Structured cognition and neural systems: From rats to language

Much of animal and human cognition is compositional in nature: higher order, complex representations are formed by (rule-governed) combination of more primitive representations. We review here some of the evidence for compositionality in perception and memory, motivating an approach that takes ideas and techniques from computational linguistics to model aspects of structural representation in cognition. We summarize some recent developments in our work that, on the one hand, use algorithms from computational linguistics to model memory consolidation and the formation of semantic memory, and on the other hand use insights from the neurobiology of memory to develop a neurally inspired model of syntactic parsing that improves over existing (not cognitively motivated) models in computational linguistics. These two theoretical studies highlight interesting analogies between language acquisition, semantic memory and memory consolidation, and suggest possible neural mechanisms, implemented in computational algorithms that may underlie memory consolidation.

► We review some evidence that perception and memory are intertwined, and have compositional character.
► We present a model that attempts at modeling semantic memory formation with probabilistic grammars.
► We present a model that employs episodic memory to develop a parser for human language.