Analysis of English free association network reveals mechanisms of efficient solution of Remote Association Tests
This research addresses the problem of understanding cognitive mechanisms in language processing for psychologists and computational linguists, but it is incremental as it builds on existing network models.
The study analyzed how the structure of an English free association network correlates with the difficulty of solving Remote Association Tests (RATs), finding that average hardness is determined by word positions on the network and that efficient strategies involve activating strong links for easy RATs and moderately weak associations for medium/hard ones.
We study correlations between the structure and properties of a free association network of the English language, and solutions of psycholinguistic Remote Association Tests (RATs). We show that average hardness of individual RATs is largely determined by relative positions of test words (stimuli and response) on the free association network. We argue that the solution of RATs can be interpreted as a first passage search problem on a network whose vertices are words and links are associations between words. We propose different heuristic search algorithms and demonstrate that in "easily-solving" RATs (those that are solved in 15 seconds by more than 64\% subjects) the solution is governed by "strong" network links (i.e. strong associations) directly connecting stimuli and response, and thus the efficient strategy consist in activating such strong links. In turn, the most efficient mechanism of solving medium and hard RATs consists of preferentially following sequence of "moderately weak" associations.