Rastelli Clara, Greco Antonino, De Pisapia Nicola, Finocchiaro Chiara
Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy.
MEG Center, University of Tübingen, 72076 Tübingen, Germany.
PNAS Nexus. 2022 Dec 2;1(5):pgac273. doi: 10.1093/pnasnexus/pgac273. eCollection 2022 Nov.
Creative problem solving is a fundamental skill of human cognition and is conceived as a search process whereby a novel and appropriate solution is generated. However, it is unclear whether children are able to balance novelty and appropriateness to generate creative solutions and what are the underlying computational mechanisms. Here, we asked children, ranging from 10 to 11 years old, to perform a word association task according to three instructions, which triggered a more appropriate (ordinary), novel (random), or balanced (creative) response. Results revealed that children exhibited greater cognitive flexibility in the creative condition compared to the control conditions, as revealed by the structure and resiliency of the semantic networks. Moreover, responses' word embeddings extracted from pretrained deep neural networks showed that semantic distance and category switching index increased in the creative condition with respect to the ordinary condition and decreased compared to the random condition. Critically, we showed how children efficiently solved the exploration/exploitation trade-off to generate creative associations by fitting a computational reinforcement learning (RL) model that simulates semantic search strategies. Our findings provide compelling evidence that children balance novelty and appropriateness to generate creative associations by optimally regulating the level of exploration in the semantic search. This corroborates previous findings on the adult population and highlights the crucial contribution of both components to the overall creative process. In conclusion, these results shed light on the connections between theoretical concepts such as bottom-up/top-down modes of thinking in creativity research and the exploration/exploitation trade-off in human RL research.
创造性地解决问题是人类认知的一项基本技能，被视为一个搜索过程，通过这个过程产生新颖且合适的解决方案。然而，目前尚不清楚儿童是否能够平衡新颖性和合适性以产生创造性的解决方案，以及潜在的计算机制是什么。在这里，我们让10至11岁的儿童根据三条指令执行一个单词联想任务，这三条指令分别引发更合适（普通）、新颖（随机）或平衡（创造性）的反应。结果表明，与控制条件相比，儿童在创造性条件下表现出更大的认知灵活性，这一点通过语义网络的结构和弹性得以体现。此外，从预训练的深度神经网络中提取的反应词嵌入表明，与普通条件相比，创造性条件下的语义距离和类别转换指数增加，与随机条件相比则降低。至关重要的是，我们展示了儿童如何通过拟合一个模拟语义搜索策略的计算强化学习（RL）模型，有效地解决探索/利用权衡问题以生成创造性联想。我们的研究结果提供了令人信服的证据，表明儿童通过在语义搜索中最优地调节探索水平，平衡新颖性和合适性以生成创造性联想。这证实了之前关于成年人群体的研究结果，并突出了这两个组成部分对整体创造性过程的关键贡献。总之，这些结果揭示了创造力研究中自下而上/自上而下思维模式等理论概念与人类强化学习研究中的探索/利用权衡之间的联系。
