Thinking in prose and poetry: A semantic neural model

The neural basis of creative thinking - indeed of all thinking - remains mysterious. One influential theory by Mednick holds that creative thinking reflects a difference in the associational structure of conceptual representations in the mind. We have previously proposed a neural network model based on itinerant dynamics to model thinking, and used it to show that a small-world, scale-free associational structure - similar to that found empirically in linguistic data - is especially efficient for exploring conceptual space and generating conceptual combinations. In this paper, we apply this model to associative networks obtained from the poetry of Dylan Thomas and John Gay, and the prose of F. Scott Fitzgerald and George Orwell. Network analysis shows that poetic texts indeed incorporate a wider distribution of associations than prose. However, neural simulations using semantic networks from the four sources present a more complex picture. We also consider the case where a poet's associative network is transformed to that of a prose-writer to test the impact of this manipulation.