Advertisement

Investigating Creativity from a Semantic Network Perspective

  • Yoed N. KenettEmail author
Chapter

Abstract

Semantic memory plays a role in the creative process, either as an integral component or as the basis upon which executive functions operate on. Yet, due to the challenge of representing semantic memory, the relationship between semantic memory and creativity has not been thoroughly investigated. In recent years, computational network science tools are increasingly being applied at the cognitive level to examine language and memory systems. Network science is based on mathematical graph theory, providing quantitative methods to investigate complex systems as networks. Here, a series of semantic network studies aimed at investigating different facets of creativity in low- and high-creative individuals will be reviewed. These studies include representing their structure of semantic memory (both at the group and individual level), simulating uncontrolled search processes over their semantic memory, examining the relation of semantic memory structure to creative achievement and fluid intelligence, and relating flexibility of thought to the robustness of their semantic networks to attack. Finally, a general theory relating semantic memory structure to typical and atypical semantic processing will be presented and its relation to individual differences in creativity will be discussed. These studies demonstrate how the role of semantic memory in creativity can be investigated via quantitative measures of connectivity, distance, and structure of semantic networks. Thus, the application of network science tools to study creativity provides a quantitative and direct investigation of theories on creativity. Importantly, network science offers powerful tools in quantitatively studying different facets of high-level cognitive constructs such as creativity.

References

  1. Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122(3), 558–569.  https://doi.org/10.1037/a0038693.CrossRefGoogle Scholar
  2. Abraham, A. (2014). Creative thinking as orchestrated by semantic processing versus cognitive control brain networks [Perspective]. Frontiers in Human Neuroscience, 8, 95.  https://doi.org/10.3389/fnhum.2014.00095.CrossRefGoogle Scholar
  3. Abraham, A., & Bubic, A. (2015). Semantic memory as the root of imagination [Opinion]. Frontiers in Psychology, 6.  https://doi.org/10.3389/fpsyg.2015.00325.
  4. Acar, S., & Runco, M. A. (2014). Assessing associative distance among ideas elicited by tests of divergent thinking. Creativity Research Journal, 26(2), 229–238.  https://doi.org/10.1080/10400419.2014.901095.CrossRefGoogle Scholar
  5. Arbesman, S., Strogatz, S. H., & Vitevitch, M. S. (2010). The structure of phonological networks across multiple languages. Entropy, 12(3), 327–337.CrossRefGoogle Scholar
  6. Ardila, A., Ostrosky-Solís, F., & Bernal, B. (2006). Cognitive testing toward the future: The example of semantic verbal fluency (ANIMALS). International Journal of Psychology, 41(5), 324–332.  https://doi.org/10.1080/00207590500345542.CrossRefGoogle Scholar
  7. Barabási, A.-L. (2016). Network science. Cambridge University Press.Google Scholar
  8. Baronchelli, A., Ferrer-i-Cancho, R., Pastor-Satorras, R., Chater, N., & Christiansen, M. H. (2013). Networks in cognitive science. Trends in Cognitive Sciences, 17(7), 348–360.  https://doi.org/10.1016/j.tics.2013.04.010.CrossRefGoogle Scholar
  9. Barr, N., Pennycook, G., Stolz, J. A., & Fugelsang, J. A. (2014). Reasoned connections: A dual-process perspective on creative thought. Thinking & Reasoning, 21(1), 61–75.  https://doi.org/10.1080/13546783.2014.895915.CrossRefGoogle Scholar
  10. Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network coupling supports creative idea production. Scientific Reports, 5, 10964,  https://doi.org/10.1038/srep10964.
