title: Connectivity and thought: the influence of semantic network structure in a neurodynamical model of thinking.
abstract: Understanding cognition has been a central focus for psychologists, neuroscientists and philosophers for thousands of years, but many of its most fundamental processes remain very poorly understood. Chief among these is the process of thought itself: the spontaneous emergence of specific ideas within the stream of consciousness. It is widely accepted that ideas, both familiar and novel, arise from the combination of existing concepts. From this perspective, thought is an emergent attribute of memory, arising from the intrinsic dynamics of the neural substrate in which information is embedded. An important issue in any understanding of this process is the relationship between the emergence of conceptual combinations and the dynamics of the underlying neural networks. Virtually all theories of ideation hypothesize that ideas arise during the thought process through association, each one triggering the next through some type of linkage, e.g., structural analogy, semantic similarity, polysemy, etc. In particular, it has been suggested that the creativity of ideation in individuals reflects the qualitative structure of conceptual associations in their minds. Interestingly, psycholinguistic studies have shown that semantic networks across many languages have a particular type of structure with small-world, scale free connectivity. So far, however, these related insights have not been brought together, in part because there has been no explicitly neural model for the dynamics of spontaneous thought. Recently, we have developed such a model. Though simplistic and abstract, this model attempts to capture the most basic aspects of the process hypothesized by theoretical models within a neurodynamical framework. It represents semantic memory as a recurrent semantic neural network with itinerant dynamics. Conceptual combinations arise through this dynamics as co-active groups of neural units, and either dissolve quickly or persist for a time as emergent metastable attractors and are recognized consciously as ideas. The work presented in this paper describes this model in detail, and uses it to systematically study the relationship between the structure of conceptual associations in the neural substrate and the ideas arising from this system's dynamics. In particular, we consider how the small-world and scale-free characteristics influence the effectiveness of the thought process under several metrics, and show that networks with both attributes indeed provide significant advantages in generating unique conceptual combinations.
authors.affiliation: School of Electronic and Computing Systems, University of Cincinnati, Cincinnati, OH 45221, USA.
authors.forename: Nagendra
authors.initials: N
authors.lastname: Marupaka
authors.forename: Laxmi R
authors.initials: LR
authors.lastname: Iyer
authors.forename: Ali A
authors.initials: AA
authors.lastname: Minai
mesh_terms: D000465:Algorithms; D001245:Association Learning / Q000502:physiology; D001921:Brain / Q000502:physiology; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D001330:Electronic Data Processing; D006801:Humans; D007802:Language; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D011578:Psycholinguistics; D012660:Semantics; D013569:Synapses / Q000502:physiology; D013850:Thinking / Q000502:physiology*