank van der

doi:10.1016/j.neucom.2004.01.101

Cited by 2 publications

(3 citation statements)

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“…Interference of multiple objects makes it increasingly more difficult to select the correct location in the blackboard layer if more objects are present, and therefore increases the probability of an incorrect binding of features. (Reproduced from [31]. )…”

Section: Neural Blackboard Architecturesmentioning

confidence: 99%

“…We have applied the blackboard architecture to visual working memory [30,31]. Humans are able to store a number of visual objects in visual working memory.…”

Section: Visual Working Memory and The Binding Problemmentioning

confidence: 99%

“…Shown is the proportion of objects which are bound correctly as a function of the number of objects.Attention may be used in different ways: it may increase the sensitivity of the attended features (additive), or it may boost the response strength for the attended features without changing the sensitivity to them (multiplicative). (Reproduced from[31]. )…”

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confidence: 99%

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Neural blackboard architectures: the realization of compositionality and systematicity in neural networks

Kamps

Velde

2006

J. Neural Eng.

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In this paper, we will first introduce the notions of systematicity and combinatorial productivity and we will argue that these notions are essential for human cognition and probably for every agent that needs to be able to deal with novel, unexpected situations in a complex environment. Agents that use compositional representations are faced with the so-called binding problem and the question of how to create neural network architectures that can deal with it is essential for understanding higher level cognition. Moreover, an architecture that can solve this problem is likely to scale better with problem size than other neural network architectures. Then, we will discuss object-based attention. The influence of spatial attention is well known, but there is solid evidence for object-based attention as well. We will discuss experiments that demonstrate object-based attention and will discuss a model that can explain the data of these experiments very well. The model strongly suggests that this mode of attention provides a neural basis for parallel search. Next, we will show a model for binding in visual cortex. This model is based on a so-called neural blackboard architecture, where higher cortical areas act as processors, specialized for specific features of a visual stimulus, and lower visual areas act as a blackboard for communication between these processors. This implies that lower visual areas are involved in more than bottom-up visual processing, something which already was apparent from the large number of recurrent connections from higher to lower visual areas. This model identifies a specific role for these feedback connections. Finally, we will discuss the experimental evidence that exists for this architecture.

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Section: Neural Blackboard Architecturesmentioning

confidence: 99%

“…We have applied the blackboard architecture to visual working memory [30,31]. Humans are able to store a number of visual objects in visual working memory.…”

Section: Visual Working Memory and The Binding Problemmentioning

confidence: 99%

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Neural blackboard architectures: the realization of compositionality and systematicity in neural networks

Kamps

Velde

2006

J. Neural Eng.

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The state of MIIND

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MIIND (Multiple Interacting Instantiations of Neural Dynamics) is a highly modular multi-level C++ framework, that aims to shorten the development time for models in Cognitive Neuroscience (CNS). It offers reusable code modules (libraries of classes and functions) aimed at solving problems that occur repeatedly in modelling, but tries not to impose a specific modelling philosophy or methodology. At the lowest level, it offers support for the implementation of sparse networks. For example, the library SparseImplementationLib supports sparse random networks

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Increasing number of objects impairs binding in visual working memory

Cited by 2 publications

References 8 publications

Neural blackboard architectures: the realization of compositionality and systematicity in neural networks

Neural blackboard architectures: the realization of compositionality and systematicity in neural networks

The state of MIIND

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