Research Colloquium "Cognitive Neuroscience"

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• Vortrag

Dr. Andrew Persichetti, Section on Cognitive Neuropsychology, NIMH, Bethesda, USA

Title: Using similarity judgments to map the representational space of abstract concepts

Abstract:
A central goal of cognitive science is to understand the representational space of concepts. However, most research has focused on concrete concepts (i.e., dogs and cars) and the relatively few attempts to examine abstract concepts (i.e., love and despair) often use methods that are not well-suited to studying them (e.g., asking people to list features of concepts or rate them on experimenter-defined dimensions). We sought to uncover the core dimensions that underlie the representational space of 378 abstract words using an odd-oneout similarity task, in which participants chose which of three words was least like the other two across many trials. We used a variational Bayesian method for embedding the concepts in a vector space, called VICE to embed the concepts in a vector space and select reproducible dimensions that best explain the human similarity judgments. The VICE model achieved ~90% of the best possible accuracy at predicting human behavior. These results give us an interpretable multi-dimensional representational space of abstract concepts. The dimensional weightings can be used in a variety of applications, including clustering abstract concepts into categories and designing fMRI experiments.

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