comRAT-C: A computational compound Remote Associates Test solver based on language data and its comparison to human performance

• A cognitive system (comRAT-C) solving the Remote Associates Test (RAT) is implemented.
• comRAT-C gives results comparable to human normative data results.
• A hypothesis on human answer preference is quantified. Empirical support is provided.
• Cognitive difficulty of RAT correlates with comRAT-C probability of finding answer.

Discovering the processes and types of knowledge organization which are involved in the creative process 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 knowledge 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 association 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 analyzed. The difficulty encountered by humans when solving RAT queries is expressed in response times and percentage of participants solving the query, and a significant moderate correlation between human data on query difficulty and the data provided by this approach is obtained.