What’s in my cluster? evaluating automated clustering methods to understand idiosyncratic search behavior in verbal fluency

HIGHLIGHTS

• What: The authors provide an empirical evaluation of several automated methods for defining clusters and switches in the VFT by comparing model-predicted clusters with participant-designated clusters. The authors propose a novel approach to understand idiosyncratic search behavior by computing a measure of discordance for each pairwise transition based on a large dataset of cluster-switch designations from independent raters (N = 211) for the same transitions via a pre-registered experiment. The majority of the work in determining clusters and switches via automated methods focuses on validating these methods against clinical datasets for prognostic or diagnostic . . .
  

   

  

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