Route choice in individuals—semantic network navigation


In a novel experimental task, individuals are asked to navigate from a goal word to an end word through a semantic network. In this forced choice task, individuals perform with a high success rate (73%) and frequently navigate to the target in the minimal number of required step (22%). We utilize these experimental results to explore different search and decision strategies. Our modeling results suggest individuals are not guessing at random (or utilizing only local information) and that knowledge of the global structure is necessary for individuals to succeed. We further show that a latent semantic space model, such as word association space, can capture much of the global semantic knowledge necessary to explain participant decisions. We suggest that performance in this task might shed light on the underlying structure of memory and more importantly search within memory.

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