A transdisciplinary view on curiosity beyond linguistic humans: animals, infants, and artificial intelligence

IF 11 1区 生物学 Q1 BIOLOGY
Sofia Forss, Alejandra Ciria, Fay Clark, Cristina-loana Galusca, David Harrison, Saein Lee
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引用次数: 0

Abstract

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.

从跨学科角度看语言人类之外的好奇心: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信息的内在动机。尽管好奇心研究已有几十年的历史,人类最早的理论也产生于对实验室啮齿动物的研究,但好奇心主要被视为两大阵营:"语言人类 "和 "其他"。尽管心理学具有遗传性,而且动物界的认知能力也有很多连续性。关于好奇心的跨学科辩论缺乏边界推动力,前语言婴儿和非人类动物相对被排除在外,导致了科学上的僵局,更广泛地说,这阻碍了以自然人的好奇心为模型的人工智能系统的发展。在这篇综述中,我们综合了研究非语言系统好奇心的多个学科的文献。通过强调动物行为学、发展心理学、神经科学和计算认知等不同学科如何产生类似的发现,我们讨论了如何利用这些发现来促进我们对好奇心的理解。我们首次提出了如何量化好奇心的特征,从而在不同物种、发育阶段、自然或人工媒介等系统中对其进行更具操作性的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biological Reviews
Biological Reviews 生物-生物学
CiteScore
21.30
自引率
2.00%
发文量
99
审稿时长
6-12 weeks
期刊介绍: Biological Reviews is a scientific journal that covers a wide range of topics in the biological sciences. It publishes several review articles per issue, which are aimed at both non-specialist biologists and researchers in the field. The articles are scholarly and include extensive bibliographies. Authors are instructed to be aware of the diverse readership and write their articles accordingly. The reviews in Biological Reviews serve as comprehensive introductions to specific fields, presenting the current state of the art and highlighting gaps in knowledge. Each article can be up to 20,000 words long and includes an abstract, a thorough introduction, and a statement of conclusions. The journal focuses on publishing synthetic reviews, which are based on existing literature and address important biological questions. These reviews are interesting to a broad readership and are timely, often related to fast-moving fields or new discoveries. A key aspect of a synthetic review is that it goes beyond simply compiling information and instead analyzes the collected data to create a new theoretical or conceptual framework that can significantly impact the field. Biological Reviews is abstracted and indexed in various databases, including Abstracts on Hygiene & Communicable Diseases, Academic Search, AgBiotech News & Information, AgBiotechNet, AGRICOLA Database, GeoRef, Global Health, SCOPUS, Weed Abstracts, and Reaction Citation Index, among others.
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