Dynamic semantic networks for exploration of creative thinking

TL;DR

This paper explores the use of dynamic semantic networks derived from lexical databases to analyze and monitor creative thinking in real-time, linking linguistic data to brain activity for enhanced understanding and support of creativity.

Contribution

It introduces a novel empirical approach using dynamic semantic networks and information-theoretic measures to analyze creative thought processes during design tasks.

Findings

Detected divergent thinking predicts success in design ideas

Dynamic semantic measures can monitor creative problem solving in real-time

Semantic networks relate to brain regions involved in concept processing

Abstract

Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.