Representation of Neuro-Information and Knowledge

Representation of Neuro-Information and Knowledge

Frank van der Velde (Leiden University, The Netherlands)
DOI: 10.4018/978-1-60960-018-1.ch002


Human cognition integrates grounding, productivity and dynamics. This chapter discusses how combinatorial structures can be created by forming temporal interconnections between grounded representations of concepts (words). Specific architectures are required so as to allow these temporal connections to be formed. A crucial aspect of these architectures is that they do not copy and transport symbols. Instead, the representations they use remain “in situ”, such that each representation of a concept used in a combinatorial structure is always the same grounded representation of that concept. These architectures therefore combine productivity of cognition with grounding of representations. Because they consist of neural structures, they are also inherently dynamic, which ensures the ability to interact dynamically with the environment. Grounded representations as constituents in combinatorial structures provide both local and global information. As constituents, they can affect specific (local) combinatorial structures, but without losing their embedding in the global information structure of which they are a part. The constraints that grounding, dynamics and productivity impose on each other have consequences for processing, learning and development. These consequences will be discussed and illustrated.
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The classical theory of cognition, or classical cognitivism for short, arose in the 1960s. It emphasized the importance of productivity for human cognition, and the demands it imposes on cognitive architectures (e.g., Anderson, 1983; Newell, 1990). Classical cognitive architectures achieve productivity because they use symbol manipulation to process or create compositional (or combinatorial) structures.

Symbol manipulation depends on the ability to make copies of symbols and to transport them to other locations. Newell (1990) argued that symbols are needed for cognition because only a limited amount of information can be stored physically at a given location. The symbol token is then needed to obtain more information when that is required for a given process. In Newell's words:

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