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.

中文翻译：

---

对超越人类语言的好奇心的跨学科观点：动物、婴儿和人工智能

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，好奇心被定义为获取新奇信息的内在动机。尽管好奇心研究已有数十年的历史，最早的人类理论也源于对实验室啮齿动物的研究，但好奇心主要被分为两个阵营：“语言人类”和“其他”。尽管心理学是可遗传的，而且整个动物界的认知能力有许多连续性。关于好奇心缺乏突破界限的跨学科辩论，对前语言婴儿和非人类动物的相对排斥导致了科学僵局，这更广泛地阻碍了以自然主体好奇心为模型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。通过强调动物行为、发展心理学、神经科学和计算认知等不同学科如何得出类似的发现，我们讨论了如何利用它来增进我们对好奇心的理解。我们首次提出如何量化好奇心的特征，从而跨系统进行更可操作的研究：跨不同物种、发育阶段以及自然或人工媒介。