Similarity and categorisation are paramount in our making sense of the world and the objects, structures and actions that we find in it. The process of classifying objects is a fundamental feature of most human pursuits, and the idea that we classify together those things that we find similar is both intuitive and popular across a wide range of disciplines. Similarity-based models of classification abound in Psychology as accounts of human performance and in Artificial Intelligence and Machine Learning as the basis of practical applications.
However, despite the centrality of these notions, similarity and categorisation are still comparatively poorly understood. A major cause for this has been that until recently, many disciplines have tended to study these only as adjuncts to larger questions and in isolation from other fields.
SimCat is a gathering of researchers addressing similarity and categorisation from a wide range of disciplines, including: