Flexibility of thought in high creative individuals represented by percolation analysis

Significance Creative thinking requires flexibility, which facilitates the creation of novel and innovative ideas. However, so far its role in creativity has been measured via indirect measures. We propose a quantitative measure of flexibility based on the robustness of semantic memory networks to attack, assuming that the higher robustness, the higher the flexibility of the network. We show how the semantic network of high creative individuals is more robust to attack, thus more flexible. This is a direct computational investigation on flexibility of semantic memory and creativity. Our approach can be applied to more general questions such as high-level cognitive capacities and clinical populations suffering from atypical thought processes. 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.

[1]  S. Mednick The associative basis of the creative process. , 1962, Psychological review.

[2]  M. Vitevitch,et al.  Using network science in the language sciences and clinic , 2015, International journal of speech-language pathology.

[3]  Yoed N. Kenett,et al.  Investigating the structure of semantic networks in low and high creative persons , 2014, Front. Hum. Neurosci..

[4]  Donald G. MacKay,et al.  On the tip of the tongue: What causes word finding failures in young and older adults? , 1991 .

[5]  Mathias Benedek,et al.  How semantic memory structure and intelligence contribute to creative thought: a network science approach , 2017 .

[6]  J. Borge-Holthoefer,et al.  Modeling Abnormal Priming in Alzheimer's Patients with a Free Association Network , 2011, PloS one.

[7]  Matthijs Baas,et al.  The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence , 2010 .

[8]  仁 寺井,et al.  日本語版Remote Associates Testの作成と評価 , 2013 .

[9]  Reuven Cohen,et al.  Complex Networks: Structure, Robustness and Function , 2010 .

[10]  S. Boccaletti,et al.  Complex network theory and the brain , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[11]  Joseph L. Austerweil,et al.  U-INVITE: Estimating Individual Semantic Networks from Fluency Data , 2016, CogSci.

[12]  M. Benedek,et al.  Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity , 2014, Intelligence.

[13]  Daniel J. Navarro,et al.  Large-Scale Network Representations of Semantics in the Mental Lexicon , 2017 .

[14]  K. Holyoak,et al.  The Oxford handbook of thinking and reasoning , 2012 .

[15]  Steven M. Smith,et al.  Cognition and the Creation of Ideas , 2012 .

[16]  M. Spitzer,et al.  A cognitive neuroscience view of schizophrenic thought disorder. , 1997, Schizophrenia bulletin.

[17]  Joshua B. Tenenbaum,et al.  The Large-Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth , 2001, Cogn. Sci..

[18]  Alexandre Arenas,et al.  Topological versus Dynamical Robustness in a Lexical Network , 2012, Int. J. Bifurc. Chaos.

[19]  Nick Chater,et al.  Networks in Cognitive Science , 2013, Trends in Cognitive Sciences.

[20]  Michael S. Vitevitch,et al.  Insights into failed lexical retrieval from network science , 2014, Cognitive Psychology.

[21]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[22]  Alexandre Arenas,et al.  Semantic Networks: Structure and Dynamics , 2010, Entropy.

[23]  Abbas Ali Saberi,et al.  Recent advances in percolation theory and its applications , 2015, 1504.02898.

[24]  Miriam Faust,et al.  Metaphors and verbal creativity: The role of the right hemisphere , 2012, Laterality.

[25]  Michael N. Jones,et al.  Foraging in Semantic Fields: How We Search Through Memory , 2015, Top. Cogn. Sci..

[26]  N. Milgram,et al.  Creative thinking and creative performance in Israeli students. , 1976, Journal of educational psychology.

[27]  Xuan Pan,et al.  Different Effects of Cognitive Shifting and Intelligence on Creativity , 2018 .