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创造性过程的神经计算模型。

A neurocomputational model of creative processes.

机构信息

Department of Psychology and Methods, Jacobs University Bremen, Bremen, Germany.

Department of Human Anatomy and Physiology, the Faculty of Health Sciences, University of Johannesburg, South Africa; School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, Queensland, Australia.

出版信息

Neurosci Biobehav Rev. 2022 Jun;137:104656. doi: 10.1016/j.neubiorev.2022.104656. Epub 2022 Apr 14.

Abstract

Creativity is associated with finding novel, surprising, and useful solutions. We argue that creative cognitive processes, divergent thinking, abstraction, and improvisation are constructed on different novelty-based processes. The prefrontal cortex plays a role in creative ideation by providing a control mechanism. Moreover, thinking about novel solutions activates the distant or loosely connected neurons of a semantic network that involves the hippocampus. Novelty can also be interpreted as different combinations of earlier learned processes, such as the motor sequencing mechanism of the basal ganglia. In addition, the cerebellum is responsible for the precise control of movements, which is particularly important in improvisation. Our neurocomputational perspective is based on three creative processes centered on novelty seeking, subserved by the prefrontal cortex, hippocampus, cerebellum, basal ganglia, and dopamine. The algorithmic implementation of our model would enable us to describe commonalities and differences between these creative processes based on the proposed neural circuitry. Given that most previous studies have mainly provided theoretical and conceptual models of creativity, this article presents the first brain-inspired neural network model of creative cognition.

摘要

创造力与发现新颖、出人意料和有用的解决方案有关。我们认为,创造性认知过程、发散思维、抽象和即兴创作是基于不同的新颖性过程构建的。前额叶皮层通过提供控制机制在创造性思维中发挥作用。此外,思考新颖的解决方案会激活语义网络中涉及海马体的远距离或松散连接的神经元。新颖性也可以解释为早期学习过程的不同组合,例如基底神经节的运动序列机制。此外,小脑负责运动的精确控制,这在即兴创作中尤为重要。我们的神经计算观点基于以新颖性为中心的三个创造性过程,由前额叶皮层、海马体、小脑、基底神经节和多巴胺提供支持。我们模型的算法实现将使我们能够根据提出的神经回路描述这些创造性过程之间的共同和差异。鉴于大多数先前的研究主要提供了创造力的理论和概念模型,本文提出了第一个受大脑启发的创造性认知神经网络模型。

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