G. Dumas, “Cognitive Science and Creativity: from natural brains in contingent social interactions to artificial intelligences between determinism and randomness”
|Guillaume Dumas, “Cognitive Science and Creativity: from natural brains in contingent social interactions to artificial intelligences between determinism and randomness” in Réseau ALEA / ALEA Network [online], 2020 <https://hasard.hypotheses.org/3152>|
While “Creativity” may appear out of reach from the determinism of scientific explanations, recent progresses in psychology and neuroscience have improved our understanding of this unique cognitive ability. The scientific study of creativity has over the last decades uncovered biological mechanisms, demonstrated its social dimensions, and even recently developed mathematical models displaying a form of artificial creativity.
Such scientific reduction of creativity to simple deterministic mechanisms may appear questionable. However, even without a complete explanatory model, applying the scientific method to the investigation of creativity can already bring a major theoretical tool: falsification. If the scientific study of creativity at the psychological level already brought multiple hypotheses about how the creative process unfold, the related theories have been debated over the last century by philosophers. For instance, the root of the controversy between nature and nurture was initially centred on creativity itself (Galton, 1874). Recent developments in both cognitive psychology and neuroscience have thus brought new data to support or disprove previously proposed theories. Experiments have for instance emphasised the importance of the multicultural exposure during development (Leung, Maddux, Galinsky, & Chiu, 2008), or supported the counter-intuitive association of creativity with “defocused attention” or “mind wandering” (Kéri, 2011).
Beyond the pure epistemological perspective, the understanding of underlying mechanisms can also be a pragmatic key for measuring creativity and ultimately for acting upon it during development and ageing (Simonton, 2012; Sawyer et al., 2003). In neuroscience, the understanding of the neural basis of creativity have for instance shown how creative insight is connected to remote association and restructuration, but also hypothesis and story generation (Sawyer, 2011). Interestingly, those different aspects appear mode deeply connected at the neural level. Indeed, the Default Mode Network (DMN) is a brain network associated with resting states and mind wandering but it appears also of key importance for semantic association and future scenario generation based on past experiences remix (Dohmatob, Dumas, & Bzdok, 2017).
Recently, the traditional focus of creativity as an individual skill also shift as a subject in social cognition. This move has started with social psychology trying to better understand collective problem solving and brainstorming (Agogué et al., 2015). In developmental psychology, this was already known that social interaction is the best way to expand children behavioural repertoire, even beyond the behaviours they were exposed to (Smolucha, 1992). Babies indeed learn a lot through interaction with their parents, especially through imitation. However, perfect imitation never occurs, and errors lead inevitably to exploration of unexpected patterns. Not only this process allows a greater adaptability of behaviour to similar but not identical context, it also constitutes an integral way for the exploration of possible behaviour, and even for the metacognitive understanding of creativity (Feldhusen, 1995).
This early exploration of potentialities through social interaction with parents can develop far beyond childhood. Ultimately, humans in dyads or bigger group face problems requiring collective effort. This demands to expand creativity beyond the individual and thus decipher how collective creativity is not merely the sum of the group members independent creative contribution. Several studies have shown how this “social gestalt” occurs and even highlighted critical factors for its emergence and efficiency. For instance, diversity of group members, especially in genders, has been associated with better performances (Egan, 2005). Paradoxically, too much diversity in expertise and skills seems not optimal, especially if members of the group are bad at communicating (Fusaroli et al., 2012). Recent developments in neuroscience of social interaction have shown how neural dynamics get synchronised between brains during exchanges (Dumas et al., 2010). Those results question the traditional isolated approach of human cognition and support a rigorous account of interpersonal dynamics at the neurobiological level (Dumas, 2011). Interestingly, those studies have clearly demonstrated how the brain was operating differently during online spontaneous interaction compared to merely offline social perception (Dumas et al., 2012; Nadel & Dumas, 2015). This place joint improvisation as a specific mode of creation, beyond individual creative modes. The question is how such inter-individual modes of creativity are related to intra-individual ones, from a developmental and evolutionary perspectives.
More recently, the field of Artificial Intelligence has become simultaneously a sandbox and a crash-test for our models of human cognition, including creativity. Artificial neural networks, especially with the blooming of deep learning, have been proven to autonomously generate text, music, and pictures. Previous attempts were merely qualifying as gimmicks of creativity since they were combining hard-wired rules with random generators. Now, those deep neural networks are literally “learning” patterns by themselves, and then reinterpret those patterns to generate new ones. So far, three factors are affecting those “artificial creators”: the innate, how the system has been designed, the acquired, what the system has been exposed to for learning patterns, and the situation, how the system is when it is creating. This actually question the role of randomness in the overall process since it can act at those three different levels simultaneously. Nevertheless, those mathematical models give a totally new window on creativity, but also new tools for meta-creating.
This later aspect, question how humans and machines will cooperate in the future and co-create reality (Battaglia, Mei, & Dumas, 2018). If the priority is the design of ethical rules (Birhane & van Dijk, 2020), the field of human-machine interaction have also shown the importance of spontaneity. Humans not only tend to prefer robots that looks cute and sympathetic, they also value behaviours and robots which paradoxically act smoothly and not like a robot (Hoffman & Breazeal, 2007). A symbiotic relationship will thus require development of artificial form of behaviour and movements that are spontaneous and creative. Another good motivation for the scientific study of creativity.
Agogué, M., Le Masson, P., Dalmasso, C., Houdé, O., & Cassotti, M. (2015). Resisting classical solutions: The creative mind of industrial designers and engineers. Psychology of Aesthetics, Creativity, and the Arts, 9(3), 313.
Battaglia, E. M., Mei, J., & Dumas, G. (2018). Systems of Global Governance in the Era of Human-Machine Convergence. arXiv preprint arXiv:1802.04255.
Birhane, A., & van Dijk, J. (2020). Robot Rights? Let’s Talk about Human Welfare Instead. arXiv preprint arXiv:2001.05046.
Dohmatob, E., Dumas, G. et Bzdok, D. (2017). Dark Control: A Unified Account of Default Mode Function by Control Theory and Reinforcement Learning. BioRxiv, 148890. doi:10.1101/148890
Dumas, G., Nadel, J., Soussignan, R., Martinerie, J. et Garnero, L. (2010). Inter-brain synchronization during social interaction. PLoS ONE, 5(8), e12166. doi:10.1371/journal.pone.0012166
Dumas, G. (2011). Towards a two-body neuroscience. Communicative & Integrative Biology, 4(3), 349‑352. doi:10.4161/cib.4.3.15110
Dumas, G., Martinerie, J., Soussignan, R. et Nadel, J. (2012). Does the brain know who is at the origin of what in an imitative interaction? Frontiers in Human Neuroscience, 6. doi:10.3389/fnhum.2012.00128
Egan, T. M. (2005). Creativity in the context of team diversity: Team leader perspectives. Advances in developing human resources, 7(2), 207-225.
Feldhusen, J. F. (1995). Creativity: A knowledge base, metacognitive skills, and personality factors. The Journal of creative behavior, 29(4), 255-268.
Fusaroli, R., Bahrami, B., Olsen, K., Roepstorff, A., Rees, G., Frith, C., & Tylén, K. (2012). Coming to terms: quantifying the benefits of linguistic coordination. Psychological science, 23(8), 931-939.
Galton, F. (1874). English men of science: Their nature and nurture. London: Macmillan.
Hoffman, G., & Breazeal, C. (2007, March). Effects of anticipatory action on human-robot teamwork efficiency, fluency, and perception of team. In Proceedings of the ACM/IEEE international conference on Human-robot interaction (pp. 1-8).
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, 215-221. doi:10.1037/a0022000
Leung, A. K., Maddux, W. W., Galinsky, A. D., & Chiu, C. (2008). Multicultural experience enhances creativity: The when and how. American Psychologist, 63, 169-181.
Nadel, J. et Dumas, G. (2014). The Interacting Body: Intra- and Interindividual Processes During Imitation. Journal of Cognitive Education and Psychology, 13(2), 163‑175. doi:10.1891/1945-89188.8.131.52
Sawyer, R. K., Csikszentmihalyi, M., John-Steiner, V., Moran, S., Feldman, D. H., Gardner, H., … & Nakamura, J. (2003). Creativity and development. Oxford University Press, USA.
Sawyer, K. (2011). The cognitive neuroscience of creativity: A critical review. Creativity Research Journal, 23(2), 137-154.
Smolucha, F. (1992). The relevance of Vygotsky’s theory of creative imagination for contemporary research on play. Creativity Research Journal, 5(1), 69-76.
Simonton, D. K. (2012). Teaching creativity: Current findings, trends, and controversies in the psychology of creativity. Teaching of Psychology, 39(3), 217-222.