  11. Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016a). Creative cognition and brain network dynamics. Trends in Cognitive Sciences, 20(2), 87–95.  https://doi.org/10.1016/j.tics.2015.10.004.CrossRefGoogle Scholar
  12. Beaty, R. E., Kaufman, S. B., Benedek, M., Jung, R. E., Kenett, Y. N., Jauk, E., et al. (2016b). Personality and complex brain networks: The role of openness to experience in default network efficiency. Human Brain Mapping, 37(2), 773–779.  https://doi.org/10.1002/hbm.23065.CrossRefGoogle Scholar
  13. Beaty, R. E., & Silvia, P. J. (2012). Why do ideas get more creative over time? An executive interpretation of the serial order effect in divergent thinking tasks. Psychology of Aesthetics, Creativity and the Arts, 6(4), 309–319.CrossRefGoogle Scholar
  14. Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., & Benedek, M. (2014). The roles of associative and executive processes in creative cognition. Memory & Cognition, 42(7), 1–12.  https://doi.org/10.3758/s13421-014-0428-8.CrossRefGoogle Scholar
  15. Beketayev, K., & Runco, M. A. (2016). Scoring divergent thinking tests by computer with a semantics-based algorithm. Europe’s Journal of Psychology, 12(2), 210–220.  https://doi.org/10.5964/ejop.v12i2.1127.CrossRefGoogle Scholar
  16. Bendetowicz, D., Urbanski, M., Aichelburg, C., Levy, R., & Volle, E. (2017). Brain morphometry predicts individual creative potential and the ability to combine remote ideas. Cortex, 86, 216–229.CrossRefGoogle Scholar
  17. Benedek, M., Franz, F., Heene, M., & Neubauer, A. C. (2012a). Differential effects of cognitive inhibition and intelligence on creativity. Personality and Individual Differences, 53(4), 480–485.  https://doi.org/10.1016/j.paid.2012.04.014.CrossRefGoogle Scholar
  18. Benedek, M., Jauk, E., Sommer, M., Arendasy, M., & Neubauer, A. C. (2014). Intelligence, creativity, and cognitive control: The common and differential involvement of executive functions in intelligence and creativity. Intelligence, 46, 73–83.  https://doi.org/10.1016/j.intell.2014.05.007.CrossRefGoogle Scholar
  19. Benedek, M., Kenett, Y. N., Umdasch, K., Anaki, D., Faust, M., & Neubauer, A. C. (2017). How semantic memory structure and intelligence contribute to creative thought: A network science approach. Thinking & Reasoning, 23(2), 158–183.  https://doi.org/10.1080/13546783.2016.1278034.CrossRefGoogle Scholar
  20. Benedek, M., Könen, T., & Neubauer, A. C. (2012b). Associative abilities underlying creativity. Psychology of Aesthetics, Creativity and the Arts, 6(3), 273–281.CrossRefGoogle Scholar
  21. Benedek, M., & Neubauer, A. C. (2013). Revisiting Mednick’s model on creativity-related differences in associative hierarchies. Evidence for a common path to uncommon thought. The Journal of Creative Behavior, 47(4), 273–289.  https://doi.org/10.1002/jocb.35.CrossRefGoogle Scholar
  22. Bilder, R. M., & Knudsen, K. S. (2014). Creative cognition and systems biology on the edge of chaos [Opinion]. Frontiers in Psychology, 5, 1104.  https://doi.org/10.3389/fpsyg.2014.01104.CrossRefGoogle Scholar
  23. Body, R., & Muskett, T. (2012). Pandas and penguins, monkeys and caterpillars: Problems of cluster analysis in semantic verbal fluency. Qualitative Research in Psychology, 10(1), 28–41.  https://doi.org/10.1080/14780887.2011.586104.CrossRefGoogle Scholar
  24. Borge-Holthoefer, J., & Arenas, A. (2010). Semantic networks: Structure and dynamics. Entropy, 12(5), 1264–1302.CrossRefGoogle Scholar
  25. Borge-Holthoefer, J., Moreno, Y., & Arenas, A. (2011). Modeling abnormal priming in Alzheimer’s patients with a free association network. PLoS ONE, 6(8), e22651.  https://doi.org/10.1371/journal.pone.0022651.CrossRefGoogle Scholar
  26. Borodkin, K., Kenett, Y. N., Faust, M., & Mashal, N. (2016). When pumpkin is closer to onion than to squash: The structure of the second language lexicon. Cognition, 156, 60–70.  https://doi.org/10.1016/j.cognition.2016.07.014.CrossRefGoogle Scholar
  27. Bourgin, D. D., Abbott, J. T., Griffiths, T. L., Smith, K. A., & Vul, E. (2014). Empirical evidence for markov chain Monte Carlo in memory search. In Proceedings of the 36th Annual Conference of the Cognitive Science Society, Boston, MA.Google Scholar
  28. Bowden, E. M., & Jung-Beeman, M. (2003). One hundred forty-four compound remote associate problems: Short insight-like problems with one-word solutions. Behavioral Research, Methods, Instruments, and Computers, 35, 634–639.CrossRefGoogle Scholar
  29. Brandes, U., Borgatti, S. P., & Freeman, L. C. (2016). Maintaining the duality of closeness and betweenness centrality. Social Networks, 44, 153–159.  https://doi.org/10.1016/j.socnet.2015.08.003.CrossRefGoogle Scholar
  30. Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., et al. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences, 112(37), 11678–11683.  https://doi.org/10.1073/pnas.1422487112.CrossRefGoogle Scholar
  31. Brysbaert, M., Ameel, E., & Storms, G. (2014). Bilingual semantic memory: A new hypothesis. In R. R. Heredia & J. Altarriba (Eds.), Foundations of Bilingual memory (pp. 133–146). New York, NY: Springer.CrossRefGoogle Scholar
  32. Bullmore, E. T., & Sporns, O. (2012). The economy of brain network organization. Nature Review Neuroscience, 13(5), 336–349.  https://doi.org/10.1038/nrn3214.CrossRefGoogle Scholar
  33. Burke, D. M., MacKay, D. G., Worthley, J. S., & Wade, E. (1991). On the tip of the tongue: What causes word finding failures in young and older adults? Journal of Memory and Language, 30(5), 542–579.  https://doi.org/10.1016/0749-596X(91)90026-G.CrossRefGoogle Scholar
  34. Campbell, D. T. (1960). Blind variation and selective retentions in creative thought as in other knowledge processes. Psychological Review, 67(6), 380–400.CrossRefGoogle Scholar
  35. Capitán, J. A., Borge-Holthoefer, J., Gómez, S., Martinez-Romo, J., Araujo, L., Cuesta, J. A., et al. (2012). Local-based semantic navigation on a networked representation of information. PLoS ONE, 7(8), e43694.  https://doi.org/10.1371/journal.pone.0043694.CrossRefGoogle Scholar
  36. Carson, S. H. (2011). Creativity and psychopathology: A shared vulnerability model. Canadian Journal of Psychiatry, 56(3), 144–153.CrossRefGoogle Scholar
  37. Carson, S. H. (2014). Leveraging the “mad genius” debate: Why we need a neuroscience of creativity and psychopathology [Opinion]. Frontiers in Human Neuroscience, 8, 771.  https://doi.org/10.3389/fnhum.2014.00771.CrossRefGoogle Scholar
  38. Chai, L. R., Mattar, M. G., Blank, I. A., Fedorenko, E., & Bassett, D. S. (2016). Functional network dynamics of the language system. Cerebral Cortex, 26(11), 4148–4159.  https://doi.org/10.1093/cercor/bhw238.CrossRefGoogle Scholar
  39. Chrysikou, E. G., Motyka, K., Nigro, C., Yang, S.-I., & Thompson-Schill, S. L. (2016). Functional fixedness in creative thinking tasks depends on stimulus modality. Psychology of Aesthetics, Creativity, and the Arts, 10(4), 425–435.  https://doi.org/10.1037/aca0000050.CrossRefGoogle Scholar
  40. Cohen, R., & Havlin, S. (2010). Complex networks: Structure, robustness and function. Cambridge, UK: University Press.CrossRefGoogle Scholar
  41. Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82, 407–428.CrossRefGoogle Scholar
  42. Craig, J., & Baron-Cohen, S. (1999). Creativity and imagination in autism and Asperger Syndrome. Journal of Autism and Developmental Disorders, 29(4), 319–326.  https://doi.org/10.1023/A:1022163403479.CrossRefGoogle Scholar
  43. De Deyne, S., Elvevåg, B., Hui, C. L. M., Poon, V. W. Y., & Chen, E. Y. H. (2016a). Rich semantic networks applied to schizophrenia: A new framework. Schizophrenia Research, 176(2–3), 454–455.  https://doi.org/10.1016/j.schres.2016.05.016.CrossRefGoogle Scholar
  44. De Deyne, S., Kenett, Y. N., Anaki, D., Faust, M., & Navarro, D. J. (2016b). Large-scale network representations of semantics in the mental lexicon. In M. N. Jones (Ed.), Big data in cognitive science: From methods to insights (pp. 174–202). New York, NY: Psychology Press: Taylor & Francis.Google Scholar
  45. De Deyne, S., & Storms, G. (2008). Word association: Network and semantic properties. Behavior Research Methods, 40(1), 213–231.CrossRefGoogle Scholar
  46. De Deyne, S., Verheyen, S., & Storms, G. (2016c). Structure and organization of the mental lexicon: A network approach derived from syntactic dependency relations and word associations. In A. Mehler, P. Blanchard, B. Job, & S. Banish (Eds.), Towards a theoretical framework for analyzing complex linguistic networks (pp. 47–79). Springer.Google Scholar
  47. Den-Heyer, K., & Briand, K. (1986). Priming single digit numbers: Automatic spreading activation dissipates as a function of semantic distance. American Journal of Psychology, 99(5), 315–340.CrossRefGoogle Scholar
  48. Doron, K. W., Bassett, D. S., & Gazzaniga, M. S. (2012). Dynamic network structure of interhemispheric coordination. Proceedings of the National Academy of Sciences, 109(46), 18661–18668.  https://doi.org/10.1073/pnas.1216402109.CrossRefGoogle Scholar
  49. Doumit, S., Marupaka, N., & Minai, A. A. (2013). Thinking in prose and poetry: A semantic neural model. Paper presented at the Neural Networks (IJCNN), The 2013 International Joint Conference on Neural Networks (IJCNN).Google Scholar
  50. Durso, F. T., Rea, C. B., & Dayton, T. (1994). Graph-theoretic confirmation of restructuring during insight. Psychological Science, 5, 94–98.CrossRefGoogle Scholar
  51. Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 1–26.CrossRefGoogle Scholar
  52. Faust, M., & Kenett, Y. N. (2014). Rigidity, chaos and integration: Hemispheric interaction and individual differences in metaphor comprehension. Frontiers in Human Neuroscience, 8(511), 1–10.  https://doi.org/10.3389/fnhum.2014.00511.CrossRefGoogle Scholar
  53. Forthmann, B., Gerwig, A., Holling, H., Çelik, P., Storme, M., & Lubart, T. (2016). The be-creative effect in divergent thinking: The interplay of instruction and object frequency. Intelligence, 57, 25–32.  https://doi.org/10.1016/j.intell.2016.03.005.CrossRefGoogle Scholar
  54. Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3–5), 75–174.  https://doi.org/10.1016/j.physrep.2009.11.002.CrossRefGoogle Scholar
  55. Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.  https://doi.org/10.2307/3033543.CrossRefGoogle Scholar
  56. Gabora, L. (2010). Revenge of the “Neurds”: Characterizing creative thought in terms of the structure and dynamics of memory. Creativity Research Journal, 22(1), 1–13.  https://doi.org/10.1080/10400410903579494.CrossRefGoogle Scholar
  57. Goñi, J., Arrondo, G., Sepulcre, J., Martincorena, I., Vélez de Mendizábal, N., Corominas-Murtra, B., et al. (2011). The semantic organization of the animal category: Evidence from semantic verbal fluency and network theory. Cognitive Processing, 12(2), 183–196.  https://doi.org/10.1007/s10339-010-0372-x.CrossRefGoogle Scholar
  58. Green, A. E. (2016). Creativity, within reason: Semantic distance and dynamic state creativity in relational thinking and reasoning. Current Directions in Psychological Science, 25(1), 28–35.  https://doi.org/10.1177/0963721415618485.CrossRefGoogle Scholar
  59. Griffiths, T. L., Steyvers, M., & Firl, A. (2007). Google and the mind: Predicting fluency with PageRank. Psychological Science, 18(12), 1069–1076.  https://doi.org/10.1111/j.1467-9280.2007.02027.x.CrossRefGoogle Scholar
  60. Groborz, M., & Neçka, E. (2003). Creativity and cognitive control: Explorations of generation and evaluation skills. Creativity Research Journal, 15(2–3), 183–197.  https://doi.org/10.1207/S15326934crj152&3_09.CrossRefGoogle Scholar
  61. Gruszka, A., & Neçka, E. (2002). Priming and acceptance of close and remote associations by creative and less creative people. Creativity Research Journal, 14(2), 193–205.CrossRefGoogle Scholar
  62. Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., et al. (2015). Controllability of structural brain networks. Nature Communications, 6.  https://doi.org/10.1038/ncomms9414.
  63. Gupta, N., Jang, Y., Mednick, S. C., & Huber, D. E. (2012). The road not taken: Creative solutions require avoidance of high-frequency responses. Psychological Science, 23(3), 288–294.  https://doi.org/10.1177/0956797611429710.CrossRefGoogle Scholar
  64. Hahn, L. W. (2008). Overcoming the limitations of single-response free associations. Electronic Journal of Integrative Biosciences, 5(1), 25–36.Google Scholar
  65. Harbison, J. I., & Haarmann, H. J. (2014). Automated scoring of originality using semantic representations. In P. Bello, M. Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th Annual Meeting of the Cognitive Science Society (pp. 2327–2332). Austin, TX: Cognitive Science Society.Google Scholar
  66. Hass, R. W. (2016a). Conceptual expansion during divergent thinking. Paper presented at the Proceedings of the 38th Annual Meeting of the Cognitive Science Society, Philadelphia, PAGoogle Scholar
  67. Hass, R. W. (2016b). Tracking the dynamics of divergent thinking via semantic distance: Analytic methods and theoretical implications. Memory & Cognition, 45(2), 233–244.  https://doi.org/10.3758/s13421-016-0659-y.CrossRefGoogle Scholar
  68. Hilgetag, C. C., & Hütt, M.-T. (2014). Hierarchical modular brain connectivity is a stretch for criticality. Trends in Cognitive Sciences, 18(3), 114–115.  https://doi.org/10.1016/j.tics.2013.10.016.CrossRefGoogle Scholar
  69. Hills, T. T., Maouene, M., Maouene, J., Sheya, A., & Smith, L. (2009). Longitudinal analysis of early semantic networks: Preferential attachment or preferential acquisition? Psychological Science, 20(6), 729–739.  https://doi.org/10.1111/j.1467-9280.2009.02365.x.CrossRefGoogle Scholar
  70. Humphries, M. D., & Gurney, K. (2008). Network ‘small-world-ness’: A quantitative method for determining canonical network equivalence. PLoS ONE, 3(4), e0002051.  https://doi.org/10.1371/journal.pone.0002051.CrossRefGoogle Scholar
  71. Jauk, E., Benedek, M., & Neubauer, A. C. (2014). The road to creative achievement: A latent variable model of ability and personality predictors. European Journal of Personality, 28(1), 95–105.  https://doi.org/10.1002/per.1941.CrossRefGoogle Scholar
  72. Jones, M. N., Willits, J., & Dennis, S. (2015). Models of semantic memory. In J. Busemeyer & J. Townsend (Eds.), Oxford handbook of mathematical and computational psychology (pp. 232–254). Oxford, UK: Oxford University Press.Google Scholar
