Flexibility of thought in high creative individuals represented by percolation analysis

Abstract

Flexibility of thought is theorized to play a critical role in the ability of high creative individuals to generate novel and innovative ideas. However, this has been examined only through indirect behavioral measures. Here we use network percolation analysis (removal of links in a network whose strength is below an increasing threshold) to computationally examine the robustness of the semantic memory networks of low and high creative individuals. Robustness of a network indicates its flexibility and thus can be used to quantify flexibility of thought as related to creativity. This is based on the assumption that the higher the robustness of the semantic network, the higher its flexibility. Our analysis reveals that the semantic network of high creative individuals is more robust to network percolation compared with the network of low creative individuals and that this higher robustness is related to differences in the structure of the networks. Specifically, we find that this higher robustness is related to stronger links connecting between different components of similar semantic words in the network, which may also help to facilitate spread of activation over their network. Thus, we directly and quantitatively examine the relation between flexibility of thought and creative ability. Our findings support the associative theory of creativity, which posits that high creative ability is related to a flexible structure of semantic memory. Finally, this approach may have further implications, by enabling a quantitative examination of flexibility of thought, in both healthy and clinical populations.

Keywords: creativity; network science; percolation theory; thought flexibility.

Fig. 1.

(Left) LSC and (Right) HSC networks with links above TH = 0.1. The nodes are numbered according to their labels (Table S1) and colored from blue to red according to their percolation order. The colder (blue) the node is, the earlier it was disconnected from the giant component, and therefore, it is weaker connected to the entire network. Because the number of components is different in both networks, the scales are different.

Fig. 2.

(A) Percolation analysis of the empirical networks of the LSC and HSC networks. (B) Effect of noise on the percolation analysis of the LSC and HSC networks, with addition of noise with SD of 0.01 (LSC/HSC-noise) or without addition of noise (LSC/HSC-or). (C) An example of typical iteration of the percolation analysis on the shuffled links analysis in both networks.

Fig. 3.

Interlinks analysis for the LSC and HSC groups. (A) LSC and (B) HSC percolated components network layout. The components are sized according to the number of nodes in the component and numbered according to their disconnection order from the giant component. (C) Scatterplot of the matched interlinks of the two groups, where each circle represents an interlink between two matched components in both networks. Circles above/below the diagonal indicate that this interlink is stronger in the HSC/LSC network compared with the LSC/HSC network.