Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-28T05:59:25.509Z Has data issue: false hasContentIssue false

29 - Computational Models of Creativity

from Part IV - Computational Modeling in Various Cognitive Fields

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

Creativity is typically defined as producing something that is novel, useful, and surprising. Such endeavor plays a critical role in the arts and scientific discovery. However, not all creativity is groundbreaking or historically important. As a common cognitive activity, creativity is amenable to scientific investigation leading to a process-based understanding, so it should be possible to propose models and write computer programs simulating the creativity process. However, the path from cognitive models to computational models is still not trodden as often as would be beneficial. This chapter reviews common concepts underlying many computational creativity efforts, namely idea generation, search, and evaluation. Two example computational models are described in more detail, namely the explicit-implicit interaction theory and the CreaCogs architecture. The chapter concludes with a discussion of current shortcomings and future directions for computational creativity as well as discussing promising avenues and successes of current models.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Al-Rifaie, M. M., & Bishop, M. (2015). Weak and strong computational creativity. In Besold, T. R., Schorlemmer, M., & Smaill, A. (Eds.), Computational Creativity Research: Towards Creative Machines (pp. 3749). Paris, France: Springer.Google Scholar
Ashby, F. G., & Hélie, S. (2011). A tutorial on computational cognitive neuroscience: modeling the neurodynamics of cognition. Journal of Mathematical Psychology, 55, 273289.Google Scholar
Augello, A., Infantino, I., Pilato, G., Rizzo, R., & Vella, F. (2015). Creativity evaluation in a cognitive architecture. Biologically Inspired Cognitive Architectures, 11, 2937.Google Scholar
Barsalou, L. (2003). Abstraction in perceptual symbol systems. Philosophical Transactions of the Royal Society of London, 358, 11771187.Google Scholar
Barsalou, L., & Wiemer-Hastings, K. (2005). Situating abstract concepts. In Pecher, D. & Zwaan, R. (Eds.), Grounding Cognition: The Role of Perception and Action in Memory, Language, and Thought (pp. 129163). New York, NY: Cambridge University Press.Google Scholar
Boden, M. A. (2004). The Creative Mind: Myths and Mechanisms (2nd ed.). London: Routledge.Google Scholar
Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate problems. Behavior Research Methods, Instruments, & Computers, 35 (4), 634639.Google Scholar
Brewer, W. F., & Treyens, J. C. (1981). Role of schemata in memory for places. Cognitive Psychology, 13 (2), 207230.Google Scholar
Calic, G., & Hélie, S. (2018). Creative sparks or paralysis traps? The effects of contradictions on creative processing and creative products. Frontiers in Psychology, 9, 1489.Google Scholar
Calic, G., Hélie, S., Bontis, N., & Mosakowski, E. (2019). Creativity from paradoxical experience: a theory of how individuals achieve creativity while adopting paradoxical frames. Journal of Knowledge Management, 23, 397418.Google Scholar
Calic, G., Mosakowski, E., Bontis, N., & Hélie, S. (2022). Is maximizing creativity good? The importance of elaboration and internal confidence in producing creative ideas. Knowledge Management Research & Practice, 20 , 776791.Google Scholar
Campbell, D. T. (1960). Blind variation and selective retention in creative thought as in other knowledge processes. Psychological Review, 67, 380400.Google Scholar
Chartier, S., & Proulx, R. (2005). NDRAM: a nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns. IEEE Transactions on Neural Networks, 16, 13931400.Google Scholar
Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82, 407428.Google Scholar
Csikszentmihalyi, M. (1996). Creativity: Flow and the Psychology of Discovery and Invention. New York, NY: HarperCollins.Google Scholar
Duch, W. (2006). Computational creativity. In Proceedings of the International Joint Conference on Neural Networks (pp. 435442). Vancouver, BC: IEEE Press.Google Scholar
Duncker, K. (1945). On problem solving. Psychological Monographs, 58, i113.CrossRefGoogle Scholar
Durso, F. T., Rea, C. B., & Dayton, T. (1994). Graph-theoretic confirmation of restructuring during insight. Psychological Science, 5, 9498.Google Scholar
Eppe, M., Maclean, E., Confalonieri, R., et al. (2018). A computational framework for conceptual blending. Artificial Intelligence, 256, 105129.Google Scholar
Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989). The structure-mapping engine: algorithm and examples. Artificial Intelligence, 41 (1), 163.Google Scholar
Fedor, A., Zachar, I., Szilagyi, A., Ollinger, M., de Vladar, H. P., & Szathmary, E. (2017). Cognitive architecture with evolutionary dynamics solves insight problem. Frontiers in Psychology, 8, 427.Google Scholar
Finke, R. A., Ward, T. B., & Smith, S. M. (1992). Creative Cognition: Theory, Research, and Applications. Cambridge, MA: MIT Press.Google Scholar
Gabora, L. (2005). Creative thought as a non-Darwinian evolutionary process. The Journal of Creative Behavior, 39, 262283.Google Scholar
Gärdenfors, P. (2004). Conceptual Spaces: The Geometry of Thought. Cambridge, MA: MIT Press.Google Scholar
Gentner, D. (1983). Structure-mapping: a theoretical framework for analogy. Cognitive Science, 7 (2), 155170.Google Scholar
Gilhooly, K. J., Fioratou, E., Anthony, S. H., & Wynn, V. (2007). Divergent thinking: strategies and executive involvement in generating novel uses for familiar objects. British Journal of Psychology, 98 (4), 611625.Google Scholar
Gray, K., Anderson, S., Chen, E. E., et al. (2019). “Forward flow”: a new measure to quantify free thought and predict creativity. American Psychologist, 74, 539554.Google Scholar
Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53 (4), 267293.Google Scholar
Guilford, J. P. (1967). The Nature of Human Intelligence. New York, NY: McGraw-Hill.Google Scholar
Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. New York, NY: Wiley.Google Scholar
Hélie, S., & Cousineau, D. (2014). The cognitive neuroscience of automaticity: behavioral and brain signatures. In Sun, M.-K. (Ed.), Advances in Cognitive and Behavioral Sciences (pp. 141159). New York, NY: Nova Science Publishers.Google Scholar
Hélie, S., Ell, S.W., & Ashby, F.G. (2015). Learning robust cortico-frontal associations with the basal ganglia: an integrative review. Cortex, 64, 123135.Google Scholar
Hélie, S., Proulx, R., & Lefebvre, B. (2011). Bottom-up learning of explicit knowledge using a Bayesian algorithm and a new Hebbian learning rule. Neural Networks, 24, 219232.Google Scholar
Hélie, S., Shamloo, F., Novak, K., & Foti, D. (2017). The roles of valuation and reward processing in cognitive function and psychiatric disorders. Annals of the New York Academy of Sciences, 1395, 3348.Google Scholar
Hélie, S., & Sun, R. (2008). Knowledge integration in creative problem solving. In Love, B. C., McRae, K., & Sloutsky, V. M. (Eds.) Proceedings of the 30th Annual Meeting of the Cognitive Science Society (pp. 16811686). Austin, TX: Cognitive Science Society.Google Scholar
Hélie, S., & Sun, R. (2009). Simulating incubation effects using the Explicit-Implicit Interaction with Bayes factor (EII-BF) model. In Proceedings of the International Joint Conference on Neural Networks (pp. 11991205). Atlanta, GA: IEEE Press.Google Scholar
Hélie, S., & Sun, R. (2010). Incubation, insight, and creative problem solving: a unified theory and a connectionist model. Psychological Review, 117 (3), 9941024.Google Scholar
Hofstadter, D. R., & Mitchell, M. (1994). The copycat project: a model of mental fluidity and analogy making. In Holyoak, K. & Barnden, J. (Eds.), Advances in Connectionist and Neural Computation Theory: Vol. 2. Analogical Connections (pp. 31112). Norwood, NJ: Ablex Publishing.Google Scholar
Indurkhya, B. (1999). An algebraic approach to modeling creativity of metaphor. In Nehaniv, C. L. (Ed.), Computation for Metaphors, Analogy, and Agents (pp. 292306). Cham: Springer.Google Scholar
Jennings, K. E. (2010). Developing creativity: artificial barriers in artificial intelligence. Minds and Machines, 20, 489501.Google Scholar
Johnson-Laird, P. N. (1988). Freedom and constraint in creativity. In Sternberg, R. J. (Ed.), The Nature of Creativity (pp. 202219). New York, NY: Cambridge University Press.Google Scholar
Jordanous, A. (2016). Four PPPPerspectives on computational creativity in theory and in practice. Connection Science, 28, 194216.Google Scholar
Kaufman, A. B., & Kaufman, J. C. (Eds.). (2015). Animal Creativity and Innovation. Oxford: Elsevier.Google Scholar
Kenett, Y. N. (2018). Investigating creativity from a semantic network perspective. In Kapoula, Z., Volle, E., Renoult, J., & Andreatta, M. (Eds.), Exploring Transdisciplinarity in Art and Sciences (pp. 4976). Cham: Springer.Google Scholar
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.Google Scholar
Kenett, Y. N., & Faust, M. (2019). A semantic network cartography of the creative mind. Trends in Cognitive Sciences, 23, 271274.Google Scholar
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 Sciences, 115, 867872.Google Scholar
Kim, K. H. (2006). Can we trust creativity tests? A review of the Torrance Tests of Creative Thinking (TTCT). Creativity Research Journal, 18 (1), 314.Google Scholar
Koestler, A. (1964). The Act of Creation. New York, NY: Macmillan.Google Scholar
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43 (1), 5969.Google Scholar
Kosko, B. (1988). Bidirectional associative memories. IEEE Transactions on Systems, Man, and Cybernetics, 18 (1), 4960.Google Scholar
Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. Chicago, IL: University of Chicago Press.Google Scholar
Lakoff, G., & Johnson, M. (1999). Philosophy in the Flesh: The Embodied Mind and its Challenge to Western Thought. New York, NY: Basic Books.Google Scholar
Langley, P., & Jones, R. (1988). A computational model of scientific insight. In Sternberg, R. J. (Ed.), The Nature of Creativity (pp. 177201). New York, NY: Cambridge University Press.Google Scholar
Lubart, T. I. (2001). Models of the creative process: past, present and future. Creativity Research Journal, 13, 295308.Google Scholar
MacGregor, J. N., & Cunningham, J. B. (2009). The effects of number and level of restructuring in insight problem solving. Journal of Problem Solving, 2 (2), 130141.Google Scholar
Maier, N. R. (1931). Reasoning in humans. ii. The solution of a problem and its appearance in consciousness. Journal of Comparative Psychology, 12 (2), 181194.Google Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: Freeman.Google Scholar
Martindale, C. (1995). Creativity and connectionism. In Smith, S. M., Ward, T. B., & Finke, R. A. (Eds.), The Creative Cognition Approach (pp. 249268). Cambridge, MA: MIT Press.Google Scholar
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, 147158.Google Scholar
Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69, 220232.Google Scholar
Mednick, S. A., & Mednick, M. (1971). Remote Associates Test: Examiner’s Manual. Boston, MA: Houghton Mifflin.Google Scholar
Minsky, M. (1975). A framework for representing knowledge. In Winston, P. (Ed.), The Psychology of Computer Vision (pp. 211277). New York, NY: McGraw-Hill.Google Scholar
Miron-Spektor, E., Gino, F., & Argote, L. (2011). Paradoxical frames and creative sparks: enhancing individual creativity through conflict and integration. Organizational Behavior and Human Decision Processes, 116, 229240.Google Scholar
Nersessian, N. (2008). Creating Scientific Concepts. Cambridge, MA: MIT Press.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1962). The processes of creative thinking. In Gruber, H. E., Terrell, G., & Wertheimer, M. (Eds.), Contemporary Approaches to Creative Thinking (pp. 63119). New York, NY: Atherton Press.Google Scholar
Ohlsson, S. (1984). Restructuring revisited: I. Summary and critique of the Gestalt theory of problem solving. Scandinavian Journal of Psychology, 25, 6578.Google Scholar
Oltețeanu, A. M. (2014). Two general classes in creative problem-solving? An account based on the cognitive processes involved in the problem structure – representation structure relationship. In Proceedings of the Workshop “Computational Creativity, Concept Invention, and General Intelligence”, Osnabrück, Germany.Google Scholar
Oltețeanu, A. M. (2016a). From simple machines to eureka in four not-so-easy steps. Towards creative visuospatial intelligence. In Müller, V. C. (Ed.), Fundamental Issues of Artificial Intelligence (vol. 376, pp. 159180). London: Synthese Library.Google Scholar
Oltețeanu, A. M. (2016b). Towards an approach for the computationally assisted creation of insight problems in the practical object domain. In Besold, T., Kutz, O., & Leon, C. (Eds.), Proceedings of the 5th International Workshop on “Computational Creativity, Concept Invention, and General Intelligence,” Osnabruck, Germany.Google Scholar
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, 8190.Google Scholar
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, 1532.Google Scholar
Oltețeanu, A. M., Falomir, Z., & Freksa, C. (2018). Artificial cognitive systems that can answer human creativity tests: an approach and two case studies. IEEE Transactions on Cognitive and Developmental Systems, 10, 469475.Google Scholar
Oltețeanu, A. M., Gautam, B., & Falomir, Z. (2015). Towards a Visual Remote Associates Test and its computational solver. In Proceedings of the International Workshop on Artificial Intelligence and Cognition – AIC 2015 (CEUR-Ws Vol. 1510).Google Scholar
Olteţeanu, A. M., & Indurkhya, B. (Eds.) (2019). Re-representation in cognitive systems. A special issue. Frontiers in Cognitive Science. Special issue.Google Scholar
Olteţeanu, A. M., Schöttner, M., & Schuberth, S. (2019). Computationally resurrecting the functional remote associates test using cognitive word associates and principles from a computational solver. Knowledge-Based Systems, 168, 19.Google Scholar
Oltețeanu, A. M., & Schultheis, H. (2019). What determines creative association? Revealing two factors which separately influence the creative process when solving the Remote Associates Test. Journal of Creative Behavior, 53, 389395.Google Scholar
Olteţeanu, A. M., Schultheis, H., & Dyer, J. B. (2018). Computationally constructing a repository of compound remote associates test items in American English with comRAT-G. Behavior Research Methods, 50 (5), 19711980.Google Scholar
Perlovsky, L., & Levine, D. (2012). The drive for creativity and the escape from creativity: neurocognitive mechanisms. Cognitive Computation, 4, 292305.Google Scholar
Qiu, J., Li, H., Yang, D., et al. (2008). The neural basis of insight problem solving: an event-related potential study. Brain and Cognition, 68 (1), 100106.Google Scholar
Rumelhart, D. E. (1984). Schemata and the cognitive system. Handbook of Social Cognition, 1, 161188.Google Scholar
Saugstad, P., & Raaheim, K. (1957). Problem-solving and availability of functions. Acta Psychologica, 13, 263278.Google Scholar
Saunders, R. (2012). Towards autonomous creative systems: a computational approach. Cognitive Computation, 4, 216225.Google Scholar
Schank, R. C., & Abelson, R. P. (1977). Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Erlbaum.Google Scholar
Schooler, J. W., & Melcher, J. (1995). The ineffability of insight. In Ward, T. & Finke, R. (Eds.), The Creative Cognition Approach (pp. 249268). Cambridge, MA: MIT Press.Google Scholar
Schooler, J. W., Ohlsson, S., & Brooks, K. (1993). Thoughts beyond words: when language overshadows insight. Journal of Experimental Psychology: General, 122, 166183.Google Scholar
Searle, J. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417457.Google Scholar
Siew, C., Wulff, D., Beckage, N., & Kenett, Y. (2019). Cognitive Network Science: a review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 2108423.Google Scholar
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), 253266.Google Scholar
Smith, S. M., & Vela, E. (1991). Incubated reminiscence effects. Memory & Cognition, 19, 168176.Google Scholar
Sowa, J. (1992). Semantic networks. In Shapiro, S. (Ed.), Encyclopedia of Artificial Intelligence (2nd ed., pp. 14931511). New York, NY: Wiley.Google Scholar
Sun, R. (1994). Integrating Rules and Connectionism for Robust Commonsense Reasoning. New York, NY: John Wiley & Sons.Google Scholar
Sun, R. (2002). Duality of the Mind: A Bottom-up Approach Toward Cognition. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Sun, R., Merrill, E., & Peterson, T. (2001). From implicit skills to explicit knowledge: a bottom-up model of skill learning. Cognitive Science, 25, 203244.Google Scholar
Sun, R., Slusarz, P., & Terry, C. (2005). The interaction of the explicit and the implicit in skill learning: a dual-process approach. Psychological Review, 112, 159192.Google Scholar
Threadgold, E., Marsh, J. E., & Ball, L. J. (2018). Normative data for 84 english rebus puzzles. Frontiers in Psychology, 9, 2513.Google Scholar
Toivonen, H., & Gross, O. (2015). Data mining and machine learning in computational creativity. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5, 265275.Google Scholar
Wallach, M. A., & Kogan, N. (1965). Modes of Thinking in Young Children: A Study of the Creativity-Intelligence Distinction. Saint Louis, MO: Holt, Rinehart & Winston.Google Scholar
Wallas, G. (1926). The Art of Thought. New York, NY: Franklin Watts.Google Scholar
Whitt, J. K., & Prentice, N. M. (1977). Cognitive processes in the development of children’s enjoyment and comprehension of joking riddles. Developmental Psychology, 13 (2), 129136.Google Scholar
Worthen, B. R., & Clark, P. M. (1971). Toward an improved measure of remote associational ability. Journal of Educational Measurement, 8 (2), 113123.Google Scholar
Yaniv, I., & Meyer, D. E. (1987). Activation and metacognition of inaccessible stored information: potential bases for incubation effects in problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 187205.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×