  73. Kajić, I., Gosmann, J., Stewart, T. C., Wennekers, T., & Eliasmith, C. (2017). A spiking neuron model of word associations for the remote associates test. Frontiers in Psychology, 8(99), 99.  https://doi.org/10.3389/fpsyg.2017.00099.CrossRefGoogle Scholar
  74. Karuza, E. A., Thompson-Schill, S. L., & Bassett, D. S. (2016). Local patterns to global architectures: Influences of network topology on human learning. Trends in Cognitive Sciences, 20(8), 629–640.  https://doi.org/10.1016/j.tics.2016.06.003.CrossRefGoogle Scholar
  75. Kaufman, S. B. (2014). The controlled chaos of creativity. In S. B. Kaufman (Ed.), Beuatiful minds. Scientific American.Google Scholar
  76. Kaufman, S. B., & Paul, E. S. (2014). Creativity and schizophrenia spectrum disorders. Frontiers in Psychology, 5, 1145.  https://doi.org/10.3389/fpsyg.2014.01145.CrossRefGoogle Scholar
  77. Kenett, Y. N. (2018). Going the extra creative mile: the role of semantic distance in creativity—Theory, research, and measurement. In R. E. Jung & O. Vartanian (Eds.), The Cambridge handbook of the neuroscience of creativity (pp. 233–248). New York, NY: Cambridge University Press.Google Scholar
  78. Kenett, Y. N., Anaki, D., & Faust, M. (2014). Investigating the structure of semantic networks in low and high creative persons. Frontiers in Human Neuroscience, 8(407), 1–16.  https://doi.org/10.3389/fnhum.2014.00407.CrossRefGoogle Scholar
  79. Kenett, Y. N., & Austerweil, J. L. (2016). Examining search processes in low and high creative individuals with random walks. In A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (pp. 313–318). Austin, TX: Cognitive Science Society.Google Scholar
  80. Kenett, Y. N., Beaty, R. E., Silvia, P. J., Anaki, D., & Faust, M. (2016a). Structure and flexibility: Investigating the relation between the structure of the mental lexicon, fluid intelligence, and creative achievement. Psychology of Aesthetics, Creativity, and the Arts, 10(4), 377–388.  https://doi.org/10.1037/aca0000056.CrossRefGoogle Scholar
  81. Kenett, Y. N., Gold, R., & Faust, M. (2016b). The hyper-modular associative mind: A computational analysis of associative responses of persons with Asperger Syndrome. Language and Speech, 59(3), 297–317.  https://doi.org/10.1177/0023830915589397.CrossRefGoogle Scholar
  82. Kenett, Y. N., Kenett, D. Y., Ben-Jacob, E., & Faust, M. (2011). Global and local features of semantic networks: Evidence from the Hebrew mental lexicon. PLoS ONE, 6(8), e23912.  https://doi.org/10.1371/journal.pone.0023912.CrossRefGoogle Scholar
  83. Kenett, Y. N., Levi, E., Anaki, D., & Faust, M. (2017). The semantic distance task: Quantifying semantic distance with semantic network path length. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(9), 1470–1489.  https://doi.org/10.1037/xlm0000391.Google Scholar
  84. Kenett, Y. N., Levy, O., Kenett, D. Y., Stanley, H. E., Faust, M., & Havlin, S. (2018). Flexibility of thought in high creative individuals represented by percolation analysis. Proceedings of the National Academy of Science, 115(5), 867–872.  https://doi.org/10.1073/pnas.1717362115.CrossRefGoogle Scholar
  85. Kenett, Y. N., Wechsler-Kashi, D., Kenett, D. Y., Schwartz, R. G., Ben Jacob, E., & Faust, M. (2013). Semantic organization in children with Cochlear Implants: Computational analysis of verbal fluency. Frontiers in Psychology, 4(543), 1–11.  https://doi.org/10.3389/fpsyg.2013.00543.CrossRefGoogle Scholar
  86. Kéri, S. (2011). Solitary minds and social capital: Latent inhibition, general intellectual functions and social network size predict creative achievements. Psychology of Aesthetics, Creativity, and the Arts, 5(3), 215–221.  https://doi.org/10.1037/a0022000.CrossRefGoogle Scholar
  87. Koschützki, D., Lehmann, K. A., Peeters, L., Richter, S., Tenfelde-Podehl, D., & Zlotowski, O. (2005). Centrality indices. In U. Brandes, & T. Erlebach (Eds.), Network analysis: Methodological Foundations (pp. 16–61). Springer.Google Scholar
  88. Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.CrossRefGoogle Scholar
  89. Lee, C. S., & Therriault, D. J. (2013). The cognitive underpinnings of creative thought: A latent variable analysis exploring the roles of intelligence and working memory in three creative thinking processes. Intelligence, 41, 306–320.CrossRefGoogle Scholar
  90. Lee, S.-A., Kenett, Y. N., Lam, M., Collinson, S. L., Chen, E. Y. H., Keefe, R. S. E., et al. (under review). The structure of the animal category in persons with schizophrenia: A network science approach.Google Scholar
  91. Lerner, A. J., Ogrocki, P. K., & Thomas, P. J. (2009). Network graph analysis of category fluency testing. Cognitive and Behavioral Neurology, 22(1), 45–52.  https://doi.org/10.1097/WNN.0b013e318192ccaf.CrossRefGoogle Scholar
  92. Madore, K. P., Addis, D. R., & Schacter, D. L. (2015). Creativity and memory: Effects of an episodic-specificity induction on divergent thinking. Psychological Science, 26(9), 1461–1468.  https://doi.org/10.1177/0956797615591863.CrossRefGoogle Scholar
  93. Mandera, P., Keuleers, E., & Brysbaert, M. (2015). How useful are corpus-based methods for extrapolating psycholinguistic variables? The Quarterly Journal of Experimental Psychology, 8, 1628–1642.  https://doi.org/10.1080/17470218.2014.988735.CrossRefGoogle Scholar
  94. Mandera, P., Keuleers, E., & Brysbaert, M. (2017). Explaining human performance in psycholinguistic tasks with models of semantic similarity based on prediction and counting: A review and empirical validation. Journal of Memory and Language, 92, 57–78.  https://doi.org/10.1016/j.jml.2016.04.001.CrossRefGoogle Scholar
  95. Martindale, C. (1995). Creativity and connectionism. In S. M. Smith, T. B. Ward, & R. A. Finke (Eds.), The creative cognition approach (pp. 249–268). Camrbidge, MA: M.I.T. Press.Google Scholar
  96. Marupaka, N., Iyer, L. R., & Minai, A. A. (2012). Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking. Neural Networks, 32, 147–158.  https://doi.org/10.1016/j.neunet.2012.02.004.CrossRefGoogle Scholar
  97. McRae, K., & Jones, M. N. (2013). Semantic memory. In D. Reisberg (Ed.), The Oxford handbook of cognitive psychology (pp. 206–219). Oxford, UK: Oxford University Press.Google Scholar
  98. Medaglia, J. D., Gu, S., Pasqualetti, F., Ashare, R. L., Lerman, C., Kable, J., et al. (2016). Cognitive control in the controllable connectome. arXiv:1606.09185.
  99. Medaglia, J. D., Lynall, M.-E., & Bassett, D. S. (2015a). Cognitive network neuroscience. Journal of Cognitive Neuroscience, 27(8), 1471–1491.  https://doi.org/10.1162/jocn_a_00810.CrossRefGoogle Scholar
  100. Medaglia, J. D., Satterthwaite, T. D., Moore, T. M., Ruparel, K., Gur, R. C., Gur, R. E., et al. (2015b). Flexible traversal through diverse brain states underlies executive function in normative neurodevelopment. arXiv:1510.08780.
  101. Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69(3), 220–232.CrossRefGoogle Scholar
  102. Mendelsohn, G. A. (1976). Associative and attentional processes in creative performance1. Journal of Personality, 44(2), 341–369.  https://doi.org/10.1111/j.1467-6494.1976.tb00127.x.CrossRefGoogle Scholar
  103. Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4.  https://doi.org/10.3389/fnins.2010.00200.
  104. Milgram, S. (1967). The small world problem. Psychological Today, 1, 62–67.Google Scholar
  105. Morais, A. S., Olsson, H., & Schooler, L. J. (2013). Mapping the structure of semantic memory. Cognitive Science, 37(1), 125–145.  https://doi.org/10.1111/cogs.12013.CrossRefGoogle Scholar
  106. Moreno, S., & Neville, J. (2013). Network hypothesis testing using mixed Kronecker product graph models. Paper presented at the Proceedings of the 13th IEEE International Conference on Data Mining,Google Scholar
  107. Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences USA, 103, 8577–8582.CrossRefGoogle Scholar
  108. Newman, M. E. J. (2010). Networks: An introduction. Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
  109. Nijstad, B. A., De Dreu, C. K. W., Rietzschel, E. F., & Baas, M. (2010). The dual pathway to creativity model: Creative ideation as a function of flexibility and persistence. European Review of Social Psychology, 21(1), 34–77.  https://doi.org/10.1080/10463281003765323.CrossRefGoogle Scholar
  110. Nusbaum, E. C., & Silvia, P. J. (2011). Are intelligence and creativity really so different? Fluid intelligence, executive processes, and strategy use in divergent thinking. Intelligence, 39(1), 36–45.CrossRefGoogle Scholar
  111. Olteţeanu, A.-M., & Falomir, Z. (2015). comRAT-C: A computational compound remote associates test solver based on language data and its comparison to human performance. Pattern Recognition Letters, 67(Part 1), 81–90.  https://doi.org/10.1016/j.patrec.2015.05.015.CrossRefGoogle Scholar
  112. Olteţeanu, A.-M., & Falomir, Z. (2016). Object replacement and object composition in a creative cognitive system. Towards a computational solver of the alternative uses test. Cognitive Systems Research, 39, 15–32.  https://doi.org/10.1016/j.cogsys.2015.12.011.CrossRefGoogle Scholar
  113. Pan, X., & Yu, H. (2016). Different effects of cognitive shifting and intelligence on creativity. The Journal of Creative Behavior.  https://doi.org/10.1002/jocb.144.CrossRefGoogle Scholar
  114. Papo, D., Buldú, J. M., Boccaletti, S., & Bullmore, E. T. (2014). Complex network theory and the brain [ https://doi.org/10.1098/rstb.2013.0520]. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 369(1653).  https://doi.org/10.1098/rstb.2013.0520.
  115. Radel, R., Davranche, K., Fournier, M., & Dietrich, A. (2015). The role of (dis)inhibition in creativity: Decreased inhibition improves idea generation. Cognition, 134, 110–120.  https://doi.org/10.1016/j.cognition.2014.09.001.CrossRefGoogle Scholar
  116. Rossman, E., & Fink, A. (2010). Do creative people use shorter association pathways? Personality and Individual Differences, 49, 891–895.CrossRefGoogle Scholar
  117. Runco, M. A., & Acar, S. (2012). Divergent thinking as an indicator of creative potential. Creativity Research Journal, 24(1), 66–75.  https://doi.org/10.1080/10400419.2012.652929.CrossRefGoogle Scholar
  118. Saberi, A. A. (2015). Recent advances in percolation theory and its applications. Physics Reports, 578, 1–32.  https://doi.org/10.1016/j.physrep.2015.03.003.CrossRefGoogle Scholar
  119. Schilling, M. A. (2005). A “small-world” network model of cognitive insight. Creativity Research Journal, 17(2–3), 131–154.  https://doi.org/10.1080/10400419.2005.9651475.CrossRefGoogle Scholar
  120. Shai, S., Kenett, D. Y., Kenett, Y. N., Faust, M., Dobson, S., & Havlin, S. (2015). Critical tipping point distinguishing two types of transitions in modular network structures. Physical Review E, 92(6), 062805.  https://doi.org/10.1103/PhysRevE.92.062805.CrossRefGoogle Scholar
  121. Siegel, D. J. (2010). Mindsight: The new science of personal transformation. New York: Bantam Books.Google Scholar
  122. Siew, C. S. Q. (2013). Community structure in the phonological network [Original Research]. Frontiers in Psychology, 4, 553.  https://doi.org/10.3389/fpsyg.2013.00553.CrossRefGoogle Scholar
  123. Siew, C. S. Q. (2016). The influence of 2-hop network density on spoken word recognition. Psychonomic Bulletin & Review, 24(2), 496–502.  https://doi.org/10.3758/s13423-016-1103-9.CrossRefGoogle Scholar
  124. Silvia, P. J. (2015). Intelligence and creativity are pretty similar after all. Educational Psychology Review, 27(4), 1–8.  https://doi.org/10.1007/s10648-015-9299-1.CrossRefGoogle Scholar
  125. Simonton, D. K. (2010). Creative thought as blind-variation and selective-retention: Combinatorial models of exceptional creativity. Physics of Life Reviews, 7(2), 190–194.  https://doi.org/10.1016/j.plrev.2010.05.004.CrossRefGoogle Scholar
  126. Simonton, D. K. (2013). Creative thought as blind variation and selective retention: Why creativity is inversely related to sightedness. Journal of Theoretical and Philosophical Psychology, 33(4), 253–266.  https://doi.org/10.1037/a0030705.CrossRefGoogle Scholar
  127. Simonton, D. K. (2015). On praising convergent thinking: Creativity as blind variation and selective retention. Creativity Research Journal, 27(3), 262–270.  https://doi.org/10.1080/10400419.2015.1063877.CrossRefGoogle Scholar
  128. Smith, K. A., Huber, D. E., & Vul, E. (2013). Multiply-constrained semantic search in the remote associates test. Cognition, 128(1), 64–75.  https://doi.org/10.1016/j.cognition.2013.03.001.CrossRefGoogle Scholar
  129. Smith, K. A., & Vul, E. (2015). The role of sequential dependence in creative semantic search. Topics in Cognitive Science, 7(3), 543–546.  https://doi.org/10.1111/tops.12152.CrossRefGoogle Scholar
  130. Smith, S. M., & Ward, T. B. (2012). Cognition and the creation of ideas. In K. J. Holyoak & R. G. Morrison (Eds.), Oxford handbook of thinking and reasoning (pp. 456–474). Oxford, UK: Oxford University Press.Google Scholar
  131. Sowden, P. T., Pringle, A., & Gabora, L. (2014). The shifting sands of creative thinking: Connections to dual-process theory. Thinking & Reasoning, 21(1), 40–60.  https://doi.org/10.1080/13546783.2014.885464.CrossRefGoogle Scholar
  132. Stam, C. J. (2014). Modern network science of neurological disorders. Nature Review Neuroscience, 15(10), 683–695.  https://doi.org/10.1038/nrn3801.CrossRefGoogle Scholar
  133. Steyvers, M., Shiffrin, R. M., & Nelson, D. L. (2004). Word association spaces for predicting semantic similarity effects in episodic memory. In A. F. Healy (Ed.), Experimental cognitive psychology and its applications: Festchrift in honor of Lyle Bourne, Walter Kintsch, and Thomas Landauer (pp. 237–249). Washington, DC: American Psychological Association.Google Scholar
