Advertisement

Implicit Learning and Creativity in Human Networks: A Computational Model

  • Marwa Shekfeh
  • Ali A. Minai
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

With rare exceptions, new ideas necessarily emerge in the minds of individuals through the recombination of existing ideas, but the epistemic repertoire for this recombination is supplied largely by ideas the individual has acquired from external sources, including interaction with peers. When agents hear new ideas and integrate them into their minds, they also implicitly create potential new ideas which can then become explicit as new ideas through later introspection. In this research, we use a multi-agent model to study such implicit learning in a social network and its relationship with the number of unique novel ideas actually expressed by agents in the network. We focus on the impact of two crucial factors: (1) The structure of the social network; and (2) The selectivity of agents in accepting ideas from their peers. We look at both latent ideas, i.e., those that are still implicit in the minds of individual agents, and novel expressed ideas, i.e., those that are expressed for the first time in the network. The results show that both network structure and the selectivity of influence have significant impact on the outcomes – especially in a system with misinformation.

Notes

Acknowledgment

This work was supported in part by National Science Foundation INSPIRE grant BCS-1247971 to Ali Minai.

References

  1. 1.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–511 (1999)ADSMathSciNetCrossRefGoogle Scholar
  2. 2.
    Brown, V., Tumeo, M., Larey, T., Paulus, P.: Modeling cognitive interactions during group brainstorming. Small Group Res. 29, 495–526 (1998)CrossRefGoogle Scholar
  3. 3.
    Campbell, D.T.: Blind variation and selective retention in creative thought as in other knowledge processes. Psychol. Rev. 67, 380–400 (1960)CrossRefGoogle Scholar
  4. 4.
    Delgado, J., Pujol, J.M., Sangüesa, R.: Emergence of coordination in scale-free networks. Web Intell. Agent Syst. 1, 131–138 (2003)Google Scholar
  5. 5.
    Fauconnier, G., Turner, M.: The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. Basic Books, New York (2003)Google Scholar
  6. 6.
    Iyer, L.R., Doboli, S., Minai, A.A., Brown, V.R., Levine, D.S., Paulus, P.B.: Neural dynamics of idea generation and the effects of priming. Neural Netw. 22, 674–686 (2009)CrossRefGoogle Scholar
  7. 7.
    Lewicki, P., Czyzewska, M., Hoffman, H.: Unconscious acquisition of complex procedural knowledge. J. Exp. Psychol. Learn. Mem. Cogn. 13(4), 523–530 (1987)CrossRefGoogle Scholar
  8. 8.
    Mednick, S.: The associative basis of the creative process. Psychol. Rev. 69(3), 220–232 (1962)CrossRefGoogle Scholar
  9. 9.
    Minai, A.A., Iyer, L.R., Padur, D., Doboli, S.: A dynamic connectionist model of idea generation. In: Proceedings of the IJCNN 2009, pp. 2109–2116 (2009)Google Scholar
  10. 10.
    Nijstad, B.A., Stroebe, W.: How the group affects the mind: a cognitive model of idea generation in groups. Pers. Soc. Psychol. Rev. 3, 186–213 (2006)CrossRefGoogle Scholar
  11. 11.
    Reber, A.S.: Implicit learning of artificial grammars. J. Verbal Learn. Verbal Behav. 6(6), 855–863 (1967)CrossRefGoogle Scholar
  12. 12.
    Sen, O.: Effects of social network topology and options on norm emergence. In: Coordination, Organizations, Institutions and Norms in Agent Systems. LNCS, vol. 6069, pp. 211–222 (2010)Google Scholar
  13. 13.
    Shekfeh, M.: MANILA: a multi-agent framework for emergent associative learning and creativity in social networks. Ph.D. thesis, Department of Electrical Engineering and Computer Science, University of Cincinnati (2017)Google Scholar
  14. 14.
    Simonton, D.K.: Scientific creativity as constrained stochastic behavior: the integration of product, person, and process perspectives. Psychol. Bull. 129, 475–494 (2003)CrossRefGoogle Scholar
  15. 15.
    Villatoro, D., Sen, S., Sabater-Mir, J.: Topology and memory effect on convention emergence. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technologies, pp. 233–240 (2009)Google Scholar
  16. 16.
    Watts, D., Strogatz, S.: Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998)ADSCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of CincinnatiCincinnatiUSA

Personalised recommendations