Collegium Helveticum, Institute for Advanced Studies, University of Zurich, ETH Zurich and Zurich University of the Arts, Zurich, Switzerland.
Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
Biol Rev Camb Philos Soc. 2024 Jun;99(3):979-998. doi: 10.1111/brv.13054. Epub 2024 Jan 29.
Curiosity is a core driver for life-long learning, problem-solving and decision-making. In a broad sense, curiosity is defined as the intrinsically motivated acquisition of novel information. Despite a decades-long history of curiosity research and the earliest human theories arising from studies of laboratory rodents, curiosity has mainly been considered in two camps: 'linguistic human' and 'other'. This is despite psychology being heritable, and there are many continuities in cognitive capacities across the animal kingdom. Boundary-pushing cross-disciplinary debates on curiosity are lacking, and the relative exclusion of pre-linguistic infants and non-human animals has led to a scientific impasse which more broadly impedes the development of artificially intelligent systems modelled on curiosity in natural agents. In this review, we synthesize literature across multiple disciplines that have studied curiosity in non-verbal systems. By highlighting how similar findings have been produced across the separate disciplines of animal behaviour, developmental psychology, neuroscience, and computational cognition, we discuss how this can be used to advance our understanding of curiosity. We propose, for the first time, how features of curiosity could be quantified and therefore studied more operationally across systems: across different species, developmental stages, and natural or artificial agents.
好奇心是终身学习、解决问题和决策的核心驱动力。从广义上讲，好奇心被定义为内在动机驱动下获取新信息的过程。尽管好奇心的研究已经有几十年的历史，而且最早的人类理论也是基于实验室啮齿动物的研究提出的，但好奇心主要被分为两个阵营：“语言人类”和“其他”。尽管心理学具有遗传性，而且在动物王国中认知能力有许多连续性，但好奇心的跨学科边界推动的辩论却很少，对非语言婴儿和非人类动物的相对排斥导致了科学僵局，更广泛地阻碍了基于自然主体好奇心的人工智能系统的发展。在这篇综述中，我们综合了多个学科的文献，这些文献研究了非语言系统中的好奇心。通过强调在动物行为学、发展心理学、神经科学和计算认知学等不同学科中如何产生相似的发现，我们讨论了如何利用这些发现来增进我们对好奇心的理解。我们首次提出如何在系统之间（不同物种、不同发育阶段以及自然或人工主体之间）量化好奇心的特征，从而更有效地进行研究。
