Dynamic semantic networks for exploration of creative thinking

TL;DR

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.

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.

Figures (7)

• Figure 1: Workflow for monitoring of creative cognition with dynamic semantic networks.
• Figure 2: The subgraph of meanings $M$ in WordNet 3.1 does not have directed cycles when the edges remain directed, however, when all edges are converted into undirected edges using the graph operator $U$, the graph $U(M)$ becomes cyclic. One consequence of the structure of $M$ is that even for two monosemous words such as 'workspace' and 'yellow' the shortest path in the undirected graph $U(M)$ may not pass through the lowest common subsumer, which in this case is the root meaning vertex $M00001740$. In this example, the length of the shortest path between 'workspace' and 'yellow' is 12, whereas the distance between 'workspace' and 'yellow' through their lowest common subsumer $M00001740$ is 14. To avoid clutter in the image, we have added only a single word for each meaning vertex, however, many of the meaning vertices are subsumed by synsets of words. For example, both words 'yellow' and 'yellowness' subsume the meaning vertex $M04972838$.
• Figure 3: Modeling the creative design process of an 'Electric car' with a dynamic semantic network. The dynamics of the semantic network from the stage of idea generation to the stage of fully developed solution provides a useful, reproducible and fast computational tool for exploration of creative thinking.
• Figure 4: Graph composition of meaning vertices and word vertices for different WordNet 3.1 searches. (A) Adding words as subordinate vertices subsumed by meanings allows for computing the depth in the taxonomy and listing the meaning subsumers of words. (B) Adding words as subsumers of meanings allows for listing the meaning subvertices and leaves. (C) Adding two distinct words $\left\{ w_{1},w_{2}\right\}$ as subordinate vertices subsumed by meanings allows for computing their lowest common subsumer $\mathcal{K}\left(w_{1},w_{2}\right)$. (D) Adding two distinct words $\left\{ w_{1},w_{2}\right\}$ into an undirected graph allows for computing the shortest path distance between the two words, which may not pass through their lowest common subsumer. Different graph compositions are needed for path-based or information content-based quantification of word similarity.
• Figure 5: Pearson correlation matrix map with hierarchical clustering (dendrogram) based on the Pearson correlation distance between subjective human evaluation (HE) of word similarity for noun--noun pairs in the RG-65 dataset and 46 objective semantic similarity measures computed with the use of WordNet 3.1. The similarity measures segregate into two clusters, a larger cluster composed from information content-based or path-based similarity measures, and a smaller cluster composed from subsumer-based similarity measures. Formulas for the similarity measures are provided in the main text. Abbreviations: AN: Al-Mubaid--Nguyen, B: Braun-Blanquet, BHK: Blanchard--Harzallah--Kuntz, D: Dice, J: Jaccard, JC: Jiang--Conrath, K: Kulczyński, L: Lin, LBM: Li--Bandar--McLean, LC: Leacock--Chodorow, MGZ: Meng--Gu--Zhou, MHG: Meng--Huang--Gu, OO: Otsuka--Ochiai, R: Resnik, RMBB: Rada--Mili--Bicknell--Blettner, S: Simpson, SB: Sánchez--Batet, SBI: Sánchez--Batet--Isern, SVH: Seco--Veale--Hayes, WP: Wu--Palmer, YYW: Yuan--Yu--Wang, ZWG: Zhou--Wang--Gu.