Neural blackboard architectures of combinatorial structures in cognition

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural ‘blackboard’ architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception

To be selected or not to be selected : A modeling and behavioral study of the mechanisms underlying stimulus-driven and top-down visual attention

This thesis investigates the mechanisms of stimulus-driven visual attention (global saliency), the mechanisms of top-down visual attention, and the interaction between these mechanisms, in visual search. Following the outline of an existing model of top-down visual attention, namely the Closed-Loop Attention Model (CLAM), simulations in this thesis explore mechanisms of visual working memory in the prefrontal cortex and of object recognition in the ventral pathway, and specify mechanisms of spatial selection in the dorsal pathway. Behavioral experiments additionally address several questions regarding stimulus-driven and top-down visual attention in visual search, and their interaction. The findings of the simulations and behavioral experiments have implications for CLAM in particular, and for the mechanisms of global saliency and top-down visual attention in general.LEI Universiteit LeidenFSW - Action Control - Ou

A Neural Model of Binding and Capacity in Visual Working Memory

The number of objects that can be maintained in visual working memory without interference is limited. We present simulations of a model of visual working memory in ventral prefrontal cortex that has this constraint as well. One layer in ventral PFC constitutes a 'blackboard ' representation of all objects in memory. These representations are used to bind the features (shape, color, location) of the objects. If there are too many objects, their representations will interfere in the blackboard and therefore the quality of these representations will degrade