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.

Cohort and Rhyme Priming Emerge from the Multiplex Network Structure of the Mental Lexicon

“…Also, network representations rely on experiments, but once built, a network can then be used for testing a wide variety of conjectures. For instance, the same network of free associations has been used multiple times for detecting patterns of word learning [7][8][9], identifying individual creativity levels [2,3,11], or even predicting word production in clinical populations [16]. The increasing adoption of complex network models in the cognitive sciences can be beneficial in terms of quantifying large-scale patterns of language usage and acquisition, mainly because of the high versatility of network models [4,6,11,17,55].…”

“…Network Metrics. As indicated in many recent investigations about lexical retrieval in semantic and phonological subcomponents of the mental lexicon, network distance is a reliable proxy of word relatedness as it is predictive of lexical retrieval [3,11,15,57]. Network distance d ij between nodes i and j in a given network N is defined as the shortest number of links connecting i and j [58].…”

“…Cognitive network science is quickly rising as an interdisciplinary field exploring psychology with the quantitative tools derived from complex networks [1][2][3][4]. Through the lens of network theory, many recent studies investigated the cognitive representation of language, a system commonly called mental lexicon [5] and deeply influencing processes such as language learning [6][7][8][9][10], memory [11][12][13][14], creativity [3,15], and language decline in cognitive impairments [16][17][18]. It has to be underlined that these studies are only a small part of a much wider literature on the mental lexicon from psycholinguistics [5,[19][20][21].…”

“…Network science provided language scientists with quantitative ways of representing and analysing the structure of lexical items within the mental lexicon [1,4,12,22]. For instance, concepts such as percolation techniques were used for detecting patterns of word confusability in phonology [12,22], strategies of language learning in healthy and clinical populations of children [6,23], differences in the levels of creativity of individual healthy subjects [3,11], or differences in the production of words in people with aphasia [17,18] or Alzheimer's disease [24]. However, the above studies considered only one aspect of language for establishing similarities among words, e.g., building single-layer networks including only phonological similarities among words [12].…”

“…More recent approaches have modelled a semantic network as a web of free associations among concepts [3,11,15], i.e., relationships based on memory rather than on any strict definition of feature sharing. The importance of network distance for quantifying patterns of lexical retrieval was recently underlined in the recent work by Kenett et al [15].…”