Abstract
We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking), which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.
Highlights
The ability to represent and process conceptual structures, as found in language processing, reasoning, and in generating conceptual representations from visual and auditory perception, are key elements of human cognition
The entire description of Information Dynamics of Thinking (IDyOT) memory construction is novel, and we present a novel simulation of neural activity based on the Neural Blackboard Architecture (NBA), which allows for a detailed comparison with brain activity observed in human processing
We have presented two knowledge representations, used in two cognitive architectures, the NBA and IDyOT, that both aim to account for conceptual representation and processing in productive forms of cognition
Summary
The ability to represent and process conceptual structures, as found in language processing, reasoning, and in generating conceptual representations from visual and auditory perception, are key elements of human cognition They can be studied with the aim to understand human cognition and its relation to the brain. We aim to outline how combined representations could be developed, for use in a combined architecture in which aspects of our neural and symbolic architectures are integrated We hypothesize that such a combined architecture could serve as a model of human conceptual processing and its relation to the brain. The neural representation in our integration is that used in the Neural Blackboard Architecture (NBA), which is aimed to represent and process conceptual structures in language (e.g., van der Velde and de Kamps, 2006, 2010), reasoning and other cognitive domains (van der Velde, 2016a). The NBA provides a route toward a neural implementation of IDyOT, which could form the basis of in parallel operating hardware
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