  134. Steyvers, M., & Tenenbaum, J. B. (2005). The large scale structure of semantic networks: Statistical analysis and a model of semantic growth. Cognitive Science, 29(1), 41–78.CrossRefGoogle Scholar
  135. Thompson, G. W., & Kello, C. (2014). Walking across Wikipedia: A scale-free network model of semantic memory retrieval. Frontiers in Psychology, 5, 86.  https://doi.org/10.3389/fpsyg.2014.00086.CrossRefGoogle Scholar
  136. Troyer, A. K. (2000). Normative data for clustering and switching on verbal fluency tasks. Journal of Clinical and Experimental Neuropsychology, 22(3), 370–378.  https://doi.org/10.1076/1380-3395(200006)22:3;1-V;FT370.CrossRefGoogle Scholar
  137. Turner, M. A. (1999). Generating novel ideas: Fluency performance in high-functioning and learning disabled individuals with autism. Journal of Child Psychology and Psychiatry, 40(2), 189–201.  https://doi.org/10.1111/1469-7610.00432.CrossRefGoogle Scholar
  138. Unsworth, N., Spillers, G. J., & Brewer, G. A. (2011). Variation in verbal fluency: A latent variable analysis of clustering, switching, and overall performance. The Quarterly Journal of Experimental Psychology, 64(3), 447–466.  https://doi.org/10.1080/17470218.2010.505292.CrossRefGoogle Scholar
  139. van Straaten, E. C. W., & Stam, C. J. (2013). Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI. European Neuropsychopharmacology: The Journal of the European College of Neuropsychopharmacology, 23(1), 7–18.  https://doi.org/10.1016/j.euroneuro.2012.10.010.CrossRefGoogle Scholar
  140. Vitevitch, M. S., & Castro, N. (2015). Using network science in the language sciences and clinic. International Journal of Speech-Language Pathology, 17(1), 13–25. doi: https://doi.org/10.3109/17549507.2014.987819.CrossRefGoogle Scholar
  141. Vitevitch, M. S., Chan, K. Y., & Goldstein, R. (2014). Insights into failed lexical retrieval from network science. Cognitive Psychology, 68, 1–32.  https://doi.org/10.1016/j.cogpsych.2013.10.002.CrossRefGoogle Scholar
  142. Vitevitch, M. S., Chan, K. Y., & Roodenrys, S. (2012). Complex network structure influences processing in long-term and short-term memory. Journal of Memory and Language, 67(1), 30–44.  https://doi.org/10.1016/j.jml.2012.02.008.CrossRefGoogle Scholar
  143. Vitevitch, M. S., Goldstein, R., & Johnson, E. (2016). Path-length and the misperception of speech: Insights from network science and psycholinguistics. In A. Mehler, A. Lücking, S. Banisch, P. Blanchard, & B. Job (Eds.), Towards a theoretical framework for analyzing complex linguistic networks (pp. 29–45). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  144. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(4), 440–442.CrossRefGoogle Scholar
  145. Wu, C., Zhong, S., & Chen, H. (2016). Discriminating the difference between remote and close association with relation to hite-matter structural connectivity. PLoS ONE, 11(10), e0165053.  https://doi.org/10.1371/journal.pone.0165053.CrossRefGoogle Scholar
  146. Yee, E., & Thompson-Schill, S. L. (2016). Putting concepts into context. Psychonomic Bulletin & Review, 23(4), 1015–1027.  https://doi.org/10.3758/s13423-015-0948-7.CrossRefGoogle Scholar
  147. Zabelina, D. L., Saporta, A., & Beeman, M. (2015). Flexible or leaky attention in creative people? Distinct patterns of attention for different types of creative thinking. Memory & Cognition, 44(3), 488–498.  https://doi.org/10.3758/s13421-015-0569-4.CrossRefGoogle Scholar
  148. Zeev-Wolf, M., Faust, M., Levkovitz, Y., Harpaz, Y., & Goldstein, A. (2015). Magnetoencephalographic evidence of early right hemisphere overactivation during metaphor comprehension in schizophrenia. Psychophysiology, 52(6), 770–781.  https://doi.org/10.1111/psyp.12408.CrossRefGoogle Scholar
  149. Zemla, J. C., Kenett, Y. N., Jun, K.-S., & Austerweil, J. L. (2016). U-INVITE: Estimating individual semantic networks from fluency data. In A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (pp. 1907–1912). Austin, TX: Cognitive Science Society.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations