comRAT-C : A Computational Compound Remote Associate Test Solver based on Language Data and its Comparison to Human Performance

Discovering the processes and types of knowledge organization which are involved in a creative pro- cess is a challenge up to this date. Human creativity is usually measured by psychological tests, such as the Remote Associates Test (RAT). In this paper, an approach based on a specific type of knowl- edge organization and processes which enables automatic solving of RAT queries is implemented (comRAT) as a part of a more general cognitive theoretical framework for creative problem-solving (CreaCogs). This aims to study: a) whether a convergence process can be used to solve such queries and b) if frequency of appearance of the test items in language data may influence knowledge associ- ation or discovery in solving such problems. The comRAT uses a knowledge base of language data extracted from the Corpus of Contemporary American English. The results obtained are compared to results obtained in empirical tests with humans. In order to explain why some answers might be preferred over others, frequencies of appearance of the queries and solutions are analysed. Human difficulty when solving RAT queries is expressed in response times and percentage of participants solving the query, and a significant moderate correlation between human data and the data provided by this approach is obtained.