A small-world network model of cognitive insight

Despite many decades of study, scientists still puzzle over the process of insight. By what mechanism does a person experience that Aha! moment, when sudden clarity emerges from a tangled web of thoughts and ideas? This research integrates psychological work on insight with graph theoretic work on small-world phenomenon, to construct a theory that explains how insight occurs, how it is similar to and different from more typical learning processes, and why it yields an affective response in the individual. I propose that cognitive insight occurs when an atypical association, forged through random recombination or directed search, results in a shortcut in an individual's network of representations. This causes a rapid decrease in path length, reorients the individual's understanding of the relationships within and among the affected representations, and can prompt a cascade of other connections. This result is demonstrated by applying graph theoretical analysis to network translations of commonly used insight problems.