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Computationally resurrecting the functional Remote Associates Test using cognitive word associates and principles from a computational solver

Olteteanu, Ana-Maria; Schöttner, Mikkel; Schuberth, Susanne – 2019

Human creativity is usually assessed with a variety of established creativity tests. One of this is the Remote Associates Test (RAT), which aims to measure the ability of reaching remote associates with linguistic stimuli. A well known variant of the RAT exists – the compound RAT, for which normative data and solvers have been proposed in the literature. However, a different type of RAT was proposed in 1971 by Worthen and Clark – a functional form which had the potential of measuring other types of associations. However, the few test items proposed by Worthen and Clark where lost during archive transport, and cannot be accessed. In this paper, we set to reconstruct an ample set of functional items in the spirit of Worthen and Clark’s idea, using information science techniques. Cognitive word associates are used as data. The process of a former computational solver of the RAT is repurposed to create rather than solve items. The approach of constructing queries is evaluated by getting human participants to solve both functional and compound items. In the process, a previous computational approach to solving the compound RAT is also validated in the functional RAT context.

Title
Computationally resurrecting the functional Remote Associates Test using cognitive word associates and principles from a computational solver
Author
Olteteanu, Ana-Maria; Schöttner, Mikkel; Schuberth, Susanne
Keywords
CreaCogs
Date
2019
Identifier
10.1016/j.knosys.2018.12.023
Appeared in
Knowledge-Based Systems
Type
Text
BibTeX Code
@article{olteteanu_computationally_2019,
author = {Olteteanu, Ana-Maria and Schöttner, Mikkel and Schuberth, Susanne},
title = {Computationally resurrecting the functional Remote Associates Test using cognitive word associates and principles from a computational solver},
journal = {Knowledge-Based Systems},
year = {2019},
volume = {168},
pages = {1--9},
doi = {10.1016/j.knosys.2018.12.023},
language = {english}
}